diff --git a/ai/RAPIDS/English/Python/jupyter_notebook/Introduction_To_Rapids.ipynb b/ai/RAPIDS/English/Python/jupyter_notebook/Introduction_To_Rapids.ipynb index 420fd5dd8..c451af4be 100644 --- a/ai/RAPIDS/English/Python/jupyter_notebook/Introduction_To_Rapids.ipynb +++ b/ai/RAPIDS/English/Python/jupyter_notebook/Introduction_To_Rapids.ipynb @@ -122,7 +122,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/ai/RAPIDS/English/Python/jupyter_notebook/START_HERE.ipynb b/ai/RAPIDS/English/Python/jupyter_notebook/START_HERE.ipynb index 21fa47cf3..89bcbd7e6 100644 --- a/ai/RAPIDS/English/Python/jupyter_notebook/START_HERE.ipynb +++ b/ai/RAPIDS/English/Python/jupyter_notebook/START_HERE.ipynb @@ -103,7 +103,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/ai/RIVA/English/Python/Readme.md b/ai/RIVA/English/Python/Readme.md new file mode 100644 index 000000000..c47cc9ed2 --- /dev/null +++ b/ai/RIVA/English/Python/Readme.md @@ -0,0 +1,54 @@ +# RIVA_Bootcamp + +## GPU Bootcamp for RIVA + +This repository consists of gpu bootcamp material for RIVA. The RIVA frameworks of libraries, APIs and models allows you to build, optimize and deploy voice and speech centric applications and models based entirely on GPUs. In this series you can access RIVA learning resources in the form of labs. The modules covered in this Bootcamp are SpeechToText, Intent Slot Classification, Named Entity Recognition, Question-Answering and Challenge. + +## Prerequisites +To run this tutorial you will need a machine with NVIDIA GPU. + +- Install the latest [Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker) . + +- The base containers required for the lab requires users to create a NGC account and generate an API key (https://docs.nvidia.com/ngc/ngc-catalog-user-guide/index.html#registering-activating-ngc-account) + +## Pulling containers +To start with, you will have to pull the RIVA Docker container +``` ngc registry resource download-version "nvidia/riva/riva_quickstart:1.4.0-beta" ``` + +### Copy Docker Initialization Scripts +```cp *.sh riva_quickstart_v1.4.0-beta ``` +```cd riva_quickstart_v1.4.0-beta ``` + +### If running with multiple users/clients, please choose a UNIQUE Riva server port number for each user (default is 50051) + +### Initialize the RIVA platform +``` bash riva_init_new.sh ``` +``` bash riva_start.sh ``` + +### start client + +``` bash riva_start_bootcamp.sh``` + +### run the Jupyter notebook + +``` jupyter notebook --ip=0.0.0.0 --allow-root --notebook-dir=/bootcamp --no-browser --port=8888 --NotebookApp.token='' ``` + +Then, open the jupyter notebook in browser: http://localhost:8888 +Start working on the lab by clicking on the `Start_Here_RIVA.ipynb` notebook. + + +## Troubleshooting + +Q. Cannot run RIVA scripts + +A. The Riva platform requires Docker to be actively running. In case you are running rootless-docker you should ensure that the Docker daemon is active and has sufficient permissions + +Q. Invalid API key + +A. Pulling pretrained model containers from NGC requires a valid API key. To get this key, please login to you NGC account and generate a new valid API key and re initialize the Riva platform + + +## For more information about the Riva platform, models and services, please refer here + +## For more information about the Riva quickstart container, please refer here + diff --git a/ai/RIVA/English/Python/jupyter_notebook/Challenge/Challenge.ipynb b/ai/RIVA/English/Python/jupyter_notebook/Challenge/Challenge.ipynb new file mode 100644 index 000000000..2e974029a --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/Challenge/Challenge.ipynb @@ -0,0 +1,492 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fe17e068", + "metadata": {}, + "source": [ + "# Challenge - Building a Semantic Index and Knowledge Base from Video Data\n", + "\n", + "## 1. Introduction\n", + "\n", + "This notebook walks through an end-to-end GPU enabled workflow where a semantic index and knowledge base is built from data available in video format; using tools found in the Riva Framework.\n", + "\n", + "After completing this excercise, you will be able to use Riva to transcribe video data, build an index for keywords and entities found in the videos, and construct a basic knowledge base from the provided data. \n", + "\n", + "\n", + "It is not required that the user is familiar with Riva beforehand. Since our aim is to go from raw videos to a knowledge base, a detailed introduction is out of scope for this notebook. We recommend [Introduction to Riva](../Introduction_to_Riva.ipynb) for additional information.\n", + "\n", + "### 1.2. Problem statement\n", + "\n", + "We are trying to answer the following questions, using videos from the GTC 2021 special address on healthcare as a source of data.\n", + "\n", + "0: how is drug discovery traditionally done?\n", + "\n", + "1: how many compounds are inferred in the molecular latent space learned?\n", + "\n", + "2: what type of model was used to generate chemistry?\n", + "\n", + "3: How many parameters does the world's largest clinical model have?\n", + "\n", + "4: how many drug candidates can be simulated per year using the a100 MIG architecture?\n", + "\n", + "5: how many new models are part of Clara discovery?\n", + "\n", + "6: where does medical data flow in from?\n", + "\n", + "7: how much data do hospitals generate each year?\n", + "\n", + "8: what is Nvidia Clara?\n", + "\n", + "9: how many models does Clara have?\n", + "\n", + "10: how much of the population can AI driven diagnostic devices reach?\n", + "\n", + "We will first use a small example video to answer a simple sample question, and use this as a template to answer the questions posed in the problem statement.\n", + "\n", + "\n", + "\n", + "### 1.3 Why RIVA?\n", + "\n", + "Riva allows us to transcribe audio data into text using ASR, and we can gain insights from these transcripts using NLP methods provided by Jarvis such as Named Entity Recognition and Question Answering\n", + "\n", + "\n", + "### 1.2.1 References\n", + "\n", + "\n", + "Dataset sources:\n", + "- NVIDIA Youtube Channel\n", + "- \n" + ] + }, + { + "cell_type": "markdown", + "id": "92e2f497", + "metadata": {}, + "source": [ + "## 1. Sample Exercise\n", + "Let us use the following file as a sample input:\n", + "Sample Video (The Amazon Rainforest)\n", + "\n", + "For convenience, we have converted the youtube video into a WAV file which can be used in our pipeline\n", + "The code for this process is found in section 2 \n", + "\n", + "Sample Audio(../data/amazon-rainforest.wav)\n", + "\n", + "For this sample, we will build a list of the Entities found in the transcript, and answer the following 2 questions:\n", + "\n", + "1: What is the Amazon Rainforest?\n", + "\n", + "2: How many reptile species are found in the Amazon Rainforest?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf720971", + "metadata": {}, + "outputs": [], + "source": [ + "## first let us preview the video \n", + "## use builtin IPython to playback youtube\n", + "from IPython.display import IFrame\n", + "\n", + "# Sample Video\n", + "IFrame(\"https://www.youtube.com/embed/M_9xIVfXA1w?rel=0&controls=0&showinfo=0\", width=\"560\", height=\"315\", frameborder=\"0\", allowfullscreen=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2efc190", + "metadata": {}, + "outputs": [], + "source": [ + "## code to convert from Youtube URL to audio file\n", + "## Warning! do not use this for copyright violations\n", + "## this code for reference, the audio file is already provided in the next cell\n", + "\n", + "##first, we will need to have two required libraries\n", + "## ffmpeg and youtube-dl\n", + "\n", + "\n", + "# !pip install youtube_dl\n", + "# !pip install ffmpeg\n", + "\n", + "##lets make this available as a function, takes in a URL and generates a WAV file\n", + "\n", + "# from youtube_dl import YoutubeDL\n", + "\n", + "# def getAudio(url):\n", + " \n", + "# audio_downloader = YoutubeDL({'format':'bestaudio'})\n", + "\n", + "# try:\n", + "\n", + "# print('Dowloading Audio')\n", + "\n", + "# URL = url\n", + "\n", + "# audio_downloader.extract_info(URL)\n", + "\n", + "# except Exception:\n", + "\n", + "# print(\"Couldn\\'t download the audio\")\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fcadfea9", + "metadata": {}, + "outputs": [], + "source": [ + "## Let's import required basic libraries\n", + "import io\n", + "import librosa\n", + "from time import time\n", + "import numpy as np\n", + "import IPython.display as ipd\n", + "import grpc\n", + "import requests\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d63e0050", + "metadata": {}, + "outputs": [], + "source": [ + "## let's import required RIVA libraries\n", + "# ASR \n", + "import riva_api.riva_asr_pb2 as rasr\n", + "import riva_api.riva_asr_pb2_grpc as rasr_srv\n", + "import riva_api.riva_audio_pb2 as ra\n", + "\n", + "# NLP \n", + "\n", + "import riva_api.riva_nlp_pb2 as rnlp\n", + "import riva_api.riva_nlp_pb2_grpc as rnlp_srv\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6abc7a08", + "metadata": {}, + "outputs": [], + "source": [ + "## Let's configure our services\n", + "## note: change the host and portnumber to your provider Riva SERVER PORT and DGX that you are connected to\n", + "\n", + "\n", + "channel = grpc.insecure_channel('YOUR DGX HOST HERE:RIVA PORT NUM')\n", + "\n", + "##setup service layer objects\n", + "\n", + "riva_asr = rasr_srv.RivaSpeechRecognitionStub(channel)\n", + "riva_nlp = rnlp_srv.RivaLanguageUnderstandingStub(channel)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52cded8d", + "metadata": {}, + "outputs": [], + "source": [ + "## lets preview the audio \n", + "## note, the audio is provided in the data folder\n", + "\n", + "# read in an audio file from local disk\n", + "path = \"../datasets/amazon-rainforest.wav\"\n", + "audio, sr = librosa.core.load(path, sr=None)\n", + "with io.open(path, 'rb') as fh:\n", + " content = fh.read()\n", + "ipd.Audio(path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69da2d3b", + "metadata": {}, + "outputs": [], + "source": [ + "##now lets use Riva ASR to transcribe the audio\n", + "# Set up an offline/batch recognition request\n", + "\n", + "req = rasr.RecognizeRequest()\n", + "req.audio = content # raw bytes from previous cell\n", + "req.config.encoding = ra.AudioEncoding.LINEAR_PCM # Supports LINEAR_PCM, FLAC, MULAW and ALAW audio encodings\n", + "req.config.sample_rate_hertz = sr # Audio will be resampled if necessary\n", + "req.config.language_code = \"en-US\" # Language model ode\n", + "req.config.max_alternatives = 1 # How many top-N hypotheses to return, 1 means return the best one\n", + "req.config.enable_automatic_punctuation = True # Add punctuation when end of VAD detected\n", + "req.config.audio_channel_count = 1 # Mono channel, change to 2 for stereo\n", + "\n", + "response = riva_asr.Recognize(req)\n", + "asr_best_transcript = response.results[0].alternatives[0].transcript\n", + "\n", + "print(\"ASR Transcript:\", asr_best_transcript) ## we will use this in the next step\n", + "\n", + "## lets inspect the response to ensure no anomalies\n", + "\n", + "print(\"\\n\\nFull Response Message:\")\n", + "print(response) \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91f22c00", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "## now, we have the raw transcript. Let us identify the named entities in this text\n", + "## first setup the nlp request object\n", + "req = rnlp.TokenClassRequest()\n", + "\n", + "## now define the model\n", + "req.model.model_name = \"riva_ner\" # If you have deployed a custom model with the domain_name \n", + " # parameter in ServiceMaker's `jarvis-build` command then you should use \n", + " # \"jarvis_ner_\" where \n", + " # is the name you provided to the domain_name parameter.\n", + "\n", + "## use the text from the previous part here \n", + "req.text.append(asr_best_transcript)\n", + "\n", + "resp = riva_nlp.ClassifyTokens(req)\n", + "\n", + "##show us what entities are present\n", + "print(\"Named Entities:\")\n", + "for result in resp.results[0].results:\n", + " print(f\" {result.token} ({result.label[0].class_name})\")" + ] + }, + { + "cell_type": "markdown", + "id": "a189caad", + "metadata": {}, + "source": [ + "Now that we have seen which entities are available, we can use the text to answer our questions. \n", + "for our sample we have a small file which contains all entities needed to answer our query, so we can go ahead and jump to the question answering phase straight away." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cece38a7", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "##now define the request object\n", + "req = rnlp.NaturalQueryRequest()\n", + "\n", + "## now lets make a list of our queries\n", + "\n", + "queries = ['What is the Amazon Rainforest?', 'How many reptile species are found in the Amazon Rainforest?']\n", + "\n", + "## setup answers/knowledge base object\n", + "answers = []\n", + "\n", + "##now lets iterate through the loop and provide answers\n", + "\n", + "for input_query in queries:\n", + " \n", + " req.query = input_query\n", + " ## we have a small transcript so we will only use this one context.\n", + " ## for larger amounts of data we need to split the text to narrow down the context\n", + " req.context = asr_best_transcript\n", + " resp = riva_nlp.NaturalQuery(req)\n", + "\n", + " print(f\"Query: {input_query}\")\n", + " print(f\"Answer: {resp.results[0].answer}\")\n", + " \n", + " ##add the answers to the knowledge base\n", + " answers.append(resp.results[0].answer)\n", + "\n", + "## show us the combined answer list\n", + "print(answers)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "65fff36b", + "metadata": {}, + "source": [ + "## 2. Challenge Exercise\n", + "Let us use the following files as a sample input:\n", + "Challenge Video (GTC-2021-healthcare)\n", + "Video URL: https://www.youtube.com/watch?v=AfC7-Iksl_M\n", + "\n", + "For convenience, we have converted the youtube video into WAV files which can be used in our pipeline\n", + "The code for this process is found above \n", + "\n", + "Sample Audio(../data/amazon-rainforest.wav)\n", + "\n", + "For the exercise, find a list of named entities in each video (or the entire talk) and answer the following questions:\n", + "\n", + "0: how is drug discovery traditionally done?\n", + "\n", + "1: how many compounds are inferred in the molecular latent space learned?\n", + "\n", + "2: what type of model was used to generate chemistry?\n", + "\n", + "3: How many parameters does the world's largest clinical model have?\n", + "\n", + "4: how many drug candidates can be simulated per year using the a100 MIG architecture?\n", + "\n", + "5: how many new models are part of Clara discovery?\n", + "\n", + "6: where does medical data flow in from?\n", + "\n", + "7: how much data do hospitals generate each year?\n", + "\n", + "8: what is Nvidia Clara?\n", + "\n", + "9: how many models does Clara have?\n", + "\n", + "10: how much of the population can AI driven diagnostic devices reach?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e0676049", + "metadata": {}, + "outputs": [], + "source": [ + "## first let us preview the video \n", + "## use builtin IPython to playback youtube\n", + "from IPython.display import IFrame\n", + "\n", + "# GTC Healthcare Special Talk\n", + "IFrame(\"https://www.youtube.com/embed/AfC7-Iksl_M?rel=0&controls=0&showinfo=0\", width=\"560\", height=\"315\", frameborder=\"0\", allowfullscreen=True)" + ] + }, + { + "cell_type": "markdown", + "id": "6e21ae11", + "metadata": {}, + "source": [ + "The talk has been split into sections and the following WAV files are available in the data folder \n", + "gtc1.wav \n", + "gtc2.wav \n", + "gtc3.wav \n", + "gtc4.wav \n", + "gtc5.wav \n", + "gtc6.wav \n", + "gtc7.wav \n", + "gtc8.wav \n", + "gtc9.wav \n", + "gtc10.wav \n", + "gtc11.wav \n", + "gtc12.wav \n", + "gtc13.wav \n", + "gtc14.wav \n", + "gtc15.wav \n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2336872f", + "metadata": {}, + "outputs": [], + "source": [ + "##first, transcribe the files \n", + "## using a function and a loop will be useful here\n", + "## TODO HERE" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c4174f0a", + "metadata": {}, + "outputs": [], + "source": [ + "## next, identify entities\n", + "## again, defining a function and a loop is useful\n", + "##TODO here" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "97017d94", + "metadata": {}, + "outputs": [], + "source": [ + "##next, match entities with questions\n", + "## some entities may not appear, may be misspelled and mispunctuated" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "8881ca7a", + "metadata": {}, + "outputs": [], + "source": [ + "## next, run the QA pipeline for matches found for each question\n", + "## a loop or a nested loop is useful here" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "519cc4fb", + "metadata": {}, + "outputs": [], + "source": [ + "##finally, compile answers into knowledge base\n", + "## a dictionary structure, combined with lists is ideal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86a1025f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/ISC/intent-slot-classification-Exercise.ipynb b/ai/RIVA/English/Python/jupyter_notebook/ISC/intent-slot-classification-Exercise.ipynb new file mode 100644 index 000000000..a9217c82f --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/ISC/intent-slot-classification-Exercise.ipynb @@ -0,0 +1,268 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Learning Objectives\n", + "In this notebook, you will learn how to: \n", + "- Use Riva ServiceMaker to take a TLT exported .riva and generate a model repository\n", + "- Deploy the model(s) locally on the Riva Server\n", + "- Exercise: Send inference requests from a demo client using Riva API bindings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Pre-requisites" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To follow along, please make sure:\n", + "- You have access to NVIDIA NGC, and are able to download the Riva Quickstart [resources](https://ngc.nvidia.com/resources/ea-riva-stage:riva_quickstart/)\n", + "- Have an .riva model file that you wish to deploy. You can obtain this from `tlt export` (with `export_format=RIVA`). Please refer the tutorial on *Joint Intent Detection and Slot Filling using Transfer Learning Toolkit* for more details on training and exporting an .riva model.\n", + "- You have followed the steps in the setup notebook to startup, initialize and deploy the Riva Service" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Riva-deploy\n", + "\n", + "The deployment tool takes as input one or more Riva Model Intermediate Representation (RMIR) files and a target model repository directory. It creates an ensemble configuration specifying the pipeline for the execution and finally writes all those assets to the output model repository directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Syntax: riva-deploy -f dir-for-rmir/model.rmir:key output-dir-for-repository\n", + "!docker run --rm --gpus 1 -v $MODEL_LOC:/data $RIVA_SM_CONTAINER -- \\\n", + " riva-deploy -f /data/intent-slot.rmir:$KEY /data/models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Start Riva Server\n", + "Once the model repository is generated, we are ready to start the Riva server. From this step onwards you should follow the Riva setup notebook and modify the config.sh file as required, and proceed to the Inference step after deploying the Riva service. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Run Inference\n", + "Once the Riva server is up and running with your models, you can send inference requests querying the server. \n", + "\n", + "To send GRPC requests, the Riva Python API bindings for client can be used. This is available as a pip .whl which is installed in the setup notebook.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following code sample shows how you can perform inference using Riva Python API gRPC bindings:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import grpc\n", + "import riva_api.riva_nlp_pb2 as rnlp\n", + "import riva_api.riva_nlp_pb2_grpc as rnlp_srv\n", + "\n", + "def run_intent_slot(grpc_server, query):\n", + " channel = grpc.insecure_channel(grpc_server)\n", + " riva_nlp = rnlp_srv.RivaLanguageUnderstandingStub(channel)\n", + "\n", + " for q in query:\n", + " req = rnlp.AnalyzeIntentRequest()\n", + " req.query = q\n", + " req.options.domain = \"default\" # The is appended to \"riva_intent_\" to look for a\n", + " # model \"riva_intent_\". So the model \"riva_intent_default\"\n", + " # needs to be preloaded in riva server. If you would like to deploy your\n", + " # custom Joint Intent and Slot model use the `--domain_name` parameter in\n", + " # ServiceMaker's `riva-build intent_slot` command.\n", + " resp = riva_nlp.AnalyzeIntent(req)\n", + " print(\"Query:\", q)\n", + " print(\"Intent:\", resp.intent)\n", + " print(\"Slots:\", resp.slots)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_intent_slot(grpc_server=\"localhost:50051\",\n", + " query=[\"Please set an alarm for 6 am\", \"Play some pop music\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`NOTE`: You could also run the above inference code from inside the Riva Client container. The QuickStart provides a script `riva_start_client.sh` to run the container. It has more examples for different services." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " You can stop all docker container before shutting down the jupyter kernel. **Caution: The following command will stop all running containers**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!docker stop $(docker ps -a -q)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The AnalyzeIntent API can be used to query a Intent Slot classifier. The API can leverage a\n", + "# text classification model to classify the domain of the input query and then route to the \n", + "# appropriate intent slot model.\n", + "\n", + "# Lets first see an example where the domain is known. This skips execution of the domain classifier\n", + "# and proceeds directly to the intent/slot model for the requested domain.\n", + "\n", + "req = rnlp.AnalyzeIntentRequest()\n", + "req.query = \"How is the humidity in San Francisco?\"\n", + "req.options.domain = \"weather\" # The is appended to \"riva_intent_\" to look for a \n", + " # model \"riva_intent_\". So in this e.g., the model \"riva_intent_weather\"\n", + " # needs to be preloaded in riva server. If you would like to deploy your \n", + " # custom Joint Intent and Slot model use the `--domain_name` parameter in \n", + " # ServiceMaker's `riva-build intent_slot` command.\n", + "\n", + "resp = riva_nlp.AnalyzeIntent(req)\n", + "print(resp)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Below is an example where the input domain is not provided.\n", + "\n", + "req = rnlp.AnalyzeIntentRequest()\n", + "req.query = \"Is it going to rain tomorrow?\"\n", + "\n", + " # The input query is first routed to the a text classification model called \"riva_text_classification_domain\"\n", + " # The output class label of \"riva_text_classification_domain\" is appended to \"riva_intent_\"\n", + " # to get the appropriate Intent Slot model to execute for the input query.\n", + " # Note: The model \"riva_text_classification_domain\" needs to be loaded into Riva server and have the appropriate\n", + " # class labels that would invoke the corresponding intent slot model.\n", + "\n", + "resp = riva_nlp.AnalyzeIntent(req)\n", + "print(resp)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Some weather Intent queries grouped together\n", + "queries = [\n", + " \"Is it currently cloudy in Tokyo?\",\n", + " \"What is the annual rainfall in Pune?\",\n", + " \"What is the humidity going to be tomorrow?\"\n", + "]\n", + "for q in queries:\n", + " req = rnlp.AnalyzeIntentRequest()\n", + " req.query = q\n", + " start = time()\n", + " resp = riva_nlp.AnalyzeIntent(req)\n", + "\n", + " print(f\"[{resp.intent.class_name}]\\t{req.query}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise\n", + "\n", + "Use the Intent Slot classification API to classify individual Intents and Slots for the following utterances:\n", + "\n", + "\n", + "- I need to catch the early train tomorrow evening.\n", + "- I am very tired after all the work done yesterday.\n", + "- Will it rain tomorrow in New York?\n", + "- Is it currently sunny in Chicago?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##first, import required libraries\n", + "\n", + "\n", + "\n", + "## next, setup service configuration\n", + "\n", + "\n", + "## next, setup request variables\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/ISC/intent-slot-classification-deployment.ipynb b/ai/RIVA/English/Python/jupyter_notebook/ISC/intent-slot-classification-deployment.ipynb new file mode 100644 index 000000000..83edf2c44 --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/ISC/intent-slot-classification-deployment.ipynb @@ -0,0 +1,276 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deploying a Joint Intent & Slot Classification Model in Riva" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Transfer Learning Toolkit](https://developer.nvidia.com/transfer-learning-toolkit) (TLT) provides the capability to export your model in a format that can deployed using [NVIDIA Riva](https://developer.nvidia.com/riva), a highly performant application framework for multi-modal conversational AI services using GPUs. \n", + "\n", + "This tutorial explores taking an .riva model, the result of `tlt intent_slot_classification export` command, and leveraging the Riva ServiceMaker framework to aggregate all the necessary artifacts for Riva deployment to a target environment. Once the model is deployed in Riva, you can issue inference requests to the server. We will demonstrate how quick and straightforward this whole process is. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Learning Objectives\n", + "In this notebook, you will learn how to: \n", + "- Use Riva ServiceMaker to take a TLT exported .riva and generate a model repository\n", + "- Deploy the model(s) locally on the Riva Server\n", + "- Send inference requests from a demo client using Riva API bindings." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Pre-requisites" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To follow along, please make sure:\n", + "- You have access to NVIDIA NGC, and are able to download the Riva Quickstart [resources](https://ngc.nvidia.com/resources/ea-riva-stage:riva_quickstart/)\n", + "- Have an .riva model file that you wish to deploy. You can obtain this from `tlt export` (with `export_format=RIVA`). Please refer the tutorial on *Joint Intent Detection and Slot Filling using Transfer Learning Toolkit* for more details on training and exporting an .riva model.\n", + "- You have followed the steps in the setup notebook to startup, initialize and deploy the Riva Service" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Riva-deploy\n", + "\n", + "The deployment tool takes as input one or more Riva Model Intermediate Representation (RMIR) files and a target model repository directory. It creates an ensemble configuration specifying the pipeline for the execution and finally writes all those assets to the output model repository directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Syntax: riva-deploy -f dir-for-rmir/model.rmir:key output-dir-for-repository\n", + "!docker run --rm --gpus 1 -v $MODEL_LOC:/data $RIVA_SM_CONTAINER -- \\\n", + " riva-deploy -f /data/intent-slot.rmir:$KEY /data/models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Start Riva Server\n", + "Once the model repository is generated, we are ready to start the Riva server. From this step onwards you should follow the Riva setup notebook and modify the config.sh file as required, and proceed to the Inference step after deploying the Riva service. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Run Inference\n", + "Once the Riva server is up and running with your models, you can send inference requests querying the server. \n", + "\n", + "To send GRPC requests, the Riva Python API bindings for client can be used. This is available as a pip .whl which is installed in the setup notebook.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following code sample shows how you can perform inference using Riva Python API gRPC bindings:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import grpc\n", + "import riva_api.riva_nlp_pb2 as rnlp\n", + "import riva_api.riva_nlp_pb2_grpc as rnlp_srv\n", + "\n", + "def run_intent_slot(grpc_server, query):\n", + " channel = grpc.insecure_channel(grpc_server)\n", + " riva_nlp = rnlp_srv.RivaLanguageUnderstandingStub(channel)\n", + "\n", + " for q in query:\n", + " req = rnlp.AnalyzeIntentRequest()\n", + " req.query = q\n", + " req.options.domain = \"default\" # The is appended to \"riva_intent_\" to look for a\n", + " # model \"riva_intent_\". So the model \"riva_intent_default\"\n", + " # needs to be preloaded in riva server. If you would like to deploy your\n", + " # custom Joint Intent and Slot model use the `--domain_name` parameter in\n", + " # ServiceMaker's `riva-build intent_slot` command.\n", + " resp = riva_nlp.AnalyzeIntent(req)\n", + " print(\"Query:\", q)\n", + " print(\"Intent:\", resp.intent)\n", + " print(\"Slots:\", resp.slots)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_intent_slot(grpc_server=\"localhost:50051\",\n", + " query=[\"Please set an alarm for 6 am\", \"Play some pop music\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`NOTE`: You could also run the above inference code from inside the Riva Client container. The QuickStart provides a script `riva_start_client.sh` to run the container. It has more examples for different services." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " You can stop all docker container before shutting down the jupyter kernel. **Caution: The following command will stop all running containers**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!docker stop $(docker ps -a -q)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The AnalyzeIntent API can be used to query a Intent Slot classifier. The API can leverage a\n", + "# text classification model to classify the domain of the input query and then route to the \n", + "# appropriate intent slot model.\n", + "\n", + "# Lets first see an example where the domain is known. This skips execution of the domain classifier\n", + "# and proceeds directly to the intent/slot model for the requested domain.\n", + "\n", + "req = rnlp.AnalyzeIntentRequest()\n", + "req.query = \"How is the humidity in San Francisco?\"\n", + "req.options.domain = \"weather\" # The is appended to \"riva_intent_\" to look for a \n", + " # model \"riva_intent_\". So in this e.g., the model \"riva_intent_weather\"\n", + " # needs to be preloaded in riva server. If you would like to deploy your \n", + " # custom Joint Intent and Slot model use the `--domain_name` parameter in \n", + " # ServiceMaker's `riva-build intent_slot` command.\n", + "\n", + "resp = riva_nlp.AnalyzeIntent(req)\n", + "print(resp)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Below is an example where the input domain is not provided.\n", + "\n", + "req = rnlp.AnalyzeIntentRequest()\n", + "req.query = \"Is it going to rain tomorrow?\"\n", + "\n", + " # The input query is first routed to the a text classification model called \"riva_text_classification_domain\"\n", + " # The output class label of \"riva_text_classification_domain\" is appended to \"riva_intent_\"\n", + " # to get the appropriate Intent Slot model to execute for the input query.\n", + " # Note: The model \"riva_text_classification_domain\" needs to be loaded into Riva server and have the appropriate\n", + " # class labels that would invoke the corresponding intent slot model.\n", + "\n", + "resp = riva_nlp.AnalyzeIntent(req)\n", + "print(resp)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Some weather Intent queries grouped together\n", + "queries = [\n", + " \"Is it currently cloudy in Tokyo?\",\n", + " \"What is the annual rainfall in Pune?\",\n", + " \"What is the humidity going to be tomorrow?\"\n", + "]\n", + "for q in queries:\n", + " req = rnlp.AnalyzeIntentRequest()\n", + " req.query = q\n", + " start = time()\n", + " resp = riva_nlp.AnalyzeIntent(req)\n", + "\n", + " print(f\"[{resp.intent.class_name}]\\t{req.query}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise\n", + "\n", + "Use the Intent Slot classification API to classify individual Intents and Slots for the following utterances:\n", + "\n", + "\n", + "- I need to catch the early train tomorrow evening.\n", + "- I am very tired after all the work done yesterday.\n", + "- Will it rain tomorrow in New York?\n", + "- Is it currently sunny in Chicago?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## What's next?\n", + "You could train your own custom models in TLT and deploy them in Riva! You could scale up your deployment using Kubernetes with the Riva AI Services Helm Chart, which will pull the relevant Images and download model artifacts from NGC, generate the model repository, start and expose the Riva speech services." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/ISC/intent-slot-classification-training.ipynb b/ai/RIVA/English/Python/jupyter_notebook/ISC/intent-slot-classification-training.ipynb new file mode 100644 index 000000000..86a080297 --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/ISC/intent-slot-classification-training.ipynb @@ -0,0 +1,714 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[Home Page](../START_HERE_RIVA_HACKATHON.ipynb)\n", + "\n", + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[1]\n", + "[2](intent-slot-classification-deployment.ipynb)\n", + "[3](intent-slot-classification-Exercise.ipynb)\n", + "     \n", + "     \n", + "     \n", + "     \n", + "[Next Notebook](intent-slot-classification-deployment.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Joint Intent Detection and Slot Filling\n", + "\n", + "### Task Description\n", + "\n", + "Understanding the intent in natural language (Intent Classification) and extracting values of pertinent attributes or specific pieces of information from a sentence (Slot Filling) are two essential tasks in Natural Language Understanding (NLU). For example:
\n", + "\n", + "> In the query: *What is the weather in Santa Clara tomorrow morning?*\n", + "> we would like to classify the query as a `weather` Intent,\n", + "> and detect `Santa Clara` as a location slot and tomorrow morning as a `date_time` slot. Intents and Slots names are usually task specific and defined as labels in the training data. This is a fundamental step that is executed in any task-driven Conversational Assistant.
\n", + "\n", + "Recent research has shown the proficiency of BERT models in this task. TLT provides the capability to train a BERT model and perform inference for both intent detection and slot filling together." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BERT Model\n", + "In this notebook, we will show how to use a pre-trained [BERT](https://arxiv.org/pdf/1810.04805.pdf) (Bidirectional Encoder Representations from Transformers) model for Joint Intent and Slot Classification leveraging TLT. The BERT model has made major breakthroughs in Natural Language Understanding in recent years. For most applications, the model is typically trained in two phases, pre-training and fine-tuning. \n", + "- The BERT core model can be pre-trained on large, generic datasets to generate dense vector representations of input sentence(s). \n", + "- It can be quickly fine-tuned to perform a wide variety of tasks such as question/answering, sentiment analysis, or named entity recognition.\n", + "\n", + "The figure below shows a high-level block diagram of pre-training and fine-tuning BERT.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In alignment with the above, for pre-training we can take one of two approaches. We can either pre-train the BERT model with our own data, or use a model pre-trained by Nvidia. After we obtain a pre-trained model, the next step would be to fine-tune it for the Intent and Slot Classification task and run inference on the fine-tuned model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "In this notebook, you will learn how to leverage the simplicity and convenience of TLT to:\n", + "- Pre-process/convert a dataset for the [**Joint Intent and Slot Classification**](#isc-task-description).\n", + "- Take a [BERT](https://arxiv.org/pdf/1810.04805.pdf) model and [**Train/Finetune**](#isc-training) it on the [NLU Evaluation](https://github.com/xliuhw/NLU-Evaluation-Data) dataset\n", + "- Run [**Inference**](#isc-inference)\n", + "- [**Export**](#isc-export-onnx) the model for the [ONNX](https://onnx.ai/) format, or [export](#isc-export-riva) in a format suitable for deployment in [Riva](https://developer.nvidia.com/riva).\n", + "\n", + "The earlier sections in the notebook give a brief introduction to the Intent and Slot Classification task, the NLU Evaluation dataset and BERT. If you are already familiar with these, and want to jump right in, you can start at section on [Data Preparation](#isc-prepare-data)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Pre-requisites\n", + "For ease of use, please install TLT inside a python virtual environment. We recommend performing this step first and then launching the notebook from the virtual environment. This can be done using the Setup notebook (../riva-setup.ipynb) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### Preparing the dataset\n", + "#### The NLU Evaluation Dataset\n", + "For this tutorial, we use a virtual assistant interaction in home domain - `NLU Evaluation` dataset. The NLU dataset is available [here](https://github.com/xliuhw/NLU-Evaluation-Data). It was collected and annotated for various NLU tasks by Liu et. al in their IWSDS 2019 [paper](https://arxiv.org/abs/1903.05566), and more information about this dataset is present in the Github README." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Downloading the dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data is available in the github [repo](https://github.com/xliuhw/NLU-Evaluation-Data) and can be downloaded directly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# IMPORTANT NOTE: Set path to a folder where you want you data and results to be saved\n", + "DATA_DOWNLOAD_DIR = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NOTE: Ensure that wget and unzip utilities are available. If not, please install them\n", + "!wget 'https://github.com/xliuhw/NLU-Evaluation-Data/archive/master.zip' -P $DATA_DOWNLOAD_DIR\n", + "\n", + "# Extract the data\n", + "!unzip $DATA_DOWNLOAD_DIR/master.zip -d $DATA_DOWNLOAD_DIR" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Joint Intent Detection and Slot Filling using Transfer Learning Toolkit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Transfer learning* is the process of transferring learned features from one application to another. It is a commonly used training technique where you use a model trained on one task and re-train to use it on a different task.\n", + "\n", + "**Transfer Learning Toolkit (TLT)** is a simple and easy-to-use Python based AI toolkit for taking purpose-built AI models and customizing them with users' own data. Developers, researchers and software partners building Conversational AI and Vision AI can leverage TLT to avoid the hassle of training from scratch, and significantly accelerate their workflow. \n", + "\n", + "
<\\center>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## TLT workflow\n", + "The rest of the notebook shows what a sample TLT workflow looks like." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setting TLT Mounts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that our dataset has been downloaded, an important step in using TLT is to set up the directory mounts. The TLT launcher uses docker containers under the hood, and **for our data and results directory to be visible to the docker, they need to be mapped**. The launcher can be configured using the config file `~/.tlt_mounts.json`. Apart from the mounts, you can also configure additional options like the Environment Variables and amount of Shared Memory available to the TLT launcher.
\n", + "\n", + "`IMPORTANT NOTE:` The code below creates a sample `~/.tlt_mounts.json` file. Here, we can map directories in which we save the data, specs, results and cache. You should configure it for your specific case such your these directories are correctly visible to the docker container. **Please also ensure that the source directories exist on your machine!**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%bash\n", + "tee ~/.tlt_mounts.json <<'EOF'\n", + "{\n", + " \"Mounts\":[\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/data\"\n", + " },\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/specs\"\n", + " },\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/results\"\n", + " },\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/root/.cache\"\n", + " }\n", + " ]\n", + "}\n", + "EOF" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make sure the source directories exist, if not, create them\n", + "! mkdir \n", + "! mkdir \n", + "! mkdir " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The rest of the notebook exemplifies the simplicity of the TLT workflow. Users with basic knowledge of Deep Learning can get started building their own custom models using a simple specification file. It's essentially just one command each to run data preprocessing, training, fine-tuning, evaluation, inference, and export! All configurations happen through YAML spec files
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Configuration/Specification Files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The essence of all commands in TLT lies in the YAML spec files. There are sample spec files already available for you to use directly or as reference to create your own. Through these spec files, you can tune many knobs like the model, dataset, hyperparameters, optimizer etc. Each command (like train, finetune, evaluate etc.) should have a dedicated spec file with configurations pertinent to it.
\n", + "\n", + "Here is an example of the training spec file:\n", + "\n", + "---\n", + "```\n", + "# Name of the file where trained model will be saved.\n", + "save_to: trained-model.tlt\n", + "\n", + "optim:\n", + " name: adam\n", + " lr: 2e-5\n", + " # optimizer arguments\n", + " betas: [0.9, 0.999]\n", + " weight_decay: 0.01\n", + "\n", + " # scheduler setup\n", + " sched:\n", + " name: WarmupAnnealing\n", + " # Scheduler params\n", + " warmup_steps: null\n", + " warmup_ratio: 0.1\n", + " last_epoch: -1\n", + " # pytorch lightning args\n", + " monitor: val_loss\n", + " reduce_on_plateau: false\n", + "\n", + "model:\n", + " class_balancing: null # choose from [null, weighted_loss]. weighted_loss enables the weighted class balancing of the loss, may be used for handling unbalanced classes\n", + " intent_loss_weight: 0.6 # relation of intent to slot loss in total loss (between 0 to 1)\n", + " pad_label: -1 # if -1 not slot token will be used\n", + " ignore_extra_tokens: false\n", + " ignore_start_end: true # do not use first and last token for slot training\n", + "\n", + " tokenizer:\n", + " tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece\n", + " vocab_file: null # path to vocab file \n", + " tokenizer_model: null # only used if tokenizer is sentencepiece\n", + " special_tokens: null\n", + "\n", + " language_model:\n", + " max_seq_length: 50\n", + " pretrained_model_name: bert-base-uncased\n", + " lm_checkpoint: null\n", + " config_file: null # json file, precedence over config\n", + " config: null\n", + "\n", + " head:\n", + " num_output_layers: 2\n", + " fc_dropout: 0.1\n", + "...\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Set Relevant Paths\n", + "Please set these paths according to your environment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NOTE: The following paths are set from the perspective of the TLT Docker. \n", + "\n", + "# The data is saved here\n", + "DATA_DIR='/data'\n", + "\n", + "# The configuration files are stored here\n", + "SPECS_DIR='/specs/intent_slot_classification'\n", + "\n", + "# The results are saved at this path\n", + "RESULTS_DIR='/results/intent_slot_classification'\n", + "\n", + "# Set your encryption key, and use the same key for all commands\n", + "KEY='tlt_encode'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Downloading Specs\n", + "We can proceed to downloading the spec files. The user may choose to modify/rewrite these specs, or even individually override them through the launcher. You can download the default spec files by using the `download_specs` command.
\n", + "\n", + "The -o argument indicating the folder where the default specification files will be downloaded, and -r that instructs the script where to save the logs. **Make sure the -o points to an empty folder!**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt intent_slot_classification download_specs \\\n", + " -r $RESULTS_DIR \\\n", + " -o $SPECS_DIR" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Data Convert\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In preparation for training/fine-tuning, we need to preprocess the dataset. `tlt intent_slot_classification dataset_convert` command can be used in conjunction with appropriate configuration in the spec file. Here is the sample `dataset_convert.yaml` spec file we use:\n", + "```\n", + "# Dataset. Available options: [assistant]\n", + "dataset_name: assistant\n", + "\n", + "# Path to the folder containing the dataset source files.\n", + "source_data_dir: ???\n", + "\n", + "# Path to the output folder.\n", + "target_data_dir: ???\n", + "\n", + "```\n", + " We encourage you to take a look at the .yaml spec files we provide!\n", + "As we show below, you can override the `source_data_dir` and `target_data_dir` options with appropriate paths." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt intent_slot_classification dataset_convert \\\n", + " -e $SPECS_DIR/dataset_convert.yaml \\\n", + " -r $RESULTS_DIR/dataset_convert \\\n", + " source_data_dir=$DATA_DIR/NLU-Evaluation-Data-master \\\n", + " target_data_dir=$DATA_DIR/NLU-Evaluation-Data-processed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The command writes the processed assistant commands along with train/val splits dataset at the specific `target_data_dir`. With this dataset, it found 64 intents and 55 slot types." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### Training / Fine-tuning\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Training a model using TLT is as simple as configuring your spec file and running the train command. The code cell below uses the train.yaml spec file available for users as reference. It is configured by default to use the pretrained `bert-base-uncased` model. For this task, you will almost always use the pretrained BERT language models, and train for this task with your custom data. In this sense, this step could also be thought of as fine-tuning. Typically, to get good results you may need to train the model for 20-50 epochs depending on the size of the data. Training with your own data will take about 15-30 mins on a single GPU. Since this is a demonstration, we train for just 1 epoch below.\n", + "\n", + "The spec file configurations can easily be overridden using the tlt-launcher CLI as shown below. For instance, below we override the `data_dir`, `trainer.max_epochs`, `training_ds.num_workers` and `validation_ds.num_workers` configurations to suit our needs.
\n", + "\n", + "For training a Joint Intent Detection and Slot Classification model in TLT, we use the `tlt intent_slot_classification train` command with the following args:\n", + "- `-e`: Path to the spec file\n", + "- `-g`: Number of GPUs to use\n", + "- `-k`: User specified encryption key to use while saving/loading the model\n", + "- `-r`: Path to a folder where the outputs should be written. Make sure this is mapped in tlt_mounts.json\n", + "- Any overrides to the spec file eg. `trainer.max_epochs`\n", + "
\n", + "\n", + "\n", + "More details about these arguments are present in the [TLT Getting Started Guide](https://docs.nvidia.com/tlt/tlt-user-guide/index.html)
\n", + "`Note:` All file paths correspond to the destination mounted directory that is visible in the TLT docker container used in backend.
\n", + "\n", + "`Note:` If you wish to proceed with a trained dataset for better inference results, you can find a .nemo model [here](\n", + "https://ngc.nvidia.com/catalog/collections/nvidia:nemotrainingframework).\n", + "\n", + "Simply re-name the .nemo file to .tlt and pass it through the finetune pipeline.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt intent_slot_classification train \\\n", + " -e $SPECS_DIR/train.yaml \\\n", + " -g 1 \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/train \\\n", + " data_dir=$DATA_DIR/NLU-Evaluation-Data-processed \\\n", + " trainer.max_epochs=1 \\\n", + " training_ds.num_workers=4 \\\n", + " validation_ds.num_workers=4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The train command produces a .tlt file called `trained-model.tlt` saved at `$RESULTS_DIR/train/checkpoints/trained-model.tlt`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### Evaluation\n", + "The evaluation spec .yaml is as simple as:\n", + "\n", + "```\n", + "# Name of the .tlt file where trained model will be restored from.\n", + "restore_from: trained-model.tlt\n", + "\n", + "data_dir: ???\n", + "\n", + "test_ds:\n", + " prefix: test\n", + " batch_size: 32\n", + " shuffle: false\n", + " num_samples: -1\n", + " num_workers: 2\n", + " drop_last: false\n", + " pin_memory: false\n", + "\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt intent_slot_classification evaluate \\\n", + " -e $SPECS_DIR/evaluate.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/train/checkpoints/trained-model.tlt \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/evaluate \\\n", + " data_dir=$DATA_DIR/NLU-Evaluation-Data-processed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output of Evaluation should give the precision, recall, and f1 report for intents and slots. Remember that we had trained for just 1 epoch since this is a demonstration!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### Inference\n", + "Inference using a .tlt trained or fine-tuned model uses the `tlt intent_slot_classification infer` command.
\n", + "The infer.yaml is also very simple, and we can directly give inputs for the model to run inference.\n", + "```\n", + "# \"Simulate\" user input:\n", + "input_batch:\n", + " - 'set alarm for seven thirty am'\n", + " - 'lower volume by fifty percent'\n", + " - 'what is my schedule for tomorrow'\n", + "\n", + "```\n", + "\n", + "We encourage you to try out your own inputs as an exercise!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt intent_slot_classification infer \\\n", + " -e $SPECS_DIR/infer.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/train/checkpoints/trained-model.tlt \\\n", + " -r $RESULTS_DIR/infer \\\n", + " -k $KEY" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This command returns the predicted intents and slots for each of the input sequences. Of course, these intents and slots are what it was trained on. You may see a full list of intents and slots in the processed data directory." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### Export to ONNX" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[ONNX](https://onnx.ai/) is a popular open format for machine learning models. It enables interoperability between different frameworks, easing the path to production. TLT provides commands to export the .tlt model to the ONNX format in an .eonnx archive. \n", + "\n", + "Sample usage of the `tlt intent_slot_classification export` command is shown in the following code cell. The `export_format` configuration can be set to `ONNX` to achieve this.\n", + "\n", + "The export.yaml file we use looks like:\n", + "```\n", + "# Name of the .tlt EFF archive to be loaded/model to be exported.\n", + "restore_from: trained-model.tlt\n", + "\n", + "# Set export format: ONNX | JARVIS\n", + "export_format: ONNX\n", + "\n", + "# Output EFF archive containing ONNX.\n", + "export_to: exported-model.eonnx\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt intent_slot_classification export \\\n", + " -e $SPECS_DIR/export.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/train/checkpoints/trained-model.tlt \\\n", + " -r $RESULTS_DIR/export \\\n", + " -k $KEY \\\n", + " export_format=ONNX" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This command exports the model as `exported-model.eonnx` which is essentially an archive containing the .onnx model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Inference using ONNX" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TLT provides the capability to use the exported .eonnx model for inference. The command `tlt intent_slot_classification infer_onnx` is very similar to the inference command for .tlt models. Again, the inputs in the spec file used are just for demo purposes, you may choose to try out your custom input!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt intent_slot_classification infer_onnx \\\n", + " -e $SPECS_DIR/infer_onnx.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/export/exported-model.eonnx \\\n", + " -r $RESULTS_DIR/infer_onnx \\\n", + " -k $KEY" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### Export to RIVA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With TLT, you can also export your model in a format that can deployed using [Nvidia Riva](https://developer.nvidia.com/riva), a highly performant application framework for multi-modal conversational AI services using GPUs! The same command for exporting to ONNX can be used here. The only small variation is the configuration for `export_format` in the spec file!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt intent_slot_classification export \\\n", + " -e $SPECS_DIR/export.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/train/checkpoints/trained-model.tlt \\\n", + " -r $RESULTS_DIR/export_riva \\\n", + " -k $KEY \\\n", + " export_format=JARVIS \\\n", + " export_to=intent-slot-model.riva" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model is exported as `slot-model.riva` which is in a format suited for deployment in Riva." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### What's Next?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You could use TLT to build custom models for your own applications, or you could deploy the custom model to Nvidia Riva!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/Intro/Intro_to_Riva.ipynb b/ai/RIVA/English/Python/jupyter_notebook/Intro/Intro_to_Riva.ipynb new file mode 100644 index 000000000..4355d73b3 --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/Intro/Intro_to_Riva.ipynb @@ -0,0 +1,683 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Python API Examples\n", + "\n", + "This notebook walks through the basics of the Riva Speech and Language AI Services." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning objectives\n", + "\n", + "- Understand how interact with Riva Speech and Natural Languages APIs, services and use cases\n", + "\n", + "## Requirements and setup\n", + "\n", + "To execute this notebook, please follow the setup steps in [README](./README.md).\n", + "\n", + "We first generate some required libraries." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Content\n", + "1. [Offline ASR Example](#1)\n", + "1. [Core NLP Service Examples](#2)\n", + "1. [TTS Service Example](#3)\n", + "1. [Riva NLP Service Examples](#4)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "## Overview\n", + "\n", + "NVIDIA Riva is a platform for building and deploying AI applications that fuse vision, speech and other sensors. It offers a complete workflow to build, train and deploy AI systems that can use visual cues such as gestures and gaze along with speech in context. With the Riva platform, you can:\n", + "\n", + "- Build speech and visual AI applications using pretrained NVIDIA Neural Modules ([NeMo](https://github.com/NVIDIA/NeMo)) available at NVIDIA GPU Cloud ([NGC](https://ngc.nvidia.com/catalog/models?orderBy=modifiedDESC&query=%20label%3A%22NeMo%2FPyTorch%22&quickFilter=models&filters=)).\n", + "\n", + "- Transfer learning: re-train your model on domain-specific data, with NVIDIA [NeMo](https://github.com/NVIDIA/NeMo). NeMo is a toolkit and platform that enables researchers to define and build new state-of-the-art speech and natural language processing models.\n", + "\n", + "- Optimize neural network performance and latency using NVIDIA TensorRT \n", + "\n", + "- Deploy AI applications with TensorRT Inference Server:\n", + " - Support multiple network formats: ONNX, TensorRT plans, PyTorch TorchScript models.\n", + " - Deployement on multiple platforms: from datacenter to edge servers, via Helm to K8s cluster, on NVIDIA Volta/Turing GPUs or Jetson Xavier platforms.\n", + "\n", + "See the below video for a demo of Riva capabilities." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import IFrame\n", + "\n", + "# Riva Youtube demo video\n", + "IFrame(\"https://www.youtube.com/embed/r264lBi1nMU?rel=0&controls=0&showinfo=0\", width=\"560\", height=\"315\", frameborder=\"0\", allowfullscreen=True)" + ] + }, + { + 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For more detailed information on Riva, please refer to the [Riva developer documentation](https://developer.nvidia.com/riva).\n", + "\n", + "## Introduction the Riva Speech and Natural Languages services\n", + "\n", + "Riva offers a rich set of speech and natural language understanding services such as:\n", + "\n", + "- Automated speech recognition (ASR)\n", + "- Text-to-Speech synthesis (TTS)\n", + "- A collection of natural language understanding services such as named entity recognition (NER), punctuation, intent classification." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import io\n", + "import librosa\n", + "from time import time\n", + "import numpy as np\n", + "import IPython.display as ipd\n", + "import grpc\n", + "import requests\n", + "\n", + "# NLP proto\n", + "import riva_api.riva_nlp_pb2 as rnlp\n", + "import riva_api.riva_nlp_pb2_grpc as rnlp_srv\n", + "\n", + "# ASR proto\n", + "import riva_api.riva_asr_pb2 as rasr\n", + "import riva_api.riva_asr_pb2_grpc as rasr_srv\n", + "\n", + "# TTS proto\n", + "import riva_api.riva_tts_pb2 as rtts\n", + "import riva_api.riva_tts_pb2_grpc as rtts_srv\n", + "import riva_api.riva_audio_pb2 as ra" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Riva clients and connect to Riva Speech API server\n", + "\n", + "The below URI assumes a local deployment of the Riva Speech API server on the default port. In case the server deployment is on a different host or via Helm chart on Kubernetes, the user should use an appropriate URI." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "##change this configuration to match your DGX number and your RIVA port number\n", + "##for example, 'dgx0180:50055'\n", + "\n", + "channel = grpc.insecure_channel('localhost:50051')\n", + "\n", + "\n", + "riva_asr = rasr_srv.RivaSpeechRecognitionStub(channel)\n", + "riva_nlp = rnlp_srv.RivaLanguageUnderstandingStub(channel)\n", + "riva_tts = rtts_srv.RivaSpeechSynthesisStub(channel)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## 1. Offline ASR Example\n", + "\n", + "Riva Speech API supports `.wav` files in PCM format, `.alaw`, `.mulaw` and `.flac` formats with single channel in this release. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This example uses a .wav file with LINEAR_PCM encoding.\n", + "# read in an audio file from local disk\n", + "path = \"/work/wav/sample.wav\"\n", + "audio, sr = librosa.core.load(path, sr=None)\n", + "with io.open(path, 'rb') as fh:\n", + " content = fh.read()\n", + "ipd.Audio(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ASR Transcript: What is natural language processing? \n", + "\n", + "\n", + "Full Response Message:\n", + "results {\n", + " alternatives {\n", + " transcript: \"What is natural language processing? \"\n", + " confidence: -8.908161163330078\n", + " }\n", + " channel_tag: 1\n", + " audio_processed: 6.400000095367432\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "# Set up an offline/batch recognition request\n", + "req = rasr.RecognizeRequest()\n", + "req.audio = content # raw bytes\n", + "req.config.encoding = ra.AudioEncoding.LINEAR_PCM # Supports LINEAR_PCM, FLAC, MULAW and ALAW audio encodings\n", + "req.config.sample_rate_hertz = sr # Audio will be resampled if necessary\n", + "req.config.language_code = \"en-US\" # Ignored, will route to correct model in future release\n", + "req.config.max_alternatives = 1 # How many top-N hypotheses to return\n", + "req.config.enable_automatic_punctuation = True # Add punctuation when end of VAD detected\n", + "req.config.audio_channel_count = 1 # Mono channel\n", + "\n", + "response = riva_asr.Recognize(req)\n", + "asr_best_transcript = response.results[0].alternatives[0].transcript\n", + "print(\"ASR Transcript:\", asr_best_transcript)\n", + "\n", + "print(\"\\n\\nFull Response Message:\")\n", + "print(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## 2. Core NLP Service Examples\n", + "\n", + "All of the Core NLP Services support batched requests. The maximum batch size,\n", + "if any, of the underlying models is hidden from the end user and automatically\n", + "batched by the Riva and TRTIS servers.\n", + "\n", + "The Core NLP API provides three methods currently:\n", + "\n", + " 1. TransformText - map an input string to an output string\n", + " \n", + " 2. ClassifyText - return a single label for the input string\n", + " \n", + " 3. ClassifyTokens - return a label per input token" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TransformText Output:\n", + " Add punctuation to this sentence.\n", + " Do you have any red Nvidia shirts?\n", + " I need one cpu, four gpus and lots of memory for my new computer. It's going to be very cool.\n" + ] + } + ], + "source": [ + "# Use the TextTransform API to run the punctuation model\n", + "req = rnlp.TextTransformRequest()\n", + "req.model.model_name = \"riva_punctuation\"\n", + "req.text.append(\"add punctuation to this sentence\")\n", + "req.text.append(\"do you have any red nvidia shirts\")\n", + "req.text.append(\"i need one cpu four gpus and lots of memory \"\n", + " \"for my new computer it's going to be very cool\")\n", + "\n", + "nlp_resp = riva_nlp.TransformText(req)\n", + "print(\"TransformText Output:\")\n", + "print(\"\\n\".join([f\" {x}\" for x in nlp_resp.text]))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Named Entities:\n", + " jensen huang (PER)\n", + " nvidia corporation (ORG)\n", + " santa clara (LOC)\n", + " california (LOC)\n" + ] + } + ], + "source": [ + "# Use the TokenClassification API to run a Named Entity Recognition (NER) model\n", + "# Note: the model configuration of the NER model indicates that the labels are\n", + "# in IOB format. Riva, subsequently, knows to:\n", + "# a) ignore 'O' labels\n", + "# b) Remove B- and I- prefixes from labels\n", + "# c) Collapse sequences of B- I- ... I- tokens into a single token\n", + "\n", + "req = rnlp.TokenClassRequest()\n", + "req.model.model_name = \"riva_ner\" # If you have deployed a custom model with the domain_name \n", + " # parameter in ServiceMaker's `riva-build` command then you should use \n", + " # \"riva_ner_\" where \n", + " # is the name you provided to the domain_name parameter.\n", + "\n", + "req.text.append(\"Jensen Huang is the CEO of NVIDIA Corporation, \"\n", + " \"located in Santa Clara, California\")\n", + "resp = riva_nlp.ClassifyTokens(req)\n", + "\n", + "print(\"Named Entities:\")\n", + "for result in resp.results[0].results:\n", + " print(f\" {result.token} ({result.label[0].class_name})\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "results {\n", + " labels {\n", + " class_name: \"weather\"\n", + " score: 0.9975590109825134\n", + " }\n", + "}\n", + "results {\n", + " labels {\n", + " class_name: \"meteorology\"\n", + " score: 0.984375\n", + " }\n", + "}\n", + "results {\n", + " labels {\n", + " class_name: \"personality\"\n", + " score: 0.984375\n", + " }\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "# Submit a TextClassRequest for text classification.\n", + "# Riva NLP comes with a default text_classification domain called \"domain_misty\" which consists of \n", + "# 4 classes: meteorology, personality, weather and nomatch\n", + "\n", + "request = rnlp.TextClassRequest()\n", + "request.model.model_name = \"riva_text_classification_domain\" # If you have deployed a custom model \n", + " # with the `--domain_name` parameter in ServiceMaker's `riva-build` command \n", + " # then you should use \"riva_text_classification_\"\n", + " # where is the name you provided to the \n", + " # domain_name parameter. In this case the domain_name is \"domain\"\n", + "request.text.append(\"Is it going to snow in Burlington, Vermont tomorrow night?\")\n", + "request.text.append(\"What causes rain?\")\n", + "request.text.append(\"What is your favorite season?\")\n", + "ct_response = riva_nlp.ClassifyText(request)\n", + "print(ct_response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## 3. TTS Service Example\n", + "\n", + "Subsequent releases will include added features, including model registration to support multiple languages/voices with the same API. Support for resampling to alternative sampling rates will also be added." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "req = rtts.SynthesizeSpeechRequest()\n", + "req.text = \"Is it recognize speech or wreck a nice beach?\"\n", + "req.language_code = \"en-US\" # currently required to be \"en-US\"\n", + "req.encoding = ra.AudioEncoding.LINEAR_PCM # Supports LINEAR_PCM, FLAC, MULAW and ALAW audio encodings\n", + "req.sample_rate_hz = 22050 # ignored, audio returned will be 22.05KHz\n", + "req.voice_name = \"ljspeech\" # ignored\n", + "\n", + "resp = riva_tts.Synthesize(req)\n", + "audio_samples = np.frombuffer(resp.audio, dtype=np.float32)\n", + "ipd.Audio(audio_samples, rate=22050)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## 4. Riva NLP Service Examples\n", + "\n", + "The NLP Service contains higher-level/more application-specific NLP APIs. This\n", + "guide demonstrates how the AnalyzeIntent API can be used for queries across\n", + "both known and unknown domains." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "intent {\n", + " class_name: \"weather.humidity\"\n", + " score: 0.983601987361908\n", + "}\n", + "slots {\n", + " token: \"san francisco\"\n", + " label {\n", + " class_name: \"weatherplace\"\n", + " score: 0.9822959899902344\n", + " }\n", + "}\n", + "slots {\n", + " token: \"?\"\n", + " label {\n", + " class_name: \"weatherplace\"\n", + " score: 0.6474800109863281\n", + " }\n", + "}\n", + "domain_str: \"weather\"\n", + "domain {\n", + " class_name: \"weather\"\n", + " score: 1.0\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "# The AnalyzeIntent API can be used to query a Intent Slot classifier. The API can leverage a\n", + "# text classification model to classify the domain of the input query and then route to the \n", + "# appropriate intent slot model.\n", + "\n", + "# Lets first see an example where the domain is known. This skips execution of the domain classifier\n", + "# and proceeds directly to the intent/slot model for the requested domain.\n", + "\n", + "req = rnlp.AnalyzeIntentRequest()\n", + "req.query = \"How is the humidity in San Francisco?\"\n", + "req.options.domain = \"weather\" # The is appended to \"riva_intent_\" to look for a \n", + " # model \"riva_intent_\". So in this e.g., the model \"riva_intent_weather\"\n", + " # needs to be preloaded in riva server. If you would like to deploy your \n", + " # custom Joint Intent and Slot model use the `--domain_name` parameter in \n", + " # ServiceMaker's `riva-build intent_slot` command.\n", + "\n", + "resp = riva_nlp.AnalyzeIntent(req)\n", + "print(resp)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "intent {\n", + " class_name: \"weather.rainfall\"\n", + " score: 0.9661880135536194\n", + "}\n", + "slots {\n", + " token: \"tomorrow\"\n", + " label {\n", + " class_name: \"weatherforecastdaily\"\n", + " score: 0.5325539708137512\n", + " }\n", + "}\n", + "slots {\n", + " token: \"?\"\n", + " label {\n", + " class_name: \"weatherplace\"\n", + " score: 0.6895459890365601\n", + " }\n", + "}\n", + "domain_str: \"weather\"\n", + "domain {\n", + " class_name: \"weather\"\n", + " score: 0.9975590109825134\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "# Below is an example where the input domain is not provided.\n", + "\n", + "req = rnlp.AnalyzeIntentRequest()\n", + "req.query = \"Is it going to rain tomorrow?\"\n", + "\n", + " # The input query is first routed to the a text classification model called \"riva_text_classification_domain\"\n", + " # The output class label of \"riva_text_classification_domain\" is appended to \"riva_intent_\"\n", + " # to get the appropriate Intent Slot model to execute for the input query.\n", + " # Note: The model \"riva_text_classification_domain\" needs to be loaded into Riva server and have the appropriate\n", + " # class labels that would invoke the corresponding intent slot model.\n", + "\n", + "resp = riva_nlp.AnalyzeIntent(req)\n", + "print(resp)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[weather.cloudy]\tIs it currently cloudy in Tokyo?\n", + "[weather.rainfall]\tWhat is the annual rainfall in Pune?\n", + "[weather.humidity]\tWhat is the humidity going to be tomorrow?\n" + ] + } + ], + "source": [ + "# Some weather Intent queries\n", + "queries = [\n", + " \"Is it currently cloudy in Tokyo?\",\n", + " \"What is the annual rainfall in Pune?\",\n", + " \"What is the humidity going to be tomorrow?\"\n", + "]\n", + "for q in queries:\n", + " req = rnlp.AnalyzeIntentRequest()\n", + " req.query = q\n", + " start = time()\n", + " resp = riva_nlp.AnalyzeIntent(req)\n", + "\n", + " print(f\"[{resp.intent.class_name}]\\t{req.query}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time to complete 10 synchronous requests: 0.05957150459289551\n", + "Time to complete 10 asynchronous requests: 0.020952463150024414\n", + "\n" + ] + } + ], + "source": [ + "# Demonstrate latency by calling repeatedly.\n", + "# NOTE: this is a synchronous API call, so request #N will not be sent until\n", + "# response #N-1 is returned. This means latency and throughput will be negatively\n", + "# impacted by long-distance & VPN connections\n", + "\n", + "req = rnlp.TextTransformRequest()\n", + "req.text.append(\"i need one cpu four gpus and lots of memory for my new computer it's going to be very cool\")\n", + "\n", + "iterations = 10\n", + "# Demonstrate synchronous performance\n", + "start_time = time()\n", + "for _ in range(iterations):\n", + " nlp_resp = riva_nlp.PunctuateText(req)\n", + "end_time = time()\n", + "print(f\"Time to complete {iterations} synchronous requests: {end_time-start_time}\")\n", + "\n", + "# Demonstrate async performance\n", + "start_time = time()\n", + "futures = []\n", + "for _ in range(iterations):\n", + " futures.append(riva_nlp.PunctuateText.future(req))\n", + "for f in futures:\n", + " f.result()\n", + "end_time = time()\n", + "print(f\"Time to complete {iterations} asynchronous requests: {end_time-start_time}\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## 5. Go deeper into Riva capabilities\n", + "\n", + "Now that you have a basic introduction to the Riva APIs, you may like to try out:\n", + "\n", + "### 1. Sample apps:\n", + "\n", + "Riva comes with various sample apps as a demonstration for how to use the APIs to build interesting applications such as a [chatbot](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/samples/weather.html), a domain specific speech recognition or [keyword (entity) recognition system](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/samples/callcenter.html), or simply how Riva allows scaling out for handling massive amount of requests at the same time. ([SpeechSquad)](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/samples/speechsquad.html) \n", + "Have a look at the Sample Application section in the [Riva developer documentation](https://developer.nvidia.com/riva) for all the sample apps.\n", + "\n", + "\n", + "### 2. Finetune your own domain specific Speech or NLP model and deploy into Riva.\n", + "\n", + "Train the latest state-of-the-art speech and natural language processing models on your own data using [NeMo](https://github.com/NVIDIA/NeMo) or [Transfer Learning ToolKit](https://developer.nvidia.com/transfer-learning-toolkit) and deploy them on Riva using the [Riva ServiceMaker tool](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/model-servicemaker.html).\n", + "\n", + "\n", + "### 3. Further resources:\n", + "\n", + "Explore the details of each of the APIs and their functionalities in the [docs](https://docs.nvidia.com/deeplearning/jarvis/user-guide/docs/protobuf-api/protobuf-api-root.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/Intro/riva-setup.ipynb b/ai/RIVA/English/Python/jupyter_notebook/Intro/riva-setup.ipynb new file mode 100644 index 000000000..65cf69e91 --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/Intro/riva-setup.ipynb @@ -0,0 +1,392 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "20621803", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "5c176255", + "metadata": {}, + "source": [ + "### Installing and setting up TLT\n", + "\n", + "For ease of use, please install TLT inside a python virtual environment. We recommend performing this step first and then launching the notebook from the virtual environment.\n", + "\n", + "In addition to installing TLT python package, please make sure of the following software requirements:\n", + "\n", + "1. python 3.6.9\n", + "2. docker-ce > 19.03.5\n", + "3. docker-API 1.40\n", + "4. nvidia-container-toolkit > 1.3.0-1\n", + "5. nvidia-container-runtime > 3.4.0-1\n", + "6. nvidia-docker2 > 2.5.0-1\n", + "7. nvidia-driver >= 455.23" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "879f2cb6", + "metadata": {}, + "outputs": [], + "source": [ + "##installing tlt\n", + "! pip install nvidia-pyindex\n", + "! pip install nvidia-tlt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e043b894", + "metadata": {}, + "outputs": [], + "source": [ + "##setting relevant paths\n", + "# NOTE: The following paths are set from the perspective of the TLT Docker.\n", + "\n", + "# The data is saved here\n", + "DATA_DIR = \"/data\"\n", + "SPECS_DIR = \"/specs\"\n", + "RESULTS_DIR = \"/results\"\n", + "\n", + "# Set your encryption key, and use the same key for all commands\n", + "KEY = 'tlt_encode'" + ] + }, + { + "cell_type": "markdown", + "id": "7f956768", + "metadata": {}, + "source": [ + "### Download AN4 Speech Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cda58d19", + "metadata": {}, + "outputs": [], + "source": [ + "! wget http://www.speech.cs.cmu.edu/databases/an4/an4_sphere.tar.gz" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5aad36ce", + "metadata": {}, + "outputs": [], + "source": [ + "! tar -xvf an4_sphere.tar.gz \n", + "! mv an4 $DATA_DIR" + ] + }, + { + "cell_type": "markdown", + "id": "e96fd9c9", + "metadata": {}, + "source": [ + "### Pre-Processing" + ] + }, + { + "cell_type": "markdown", + "id": "c28ecfa5", + "metadata": {}, + "source": [ + "This step converts the mp3 files into wav files and splits the data into training and testing sets. It also generates a \"meta-data\" file to be consumed by the dataloader for training and testing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3604982", + "metadata": {}, + "outputs": [], + "source": [ + "! tlt speech_to_text dataset_convert \\\n", + " -e $SPECS_DIR/speech_to_text/dataset_convert_an4.yaml \\\n", + " source_data_dir=$DATA_DIR/an4 \\\n", + " target_data_dir=$DATA_DIR/an4_converted" + ] + }, + { + "cell_type": "markdown", + "id": "bc93df70", + "metadata": {}, + "source": [ + "---\n", + "## Riva ServiceMaker\n", + "Servicemaker is the set of tools that aggregates all the necessary artifacts (models, files, configurations, and user settings) for Riva deployment to a target environment. It has two main components as shown below:" + ] + }, + { + "cell_type": "markdown", + "id": "d14a6ad3", + "metadata": {}, + "source": [ + "### 1. Riva-build\n", + "\n", + "This step helps build a Riva-ready version of the model. It’s only output is an intermediate format (called a RMIR) of an end to end pipeline for the supported services within Riva. We are taking a ASR QuartzNet Model in consideration
\n", + "\n", + "`riva-build` is responsible for the combination of one or more exported models (.riva files) into a single file containing an intermediate format called Riva Model Intermediate Representation (.rmir). This file contains a deployment-agnostic specification of the whole end-to-end pipeline along with all the assets required for the final deployment and inference. Please checkout the [documentation](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/service-asr.html#pipeline-configuration) to find out more." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b9990631", + "metadata": {}, + "outputs": [], + "source": [ + "# IMPORTANT: UPDATE THESE PATHS \n", + "\n", + "# ServiceMaker Docker\n", + "RIVA_SM_CONTAINER = \"\"\n", + "\n", + "# Directory where the .riva model is stored $MODEL_LOC/*.riva\n", + "MODEL_LOC = \"\"\n", + "\n", + "# Name of the .riva file\n", + "MODEL_NAME = \"\"\n", + "\n", + "# Key that model is encrypted with, while exporting with TLT\n", + "KEY = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9da4ede8", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the ServiceMaker docker\n", + "! docker pull $RIVA_SM_CONTAINER" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80eb0fa6", + "metadata": {}, + "outputs": [], + "source": [ + "# Syntax: riva-build output-dir-for-rmir/model.rmir:key dir-for-riva/model.riva:key --acoustic_model_name=\n", + "! docker run --rm --gpus 0 -v $MODEL_LOC:/data $RIVA_SM_CONTAINER -- \\\n", + " riva-build speech_recognition /data/asr.rmir:$KEY /data/$MODEL_NAME:$KEY --offline \\\n", + " --decoder_type=greedy" + ] + }, + { + "cell_type": "markdown", + "id": "605b8b8f", + "metadata": {}, + "source": [ + "### 2. Riva-deploy\n", + "\n", + "The deployment tool takes as input one or more Riva Model Intermediate Representation (RMIR) files and a target model repository directory. It creates an ensemble configuration specifying the pipeline for the execution and finally writes all those assets to the output model repository directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "616af5ce", + "metadata": {}, + "outputs": [], + "source": [ + "## FOR ASR build\n", + "# Syntax: riva-deploy -f dir-for-rmir/model.rmir:key output-dir-for-repository\n", + "! docker run --rm --gpus 0 -v $MODEL_LOC:/data $RIVA_SM_CONTAINER -- \\\n", + " riva-deploy -f /data/asr.rmir:$KEY /data/models/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf4e1682", + "metadata": {}, + "outputs": [], + "source": [ + "## FOR QA build\n", + "# Syntax: riva-build output-dir-for-rmir/model.rmir:key dir-for-riva/model.riva:key\n", + "!docker run --rm --gpus 1 -v $MODEL_LOC:/data $RIVA_SM_CONTAINER -- \\\n", + " riva-build qa /data/question-answering.rmir:$KEY /data/$MODEL_NAME:$KEY" + ] + }, + { + "cell_type": "markdown", + "id": "ef4078ce", + "metadata": {}, + "source": [ + "`NOTE:` Above, qa-model.riva is the qa model obtained from `tlt question_answering export`" + ] + }, + { + "cell_type": "markdown", + "id": "48cf51ec", + "metadata": {}, + "source": [ + "---\n", + "## Start Riva Server\n", + "Once the model repository is generated, we are ready to start the Riva server. From this step onwards you need to download the Riva QuickStart Resource from NGC. \n", + "Set the path to the directory here:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90e79251", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the Riva QuickStart directory\n", + "RIVA_DIR = \"\"" + ] + }, + { + "cell_type": "markdown", + "id": "85208b4a", + "metadata": {}, + "source": [ + "Next, we modify config.sh to enable relevant Riva services (asr for QuartzNet Model), provide the encryption key, and path to the model repository (`riva_model_loc`) generated in the previous step among other configurations. \n", + "\n", + "For instance, if above the model repository is generated at `$MODEL_LOC/models`, then you can specify `riva_model_loc` as the same directory as `MODEL_LOC`
\n", + "\n", + "Pretrained versions of models specified in models_asr/nlp/tts are fetched from NGC. Since we are using our custom model, we can comment it in models_asr (and any others that are not relevant to your use case).
\n", + "\n", + "\n", + "`NOTE:` You can perform this step of editing config.sh from outside this notebook." + ] + }, + { + "cell_type": "markdown", + "id": "d2e394d8", + "metadata": {}, + "source": [ + "#### config.sh snippet\n", + "```\n", + "# Enable or Disable Riva Services \n", + "service_enabled_asr=true ## MAKE CHANGES HERE\n", + "service_enabled_nlp=false ## MAKE CHANGES HERE\n", + "service_enabled_tts=false ## MAKE CHANGES HERE\n", + "\n", + "# Specify one or more GPUs to use\n", + "# specifying more than one GPU is currently an experimental feature, and may result in undefined behaviours.\n", + "gpus_to_use=\"device=0\"\n", + "\n", + "# Specify the encryption key to use to deploy models\n", + "MODEL_DEPLOY_KEY=\"tlt_encode\" ## MAKE CHANGES HERE\n", + "\n", + "# Locations to use for storing models artifacts\n", + "#\n", + "# If an absolute path is specified, the data will be written to that location\n", + "# Otherwise, a docker volume will be used (default).\n", + "#\n", + "# riva_init.sh will create a `rmir` and `models` directory in the volume or\n", + "# path specified. \n", + "#\n", + "# RMIR ($riva_model_loc/rmir)\n", + "# Riva uses an intermediate representation (RMIR) for models\n", + "# that are ready to deploy but not yet fully optimized for deployment. Pretrained\n", + "# versions can be obtained from NGC (by specifying NGC models below) and will be\n", + "# downloaded to $riva_model_loc/rmir by `riva_init.sh`\n", + "# \n", + "# Custom models produced by NeMo or TLT and prepared using riva-build\n", + "# may also be copied manually to this location $(riva_model_loc/rmir).\n", + "#\n", + "# Models ($riva_model_loc/models)\n", + "# During the riva_init process, the RMIR files in $riva_model_loc/rmir\n", + "# are inspected and optimized for deployment. The optimized versions are\n", + "# stored in $riva_model_loc/models. The riva server exclusively uses these\n", + "# optimized versions.\n", + "riva_model_loc=\"\" ## MAKE CHANGES HERE (Replace with MODEL_LOC) \n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "893749fe", + "metadata": {}, + "outputs": [], + "source": [ + "# Ensure you have permission to execute these scripts\n", + "! cd $RIVA_DIR && chmod +x ./riva_init.sh && chmod +x ./riva_start.sh" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02b3b9f8", + "metadata": {}, + "outputs": [], + "source": [ + "# Run Riva Init. This will fetch the containers/models\n", + "# YOU CAN SKIP THIS STEP IF YOU DID RIVA DEPLOY\n", + "! cd $RIVA_DIR && ./riva_init.sh config.sh" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7a4328de", + "metadata": {}, + "outputs": [], + "source": [ + "# Run Riva Start. This will deploy your model(s).\n", + "! cd $RIVA_DIR && ./riva_start.sh config.sh" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1fbe3413", + "metadata": {}, + "outputs": [], + "source": [ + "# Install client API bindings\n", + "!cd $RIVA_DIR && pip install $RIVA_API_WHL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "649ca521", + "metadata": {}, + "outputs": [], + "source": [ + "# IMPORTANT: Set the name of the whl file\n", + "RIVA_API_WHL = \"\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/NER/Backup.ipynb b/ai/RIVA/English/Python/jupyter_notebook/NER/Backup.ipynb new file mode 100644 index 000000000..294c0391c --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/NER/Backup.ipynb @@ -0,0 +1,346 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Previous Notebook](token-classification-deployment.ipynb)\n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[Home Page](../START_HERE_RIVA_BOOTCAMP.ipynb)\n", + "\n", + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[1](token-classification-training.ipynb)\n", + "[2](token-classification-deployment.ipynb)\n", + "[3]\n", + "     \n", + "     \n", + "     \n", + "     " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Testing a Named Entity Recognition Model in Riva - Exercise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "This notebook explores taking an .riva model, the result of `tlt token_classification` command, and leveraging the Riva ServiceMaker framework to aggregate all the necessary artifacts for Riva deployment to a target environment. Once the model is deployed in Riva, you can issue inference requests to the server. . " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Learning Objectives\n", + "In this notebook, you will learn how to: \n", + "- Deploy the model(s) locally on the Riva Server\n", + "- Send inference requests using the Riva Python API." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Pre-requisites" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To follow along, please make sure:\n", + "- You have access to NVIDIA NGC, and are able to download the Riva Quickstart [resources](https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart)\n", + "- Have an .riva model file that you wish to deploy. You can obtain this from `tlt export` (with `export_format=RIVA`). Please refer the tutorial on *Named entity recognition using Transfer Learning Toolkit* for more details on training and exporting an .riva model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Run Inference\n", + "Once the Riva server is up and running with your models, you can send inference requests querying the server. \n", + "\n", + "To send GRPC requests, you can install Riva Python API bindings for client. This is available as a pip .whl with the QuickStart.\n", + "\n", + "Otherwise, you can use the riva-client docker container which comes pre-installed with all the inference dependancies." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect to Riva server and run inference\n", + "Now we actually query the Riva server, let's get started. The following cell queries the riva server(using grpc) to yield a result." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile $RIVA_DIR/ner_client.py\n", + "\n", + "import grpc\n", + "import os\n", + "import argparse\n", + "import riva_api.riva_nlp_pb2 as rnlp\n", + "import riva_api.riva_nlp_pb2_grpc as rnlp_srv\n", + "\n", + "# use the NER network to return top-1 classes for entities\n", + "def postprocess_labels_server(tokens_response):\n", + " results = []\n", + " for i in range(0, len(tokens_response.results)):\n", + " slots = []\n", + " slot_scores = []\n", + " tokens = []\n", + " for j in range(0, len(tokens_response.results[i].results)):\n", + " entity = tokens_response.results[i].results[j]\n", + " tokens.append(entity.token)\n", + " slots.append(entity.label[0].class_name)\n", + " slot_scores.append(entity.label[0].score)\n", + " results.append((slots, tokens, slot_scores))\n", + "\n", + " return results\n", + "\n", + "def run_ner(grpc_server, query):\n", + " channel = grpc.insecure_channel(grpc_server)\n", + " riva_nlp = rnlp_srv.RivaLanguageUnderstandingStub(channel)\n", + " req = rnlp.AnalyzeEntitiesRequest()\n", + " req.query = query\n", + " resp = riva_nlp.AnalyzeEntities(req)\n", + " print(\"Query:\", query)\n", + " print(postprocess_labels_server(resp))\n", + "\n", + "def get_args():\n", + " parser = argparse.ArgumentParser(description=\"Client app to test named entity recognition on Riva\")\n", + " parser.add_argument(\"--server\", default=\"localhost:50051\", type=str, help=\"URI to GRPC server endpoint\")\n", + " parser.add_argument(\"--query\", default=\"NVIDIA is located at Santa Clara\", type=str, help=\"Input Query\")\n", + " return parser.parse_args()\n", + "\n", + "def run_ner_client():\n", + " args = get_args()\n", + " run_ner(args.server, query=args.query)\n", + "\n", + "if __name__ == '__main__':\n", + " run_ner_client()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you've installed the Riva Python API bindings for client using the pip whl, you can execute the above saved script with the following command:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#! python3 $RIVA_DIR/ner_client.py --query \"NVIDIA is located at Santa Clara\" --server localhost:50051" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Running Token Classification Inference using the Riva Python API directly" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also run Named Entity Recognition using the API directly without executing client side scripts.\n", + "An example is shown below" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# first import require libraries\n", + "import io\n", + "import librosa\n", + "from time import time\n", + "import numpy as np\n", + "import IPython.display as ipd\n", + "import grpc\n", + "import requests\n", + "\n", + "# NLP proto\n", + "##change this configuration to match your DGX number and your RIVA port number\n", + "##for example, 'dgx0180:50055'\n", + "\n", + "channel = grpc.insecure_channel('localhost:50051')\n", + "\n", + "import riva_api.riva_nlp_pb2 as rnlp\n", + "import riva_api.riva_nlp_pb2_grpc as rnlp_srv\n", + "\n", + "\n", + "riva_nlp = rnlp_srv.RivaLanguageUnderstandingStub(channel)\n", + "\n", + "\n", + "\n", + "# Use the TokenClassification API to run a Named Entity Recognition (NER) model\n", + "# Note: the model configuration of the NER model indicates that the labels are\n", + "# in IOB format. Riva, subsequently, knows to:\n", + "# a) ignore 'O' labels\n", + "# b) Remove B- and I- prefixes from labels\n", + "# c) Collapse sequences of B- I- ... I- tokens into a single token\n", + "\n", + "req = rnlp.TokenClassRequest()\n", + "req.model.model_name = \"riva_ner\" # If you have deployed a custom model with the domain_name \n", + " # parameter in ServiceMaker's `riva-build` command then you should use \n", + " # \"riva_ner_\" where \n", + " # is the name you provided to the domain_name parameter.\n", + "\n", + "req.text.append(\"Jensen Huang is the CEO of NVIDIA Corporation, \"\n", + " \"located in Santa Clara, California\")\n", + "resp = riva_nlp.ClassifyTokens(req)\n", + "\n", + "print(\"Named Entities:\")\n", + "for result in resp.results[0].results:\n", + " print(f\" {result.token} ({result.label[0].class_name})\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise :\n", + "\n", + "Use the Token classification API to identify Named Entities in the following sentences\n", + "Note: you may use the client python file to compare your result against the model you choose" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1: \"The weather in New York today is much hotter than in San Francisco.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2: \"The Sahara desert is located in Africa.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3: \"The Amazon rainforest is being affected by climate change.\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##first, import the required libraries\n", + "\n", + "import io\n", + "import librosa\n", + "from time import time\n", + "import numpy as np\n", + "import IPython.display as ipd\n", + "import grpc\n", + "import requests\n", + "\n", + "# NLP proto\n", + "\n", + "\n", + "## next, setup the connection parameters and variables\n", + "## next, initialize the query variables\n", + "\n", + "\n", + "\n", + "\n", + "# Use the TokenClassification API to run a Named Entity Recognition (NER) model\n", + "# Note: the model configuration of the NER model indicates that the labels are\n", + "# in IOB format. Jarvis, subsequently, knows to:\n", + "# a) ignore 'O' labels\n", + "# b) Remove B- and I- prefixes from labels\n", + "# c) Collapse sequences of B- I- ... I- tokens into a single token\n", + "\n", + "## initialize the request object\n", + "\n", + "\n", + "\n", + "##setup the query\n", + "\n", + "\n", + "##make the request\n", + "\n", + "\n", + "\n", + "\n", + "##parse the response variable and print out the entities found \n", + "\n", + "\n", + "## compare results here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/NER/token-classification-deployment.ipynb b/ai/RIVA/English/Python/jupyter_notebook/NER/token-classification-deployment.ipynb new file mode 100644 index 000000000..c209af7b8 --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/NER/token-classification-deployment.ipynb @@ -0,0 +1,282 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[Home Page](../START_HERE_RIVA_BOOTCAMP.ipynb)\n", + "\n", + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[1](token-classification-training.ipynb)\n", + "[2]\n", + "[3](token-classification-Exercise.ipynb)\n", + "     \n", + "     \n", + "     \n", + "     \n", + "[Next Notebook](token-classification-Exercise.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deploying a Named Entity Recognition Model in Riva" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Transfer Learning Toolkit](https://developer.nvidia.com/transfer-learning-toolkit) (TLT) provides the capability to export your model in a format that can deployed using [NVIDIA Riva](https://developer.nvidia.com/riva), a highly performant application framework for multi-modal conversational AI services using GPUs. \n", + "\n", + "This tutorial explores taking an .riva model, the result of `tlt token_classification` command, and leveraging the Riva ServiceMaker framework to aggregate all the necessary artifacts for Riva deployment to a target environment. Once the model is deployed in Riva, you can issue inference requests to the server. We will demonstrate how quick and straightforward this whole process is. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Learning Objectives\n", + "In this notebook, you will learn how to: \n", + "- Use Riva ServiceMaker to take a TLT exported .riva and convert it to .rmir\n", + "- Deploy the model(s) locally on the Riva Server\n", + "- Send inference requests from a demo client using Riva API bindings." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Pre-requisites" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To follow along, please make sure:\n", + "- You have access to NVIDIA NGC, and are able to download the Riva Quickstart [resources](https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart)\n", + "- Have an .riva model file that you wish to deploy. You can obtain this from `tlt export` (with `export_format=RIVA`). Please refer the tutorial on *Named entity recognition using Transfer Learning Toolkit* for more details on training and exporting an .riva model.\n", + "- You have followed the steps in the setup notebook and have a running instance of the RIVA server ready" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Riva-deploy\n", + "\n", + "The deployment tool takes as input one or more Riva Model Intermediate Representation (RMIR) files and a target model repository directory. It creates an ensemble configuration specifying the pipeline for the execution and finally writes all those assets to the output model repository directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Syntax: riva-deploy -f dir-for-rmir/model.rmir:key output-dir-for-repository\n", + "! docker run --rm --gpus 0 -v $MODEL_LOC:/data $RIVA_SM_CONTAINER -- \\\n", + " riva-deploy -f /data/token-classification.rmir:$KEY /data/models/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Run Inference\n", + "Once the Riva server is up and running with your models, you can send inference requests querying the server. \n", + "\n", + "To send GRPC requests, you can install Riva Python API bindings for client. This is available as a pip .whl with the QuickStart.\n", + "\n", + "Otherwise, you can use the riva-client docker container which comes pre-installed with all the inference dependancies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Install client API bindings\n", + "! cd $RIVA_DIR && pip install " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect to Riva server and run inference\n", + "Now we actually query the Riva server, let's get started. The following cell queries the riva server(using grpc) to yield a result." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile $RIVA_DIR/ner_client.py\n", + "\n", + "import grpc\n", + "import os\n", + "import argparse\n", + "import riva_api.riva_nlp_pb2 as rnlp\n", + "import riva_api.riva_nlp_pb2_grpc as rnlp_srv\n", + "\n", + "# use the NER network to return top-1 classes for entities\n", + "def postprocess_labels_server(tokens_response):\n", + " results = []\n", + " for i in range(0, len(tokens_response.results)):\n", + " slots = []\n", + " slot_scores = []\n", + " tokens = []\n", + " for j in range(0, len(tokens_response.results[i].results)):\n", + " entity = tokens_response.results[i].results[j]\n", + " tokens.append(entity.token)\n", + " slots.append(entity.label[0].class_name)\n", + " slot_scores.append(entity.label[0].score)\n", + " results.append((slots, tokens, slot_scores))\n", + "\n", + " return results\n", + "\n", + "def run_ner(grpc_server, query):\n", + " channel = grpc.insecure_channel(grpc_server)\n", + " riva_nlp = rnlp_srv.RivaLanguageUnderstandingStub(channel)\n", + " req = rnlp.AnalyzeEntitiesRequest()\n", + " req.query = query\n", + " resp = riva_nlp.AnalyzeEntities(req)\n", + " print(\"Query:\", query)\n", + " print(postprocess_labels_server(resp))\n", + "\n", + "def get_args():\n", + " parser = argparse.ArgumentParser(description=\"Client app to test named entity recognition on Riva\")\n", + " ##change this configuration to match your server deployment parameters\n", + " parser.add_argument(\"--server\", default=\"localhost:50051\", type=str, help=\"URI to GRPC server endpoint\")\n", + " parser.add_argument(\"--query\", default=\"NVIDIA is located at Santa Clara\", type=str, help=\"Input Query\")\n", + " return parser.parse_args()\n", + "\n", + "def run_ner_client():\n", + " args = get_args()\n", + " run_ner(args.server, query=args.query)\n", + "\n", + "if __name__ == '__main__':\n", + " run_ner_client()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you've installed the Riva Python API bindings for client using the pip whl, you can execute the above saved script with the following command:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#! python3 $RIVA_DIR/ner_client.py --query \"NVIDIA is located at Santa Clara\" --server localhost:50051" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Otherwise, to use the Riva client docker container, you can execute the below docker run command with the right `RIVA_CLIENT_CONTAINER` name." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "RIVA_CLIENT_CONTAINER = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!docker run -d --privileged \\\n", + " -v $RIVA_DIR:/riva \\\n", + " --net=host --rm \\\n", + " --name riva-client \\\n", + " $RIVA_CLIENT_CONTAINER sleep 1h" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, executing the client python script in the above client container" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!docker exec riva-client python3 /riva/ner_client.py --query \"NVIDIA is located at Santa Clara\" --server localhost:50051" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make sure to close the riva-client docker container before shutting down the jupyter kernel. In addition, do close the riva services container with the `riva_stop.sh` script in the quickstart" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! docker stop riva-client\n", + "! cd $RIVA_DIR && ./riva_stop.sh config.sh" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/NER/token-classification-exercise.ipynb b/ai/RIVA/English/Python/jupyter_notebook/NER/token-classification-exercise.ipynb new file mode 100644 index 000000000..294c0391c --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/NER/token-classification-exercise.ipynb @@ -0,0 +1,346 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Previous Notebook](token-classification-deployment.ipynb)\n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[Home Page](../START_HERE_RIVA_BOOTCAMP.ipynb)\n", + "\n", + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[1](token-classification-training.ipynb)\n", + "[2](token-classification-deployment.ipynb)\n", + "[3]\n", + "     \n", + "     \n", + "     \n", + "     " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Testing a Named Entity Recognition Model in Riva - Exercise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "This notebook explores taking an .riva model, the result of `tlt token_classification` command, and leveraging the Riva ServiceMaker framework to aggregate all the necessary artifacts for Riva deployment to a target environment. Once the model is deployed in Riva, you can issue inference requests to the server. . " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Learning Objectives\n", + "In this notebook, you will learn how to: \n", + "- Deploy the model(s) locally on the Riva Server\n", + "- Send inference requests using the Riva Python API." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Pre-requisites" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To follow along, please make sure:\n", + "- You have access to NVIDIA NGC, and are able to download the Riva Quickstart [resources](https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart)\n", + "- Have an .riva model file that you wish to deploy. You can obtain this from `tlt export` (with `export_format=RIVA`). Please refer the tutorial on *Named entity recognition using Transfer Learning Toolkit* for more details on training and exporting an .riva model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Run Inference\n", + "Once the Riva server is up and running with your models, you can send inference requests querying the server. \n", + "\n", + "To send GRPC requests, you can install Riva Python API bindings for client. This is available as a pip .whl with the QuickStart.\n", + "\n", + "Otherwise, you can use the riva-client docker container which comes pre-installed with all the inference dependancies." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect to Riva server and run inference\n", + "Now we actually query the Riva server, let's get started. The following cell queries the riva server(using grpc) to yield a result." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile $RIVA_DIR/ner_client.py\n", + "\n", + "import grpc\n", + "import os\n", + "import argparse\n", + "import riva_api.riva_nlp_pb2 as rnlp\n", + "import riva_api.riva_nlp_pb2_grpc as rnlp_srv\n", + "\n", + "# use the NER network to return top-1 classes for entities\n", + "def postprocess_labels_server(tokens_response):\n", + " results = []\n", + " for i in range(0, len(tokens_response.results)):\n", + " slots = []\n", + " slot_scores = []\n", + " tokens = []\n", + " for j in range(0, len(tokens_response.results[i].results)):\n", + " entity = tokens_response.results[i].results[j]\n", + " tokens.append(entity.token)\n", + " slots.append(entity.label[0].class_name)\n", + " slot_scores.append(entity.label[0].score)\n", + " results.append((slots, tokens, slot_scores))\n", + "\n", + " return results\n", + "\n", + "def run_ner(grpc_server, query):\n", + " channel = grpc.insecure_channel(grpc_server)\n", + " riva_nlp = rnlp_srv.RivaLanguageUnderstandingStub(channel)\n", + " req = rnlp.AnalyzeEntitiesRequest()\n", + " req.query = query\n", + " resp = riva_nlp.AnalyzeEntities(req)\n", + " print(\"Query:\", query)\n", + " print(postprocess_labels_server(resp))\n", + "\n", + "def get_args():\n", + " parser = argparse.ArgumentParser(description=\"Client app to test named entity recognition on Riva\")\n", + " parser.add_argument(\"--server\", default=\"localhost:50051\", type=str, help=\"URI to GRPC server endpoint\")\n", + " parser.add_argument(\"--query\", default=\"NVIDIA is located at Santa Clara\", type=str, help=\"Input Query\")\n", + " return parser.parse_args()\n", + "\n", + "def run_ner_client():\n", + " args = get_args()\n", + " run_ner(args.server, query=args.query)\n", + "\n", + "if __name__ == '__main__':\n", + " run_ner_client()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you've installed the Riva Python API bindings for client using the pip whl, you can execute the above saved script with the following command:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#! python3 $RIVA_DIR/ner_client.py --query \"NVIDIA is located at Santa Clara\" --server localhost:50051" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Running Token Classification Inference using the Riva Python API directly" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also run Named Entity Recognition using the API directly without executing client side scripts.\n", + "An example is shown below" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# first import require libraries\n", + "import io\n", + "import librosa\n", + "from time import time\n", + "import numpy as np\n", + "import IPython.display as ipd\n", + "import grpc\n", + "import requests\n", + "\n", + "# NLP proto\n", + "##change this configuration to match your DGX number and your RIVA port number\n", + "##for example, 'dgx0180:50055'\n", + "\n", + "channel = grpc.insecure_channel('localhost:50051')\n", + "\n", + "import riva_api.riva_nlp_pb2 as rnlp\n", + "import riva_api.riva_nlp_pb2_grpc as rnlp_srv\n", + "\n", + "\n", + "riva_nlp = rnlp_srv.RivaLanguageUnderstandingStub(channel)\n", + "\n", + "\n", + "\n", + "# Use the TokenClassification API to run a Named Entity Recognition (NER) model\n", + "# Note: the model configuration of the NER model indicates that the labels are\n", + "# in IOB format. Riva, subsequently, knows to:\n", + "# a) ignore 'O' labels\n", + "# b) Remove B- and I- prefixes from labels\n", + "# c) Collapse sequences of B- I- ... I- tokens into a single token\n", + "\n", + "req = rnlp.TokenClassRequest()\n", + "req.model.model_name = \"riva_ner\" # If you have deployed a custom model with the domain_name \n", + " # parameter in ServiceMaker's `riva-build` command then you should use \n", + " # \"riva_ner_\" where \n", + " # is the name you provided to the domain_name parameter.\n", + "\n", + "req.text.append(\"Jensen Huang is the CEO of NVIDIA Corporation, \"\n", + " \"located in Santa Clara, California\")\n", + "resp = riva_nlp.ClassifyTokens(req)\n", + "\n", + "print(\"Named Entities:\")\n", + "for result in resp.results[0].results:\n", + " print(f\" {result.token} ({result.label[0].class_name})\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise :\n", + "\n", + "Use the Token classification API to identify Named Entities in the following sentences\n", + "Note: you may use the client python file to compare your result against the model you choose" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1: \"The weather in New York today is much hotter than in San Francisco.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2: \"The Sahara desert is located in Africa.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3: \"The Amazon rainforest is being affected by climate change.\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##first, import the required libraries\n", + "\n", + "import io\n", + "import librosa\n", + "from time import time\n", + "import numpy as np\n", + "import IPython.display as ipd\n", + "import grpc\n", + "import requests\n", + "\n", + "# NLP proto\n", + "\n", + "\n", + "## next, setup the connection parameters and variables\n", + "## next, initialize the query variables\n", + "\n", + "\n", + "\n", + "\n", + "# Use the TokenClassification API to run a Named Entity Recognition (NER) model\n", + "# Note: the model configuration of the NER model indicates that the labels are\n", + "# in IOB format. Jarvis, subsequently, knows to:\n", + "# a) ignore 'O' labels\n", + "# b) Remove B- and I- prefixes from labels\n", + "# c) Collapse sequences of B- I- ... I- tokens into a single token\n", + "\n", + "## initialize the request object\n", + "\n", + "\n", + "\n", + "##setup the query\n", + "\n", + "\n", + "##make the request\n", + "\n", + "\n", + "\n", + "\n", + "##parse the response variable and print out the entities found \n", + "\n", + "\n", + "## compare results here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/NER/token-classification-training.ipynb b/ai/RIVA/English/Python/jupyter_notebook/NER/token-classification-training.ipynb new file mode 100644 index 000000000..47d03b546 --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/NER/token-classification-training.ipynb @@ -0,0 +1,693 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[Home Page](../START_HERE_RIVA_BOOTCAMP.ipynb)\n", + "\n", + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[1]\n", + "[2](token-classification-deployment.ipynb)\n", + "[3](token-classification-Exercise.ipynb)\n", + "     \n", + "     \n", + "     \n", + "     \n", + "[Next Notebook](token-classification-deployment.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TLT - Named Entity Recognition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Transfer Learning Toolkit (TLT) is a python based AI toolkit for taking purpose-built pre-trained AI models and customizing them with your own data. \n", + "\n", + "Transfer learning extracts learned features from an existing neural network to a new one. Transfer learning is often used when creating a large training dataset is not feasible. \n", + "\n", + "Developers, researchers and software partners building intelligent vision AI apps and services, can bring their own data to fine-tune pre-trained models instead of going through the hassle of training from scratch." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Transfer Learning Toolkit](https://developer.nvidia.com/sites/default/files/akamai/embedded-transfer-learning-toolkit-software-stack-1200x670px.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The goal of this toolkit is to reduce that 80 hour workload to an 8 hour workload, which can enable data scientist to have considerably more train-test iterations in the same time frame.\n", + "\n", + "Let's see this in action with a use case for Named Entity Recognition." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Named Entity Recognition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Named entity recognition (NER), also referred to as entity chunking, identification or extraction, is the task of detecting and classifying key information (entities) in text. \n", + "\n", + "For example, in a sentence: Mary lives in Santa Clara and works at NVIDIA, we should detect that **Mary** is a person, **Santa Clara** is a location and **NVIDIA** is a company." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Named Entity Recognition using TLT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Dataset Details" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial we going to use [GMB(Groningen Meaning Bank)](http://www.let.rug.nl/bjerva/gmb/about.php) corpus for entity recognition.\n", + "\n", + "GMB is a fairly large corpus with a lot of annotations. Note, that GMB is not completely human annotated and it’s not considered 100% correct.\n", + "\n", + "The data is labeled using the [IOB format](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging) (short for inside, outside, beginning). \n", + "\n", + "The following classes appear in the dataset:\n", + "\n", + "* LOC = Geographical Entity\n", + "* ORG = Organization\n", + "* PER = Person\n", + "* GPE = Geopolitical Entity\n", + "* TIME = Time indicator\n", + "* ART = Artifact\n", + "* EVE = Event\n", + "* NAT = Natural Phenomenon\n", + "\n", + "For this tutorial, classes ART, EVE, and NAT were combined into a MISC class due to small number of examples for these classes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# IMPORTANT NOTE: Set path to a folder where you want you data to be saved.\n", + "DATA_DOWNLOAD_DIR = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! wget https://dldata-public.s3.us-east-2.amazonaws.com/gmb_v_2.2.0_clean.zip" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! unzip gmb_v_2.2.0_clean.zip -d $DATA_DOWNLOAD_DIR" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the data folder should contain 5 files:\n", + "\n", + "- labels_dev.txt\n", + "- labels_train.txt\n", + "- text_dev.txt\n", + "- text_train.txt\n", + "- label_ids.csv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! ls -l $DATA_DOWNLOAD_DIR/gmb_v_2.2.0_clean" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# let's take a look at the data \n", + "print('Train text:')\n", + "! head -n 5 {DATA_DOWNLOAD_DIR}/gmb_v_2.2.0_clean/text_train.txt\n", + "\n", + "print('\\nTrain label:')\n", + "! head -n 5 {DATA_DOWNLOAD_DIR}/gmb_v_2.2.0_clean/labels_train.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Set Relevant Paths" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once tlt is installed, the next step is to setup the mounts for TLT.
\n", + "\n", + "The file `~/.tlt_mounts.json` takes care of the mounts inside docker container and also for additional arguments to be passed to docker run command. This file is stored in the users home directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!ls -la ~/.tlt_mounts.json" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's overwrite `~/.tlt_mounts.json` with the mounts needed. Please change the paths to \"source\" key below.\n", + "After installing tlt, the next step is to setup the mounts for TLT. The TLT launcher uses docker containers under the hood, and **for our data and results directory to be visible to the docker, they need to be mapped**. The launcher can be configured using the config file `~/.tlt_mounts.json`. Apart from the mounts, you can also configure additional options like the Environment Variables and amount of Shared Memory available to the TLT launcher.
\n", + "\n", + "`IMPORTANT NOTE:` The code below creates a sample `~/.tlt_mounts.json` file. Here, we can map directories in which we save the data, specs, results and cache. You should configure it for your specific case such your these directories are correctly visible to the docker container. **Please also ensure that the source directories exist on your machine!**\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile ~/.tlt_mounts.json\n", + "{\n", + " \"Mounts\":[\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/data\"\n", + " },\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/specs\"\n", + " },\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/results\"\n", + " },\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/root/.cache\"\n", + " }\n", + " ],\n", + " \"DockerOptions\": {\n", + " \"shm_size\": \"16G\",\n", + " \"ulimits\": {\n", + " \"memlock\": -1,\n", + " \"stack\": 67108864\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The \"source\" and \"destination\" are mounts for the source and destination folders to access the pre-processed and processed dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make sure the source directories exist, if not, create them\n", + "! mkdir \n", + "! mkdir \n", + "! mkdir " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NOTE: The following paths are set from the perspective of the TLT Docker.\n", + "\n", + "# The data is saved here\n", + "DATA_DIR = \"/data\"\n", + "SPECS_DIR = \"/specs\"\n", + "RESULTS_DIR = \"/results\"\n", + "\n", + "# Set your encryption key, and use the same key for all commands\n", + "KEY = 'tlt_encode'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can check the docker image versions and the tasks that tlt can perform. You can also check this out with `tlt info --verbose`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! tlt info --verbose" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that everything is setup, we would like to take a bit of time to explain the tlt interface for ease of use. The command structure can be broken down as follows: `tlt `
\n", + "\n", + "Let's see this in further detail." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Downloading Specs\n", + "TLT's Conversational AI Toolkit works off of spec files which make it easy to edit hyperparameters on the fly. We can proceed to downloading the spec files. The user may choose to modify/rewrite these specs, or even individually override them through the launcher. You can download the default spec files by using the `download_specs` command.
\n", + "\n", + "The -o argument indicating the folder where the default specification files will be downloaded, and -r that instructs the script where to save the logs. **Make sure the -o points to an empty folder!**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! tlt token_classification download_specs \\\n", + " -r $RESULTS_DIR/token_classification \\\n", + " -o $SPECS_DIR/token_classification" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Data pre-processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[TokenClassification Model in NeMo](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/models/token_classification/token_classification_model.py) supports NER and other token level classification tasks, as long as the data follows the format specified below. \n", + "\n", + "Token Classification Model requires the data to be split into 2 files: \n", + "* text.txt and \n", + "* labels.txt. \n", + "\n", + "Each line of the **text.txt** file contains text sequences, where words are separated with spaces, i.e.: \n", + "[WORD] [SPACE] [WORD] [SPACE] [WORD].\n", + "\n", + "The **labels.txt** file contains corresponding labels for each word in text.txt, the labels are separated with spaces, i.e.:\n", + "[LABEL] [SPACE] [LABEL] [SPACE] [LABEL].\n", + "\n", + "Example of a text.txt file:\n", + "```\n", + "Jennifer is from New York City .\n", + "She likes ...\n", + "...\n", + "```\n", + "Corresponding labels.txt file:\n", + "```\n", + "B-PER O O B-LOC I-LOC I-LOC O\n", + "O O ...\n", + "...\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To convert an IOB format data to the format required for training, we can use the `dataset_convert` command in TLT on your train and dev files\n", + "\n", + "For this tutorial, we are using the preprocessed GMB dataset, thus we won't be required to convert the dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Train the NER model from scratch using pre-trained BERT base uncased model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our Named Entity Recognition model is comprised of the pretrained BERT model followed by a Token Classification layer.\n", + "\n", + "The model is defined in a config file which are already available for you to use directly or as reference to create your own. \n", + "\n", + "Through these spec files, the user can tune many knobs like the model, dataset, hyperparameters, optimizer etc. Each command (like train, finetune, evaluate etc.) should have a dedicated spec file. These sample spec files are available at `$SPECS_DIR/token_classification`\n", + "\n", + "The important sections to look out for are:\n", + "\n", + "- model: All arguments that are related to the model - language model, token classifier, optimizer and schedulers, datasets and any other related information\n", + "- trainer: Any argument to be passed to PyTorch Lightning\n", + "\n", + "The model config file for token classification is available at [Github](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/token_classification/conf/token_classification_config.yaml). A similar file (at the above location inside TLT docker image) is used for TLT.\n", + "\n", + "Among other things, the config file contains dictionaries called **dataset**, **train_ds** and **validation_ds**. These are configurations used to setup the Dataset and DataLoaders of the corresponding config.\n", + "\n", + "We assume that both training and evaluation files are located in the same directory, and use the default names mentioned during the data download step. So, to start model training, we simply need to specify `model.dataset.data_dir`, like we are going to do below.\n", + "\n", + "Also notice that some config lines, including `model.dataset.data_dir` have ??? in place of paths, which means that the values for these fields are required to be specified by the user.\n", + "\n", + "Thus, we must provide the following parameters to TLT:\n", + "1. **data_dir** - path to the processed data\n", + "2. **model.label_ids** - mapping of labels with their numerical ids\n", + "\n", + "We can pass these parameters while using the TLT train command. In addition to these, we can also change other parameters like max_epochs.\n", + "\n", + "A similar version of model config file we saw above is also passed to the TLT train command." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt token_classification train \\\n", + " -e $SPECS_DIR/token_classification/train.yaml \\\n", + " -g 1 \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/token_classification/train \\\n", + " data_dir=$DATA_DIR/gmb_v_2.2.0_clean \\\n", + " training_ds.num_samples=-1 \\\n", + " validation_ds.num_samples=-1 \\\n", + " model.label_ids=$DATA_DIR/gmb_v_2.2.0_clean/label_ids.csv \\\n", + " trainer.max_epochs=1 \\\n", + " model.language_model.pretrained_model_name=bert-base-uncased" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Finetune NER model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we were training from scratch, the datasets were prepared for training during the model initialization. When we are using a pretrained NER model, before training, we need to setup training and evaluation data, and also provide path to the pre-trained model.tlt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For simplicity of this tutorial, we will be using the `trained-model.tlt` that was saved during the last section (Train the NER model from scratch using pre-trained BERT Base uncased model) and finetune it again on the GMB dataset.\n", + "\n", + "Note: If you wish to proceed with a trained dataset for better inference results, you can find a .nemo model [here](\n", + "https://ngc.nvidia.com/catalog/collections/nvidia:nemotrainingframework).\n", + "\n", + "Simply re-name the .nemo file to .tlt and pass it through the finetune pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt token_classification finetune \\\n", + " -e $SPECS_DIR/token_classification/finetune.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/token_classification/train/checkpoints/trained-model.tlt \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/token_classification/finetune-bert-base \\\n", + " data_dir=$DATA_DIR/gmb_v_2.2.0_clean \\\n", + " trainer.max_epochs=1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This command will generate a fine-tuned model `finetuned-model.tlt` at $RESULTS_DIR/finetune-bert-base/checkpoints" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Evaluate NER on the Validation Set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To see how the model performs, we can generate prediction the same way we did it earlier or we can use our model to generate predictions for a dataset from a file, for example, to perform final evaluation or to do error analysis. Below, we are using a subset of dev set, but it could be any text file as long as it follows the data format described above. \n", + "\n", + "`labels_file` is optional here, and if provided will be used to get metrics." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the below command, TLT will evaluate on `text_dev.txt` present in the data_dir, using `trained-model.tlt` model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt token_classification evaluate \\\n", + " -e $SPECS_DIR/token_classification/evaluate.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/token_classification/finetune-bert-base/checkpoints/finetuned-model.tlt \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/token_classification/evaluate \\\n", + " data_dir=$DATA_DIR/gmb_v_2.2.0_clean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Export model to ONNX format for deployment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the model is trained up-to satisfactory metrics, we can export it in ONNX format to be deployed with any inferencing solution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt token_classification export \\\n", + " -e $SPECS_DIR/token_classification/export.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/token_classification/finetune-bert-base/checkpoints/finetuned-model.tlt \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/token_classification/export \\\n", + " export_format=ONNX" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This command exports the model as `exported-model.eonnx` which is essentially an archive containing the .onnx model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you're specifically interested to deploy this model using NVIDIA Riva, please set `export_format=JARVIS`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt token_classification export \\\n", + " -e $SPECS_DIR/token_classification/export.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/token_classification/finetune-bert-base/checkpoints/finetuned-model.tlt \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/token_classification/riva \\\n", + " export_format=JARVIS" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model is exported as `exported-model.riva` which is in a format suited for deployment in Riva." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Infer on a custom input sentence" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get the model's predictions on a given sentence of your choice, you can save those sentences in infer.yaml and use TLT infer command." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The infer.yaml contains:\n", + "\n", + "```\n", + "# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.\n", + "# TLT Spec file for inference using the previously pretrained Token Classification model.\n", + "\n", + "# \"Simulate\" user input: batch with two samples.\n", + "input_batch:\n", + " - 'We bought four shirts from the Nvidia gear store in Santa Clara.'\n", + " - 'Nvidia is a company.'\n", + "\n", + "```\n", + "Please edit `input_batch` with your own sentences to measure the output of this NER model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt token_classification infer \\\n", + " -e $SPECS_DIR/token_classification/infer.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/token_classification/finetune-bert-base/checkpoints/finetuned-model.tlt \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/token_classification/infer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### What's Next?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You could use TLT to build custom models for your own applications, or you could deploy the custom model to Nvidia Riva!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/PC/punctuation-and-capitalization-Exercise.ipynb b/ai/RIVA/English/Python/jupyter_notebook/PC/punctuation-and-capitalization-Exercise.ipynb new file mode 100644 index 000000000..d2d4a4669 --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/PC/punctuation-and-capitalization-Exercise.ipynb @@ -0,0 +1,288 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "demanding-antibody", + "metadata": {}, + "source": [ + "# Punctuation and Capitalization Model in RIVA - Exercise" + ] + }, + { + "cell_type": "markdown", + "id": "federal-quarterly", + "metadata": {}, + "source": [ + "[Transfer Learning Toolkit (TLT)](https://developer.nvidia.com/transfer-learning-toolkit) provides the capability to export your model in a format that can deployed using NVIDIA [Riva](https://developer.nvidia.com/riva), a highly performant application framework for multi-modal conversational AI services using GPUs.\n", + "\n", + "This tutorial explores taking an .riva model, the result of `tlt punctuation_and_capitalization export` command, and leveraging the Riva ServiceMaker framework to aggregate all the necessary artifacts for Riva deployment to a target environment. Once the model is deployed in Riva, you can issue inference requests to the server. We will demonstrate how quick and straightforward this whole process is." + ] + }, + { + "cell_type": "markdown", + "id": "involved-rider", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "\n", + "In this notebook, you will learn how to: \n", + "- Use Riva ServiceMaker to take a TLT exported .riva and convert it to .rmir\n", + "- Deploy the model(s) locally on the Riva Server\n", + "- Send inference requests from a demo client using Riva API bindings." + ] + }, + { + "cell_type": "markdown", + "id": "unusual-trance", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "Before going through the jupyter notebook, please make sure:\n", + "- You have access to NVIDIA NGC, and are able to download the Riva Quickstart [resources](https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart)\n", + "- Have an .riva model file that you wish to deploy. You can obtain this from ``tlt export`` (with ``export_format=RIVA``). \n", + "- You have followed the instruction in the setup notebook to setup, deploy and run the Riva Service\n", + "\n", + "NOTE: Please refer to the tutorial on *Punctuation And Capitalization using Transfer Learning Toolkit* for more details on training and exporting an .riva model for punctuation and capitalization task." + ] + }, + { + "cell_type": "markdown", + "id": "perfect-kuwait", + "metadata": {}, + "source": [ + "### Riva-deploy\n", + "\n", + "The deployment tool takes as input one or more Riva Model Intermediate Representation (RMIR) files and a target model repository directory. It creates an ensemble configuration specifying the pipeline for the execution and finally writes all those assets to the output model repository directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aware-credits", + "metadata": {}, + "outputs": [], + "source": [ + "# Syntax: riva-deploy -f dir-for-rmir/model.rmir:key output-dir-for-repository\n", + "!docker run --rm --gpus all -v $MODEL_LOC:/data $RIVA_SM_CONTAINER -- \\\n", + " riva-deploy -f /data/punct-capit.rmir:$KEY /data/models" + ] + }, + { + "cell_type": "markdown", + "id": "valued-chapel", + "metadata": {}, + "source": [ + "## Start Riva Server\n", + "\n", + "Once the model repository is generated, we are ready to start the Riva server. From this step you need to have a running Riva server, which can be done by using the steps shown in the setup notebook. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "considered-voluntary", + "metadata": {}, + "outputs": [], + "source": [ + "### Set the path to Riva directory\n", + "RIVA_DIR = " + ] + }, + { + "cell_type": "markdown", + "id": "young-table", + "metadata": {}, + "source": [ + "Note: you can modify ``config.sh`` (as shown in the setup notebook) to enable relevant Riva services (nlp for Punctuation & Capitalization model), provide the encryption key, and path to the model repository (``riva_model_loc``) generated in the previous step among other configurations.\n", + "\n", + "Pretrained versions of models specified in models_asr/nlp/tts are fetched from NGC. Since we are using our custom model, we can comment it in models_nlp (and any others that are not relevant to our use case). " + ] + }, + { + "cell_type": "markdown", + "id": "chief-greensboro", + "metadata": {}, + "source": [ + "## Run Inference\n", + "Once the Riva server is up and running with your models, you can send inference requests querying the server. \n", + "\n", + "To send GRPC requests, you can install Riva Python API bindings for client. This is available as a pip .whl with the QuickStart." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "inappropriate-tomato", + "metadata": {}, + "outputs": [], + "source": [ + "# IMPORTANT: Set the name of the whl file\n", + "RIVA_API_WHL = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "pediatric-junior", + "metadata": {}, + "outputs": [], + "source": [ + "# Install client API bindings\n", + "!cd $RIVA_DIR && pip install $RIVA_API_WHL" + ] + }, + { + "cell_type": "markdown", + "id": "temporal-sense", + "metadata": {}, + "source": [ + "Run the following sample code from within the client docker container:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "decreased-excitement", + "metadata": {}, + "outputs": [], + "source": [ + "import grpc\n", + "import argparse\n", + "import os\n", + "import riva_api.riva_nlp_pb2 as rnlp\n", + "import riva_api.riva_nlp_pb2_grpc as rnlp_srv\n", + "\n", + "class BertPunctuatorClient(object):\n", + " def __init__(self, grpc_server, model_name=\"riva_punctuation\"):\n", + " # generate the correct model based on precision and whether or not ensemble is used\n", + " print(\"Using model: {}\".format(model_name))\n", + " self.model_name = model_name\n", + " self.channel = grpc.insecure_channel(grpc_server)\n", + " self.riva_nlp = rnlp_srv.RivaLanguageUnderstandingStub(self.channel)\n", + "\n", + " self.has_bos = True\n", + " self.has_eos = False\n", + "\n", + " def run(self, input_strings):\n", + " if isinstance(input_strings, str):\n", + " # user probably passed a single string instead of a list/iterable\n", + " input_strings = [input_strings]\n", + "\n", + " request = rnlp.TextTransformRequest()\n", + " request.model.model_name = self.model_name\n", + " for q in input_strings:\n", + " request.text.append(q)\n", + " response = self.riva_nlp.TransformText(request)\n", + "\n", + " return response.text[0]\n", + "\n", + "def run_punct_capit(server,model,query):\n", + " print(\"Client app to test punctuation and capitalization on Riva\")\n", + " client = BertPunctuatorClient(server, model_name=model)\n", + " result = client.run(query)\n", + " print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "labeled-western", + "metadata": {}, + "outputs": [], + "source": [ + "run_punct_capit(server=\"localhost:50051\",\n", + " model=\"riva_punctuation\",\n", + " query=\"how are you doing\")" + ] + }, + { + "cell_type": "markdown", + "id": "lined-roberts", + "metadata": {}, + "source": [ + "You can stop all docker container before shutting down the jupyter kernel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "hybrid-album", + "metadata": {}, + "outputs": [], + "source": [ + "!docker stop $(docker ps -a -q)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "03d4bb1b", + "metadata": {}, + "outputs": [], + "source": [ + "## The following shows how to run the punctuation service using the Python client API\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b9ff33a0", + "metadata": {}, + "outputs": [], + "source": [ + "# Use the TextTransform API to run the punctuation model\n", + "req = rnlp.TextTransformRequest()\n", + "req.model.model_name = \"riva_punctuation\"\n", + "req.text.append(\"add punctuation to this sentence\")\n", + "req.text.append(\"do you have any red nvidia shirts\")\n", + "req.text.append(\"i need one cpu four gpus and lots of memory \"\n", + " \"for my new computer it's going to be very cool\")\n", + "\n", + "nlp_resp = riva_nlp.TransformText(req)\n", + "print(\"TransformText Output:\")\n", + "print(\"\\n\".join([f\" {x}\" for x in nlp_resp.text]))" + ] + }, + { + "cell_type": "markdown", + "id": "f679d61c", + "metadata": {}, + "source": [ + "### Exercise\n", + "\n", + "Use the punctuation service to modify the following paragraph:\n", + "\n", + "winston is one of the most laid-back people i know he is tall and slim with black hair and he always wears a t-shirt and black jeans his jeans have holes in them and his baseball boots are scruffy too he usually sits at the back of the class and he often seems to be asleep however when the exam results are given out he always gets an \"A\" i don't think hes as lazy as he appears to be" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "346836bb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/PC/punctuation-and-capitalization-deployment.ipynb b/ai/RIVA/English/Python/jupyter_notebook/PC/punctuation-and-capitalization-deployment.ipynb new file mode 100644 index 000000000..10117c0a0 --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/PC/punctuation-and-capitalization-deployment.ipynb @@ -0,0 +1,238 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "demanding-antibody", + "metadata": {}, + "source": [ + "# Deploying Punctuation and Capitalization Model in RIVA" + ] + }, + { + "cell_type": "markdown", + "id": "federal-quarterly", + "metadata": {}, + "source": [ + "[Transfer Learning Toolkit (TLT)](https://developer.nvidia.com/transfer-learning-toolkit) provides the capability to export your model in a format that can deployed using NVIDIA [Riva](https://developer.nvidia.com/riva), a highly performant application framework for multi-modal conversational AI services using GPUs.\n", + "\n", + "This tutorial explores taking an .riva model, the result of `tlt punctuation_and_capitalization export` command, and leveraging the Riva ServiceMaker framework to aggregate all the necessary artifacts for Riva deployment to a target environment. Once the model is deployed in Riva, you can issue inference requests to the server. We will demonstrate how quick and straightforward this whole process is." + ] + }, + { + "cell_type": "markdown", + "id": "involved-rider", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "\n", + "In this notebook, you will learn how to: \n", + "- Use Riva ServiceMaker to take a TLT exported .riva and convert it to .rmir\n", + "- Deploy the model(s) locally on the Riva Server\n", + "- Send inference requests from a demo client using Riva API bindings." + ] + }, + { + "cell_type": "markdown", + "id": "unusual-trance", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "Before going through the jupyter notebook, please make sure:\n", + "- You have access to NVIDIA NGC, and are able to download the Riva Quickstart [resources](https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart)\n", + "- Have an .riva model file that you wish to deploy. You can obtain this from ``tlt export`` (with ``export_format=RIVA``). \n", + "- You have followed the instruction in the setup notebook to setup, deploy and run the Riva Service\n", + "\n", + "NOTE: Please refer to the tutorial on *Punctuation And Capitalization using Transfer Learning Toolkit* for more details on training and exporting an .riva model for punctuation and capitalization task." + ] + }, + { + "cell_type": "markdown", + "id": "perfect-kuwait", + "metadata": {}, + "source": [ + "### Riva-deploy\n", + "\n", + "The deployment tool takes as input one or more Riva Model Intermediate Representation (RMIR) files and a target model repository directory. It creates an ensemble configuration specifying the pipeline for the execution and finally writes all those assets to the output model repository directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aware-credits", + "metadata": {}, + "outputs": [], + "source": [ + "# Syntax: riva-deploy -f dir-for-rmir/model.rmir:key output-dir-for-repository\n", + "!docker run --rm --gpus all -v $MODEL_LOC:/data $RIVA_SM_CONTAINER -- \\\n", + " riva-deploy -f /data/punct-capit.rmir:$KEY /data/models" + ] + }, + { + "cell_type": "markdown", + "id": "valued-chapel", + "metadata": {}, + "source": [ + "## Start Riva Server\n", + "\n", + "Once the model repository is generated, we are ready to start the Riva server. From this step you need to have a running Riva server, which can be done by using the steps shown in the setup notebook. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "considered-voluntary", + "metadata": {}, + "outputs": [], + "source": [ + "### Set the path to Riva directory\n", + "RIVA_DIR = " + ] + }, + { + "cell_type": "markdown", + "id": "young-table", + "metadata": {}, + "source": [ + "Note: you can modify ``config.sh`` (as shown in the setup notebook) to enable relevant Riva services (nlp for Punctuation & Capitalization model), provide the encryption key, and path to the model repository (``riva_model_loc``) generated in the previous step among other configurations.\n", + "\n", + "Pretrained versions of models specified in models_asr/nlp/tts are fetched from NGC. Since we are using our custom model, we can comment it in models_nlp (and any others that are not relevant to our use case). " + ] + }, + { + "cell_type": "markdown", + "id": "chief-greensboro", + "metadata": {}, + "source": [ + "## Run Inference\n", + "Once the Riva server is up and running with your models, you can send inference requests querying the server. \n", + "\n", + "To send GRPC requests, you can install Riva Python API bindings for client. This is available as a pip .whl with the QuickStart." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "inappropriate-tomato", + "metadata": {}, + "outputs": [], + "source": [ + "# IMPORTANT: Set the name of the whl file\n", + "RIVA_API_WHL = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "pediatric-junior", + "metadata": {}, + "outputs": [], + "source": [ + "# Install client API bindings\n", + "!cd $RIVA_DIR && pip install $RIVA_API_WHL" + ] + }, + { + "cell_type": "markdown", + "id": "temporal-sense", + "metadata": {}, + "source": [ + "Run the following sample code from within the client docker container:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "decreased-excitement", + "metadata": {}, + "outputs": [], + "source": [ + "import grpc\n", + "import argparse\n", + "import os\n", + "import riva_api.riva_nlp_pb2 as rnlp\n", + "import riva_api.riva_nlp_pb2_grpc as rnlp_srv\n", + "\n", + "class BertPunctuatorClient(object):\n", + " def __init__(self, grpc_server, model_name=\"riva_punctuation\"):\n", + " # generate the correct model based on precision and whether or not ensemble is used\n", + " print(\"Using model: {}\".format(model_name))\n", + " self.model_name = model_name\n", + " self.channel = grpc.insecure_channel(grpc_server)\n", + " self.riva_nlp = rnlp_srv.RivaLanguageUnderstandingStub(self.channel)\n", + "\n", + " self.has_bos = True\n", + " self.has_eos = False\n", + "\n", + " def run(self, input_strings):\n", + " if isinstance(input_strings, str):\n", + " # user probably passed a single string instead of a list/iterable\n", + " input_strings = [input_strings]\n", + "\n", + " request = rnlp.TextTransformRequest()\n", + " request.model.model_name = self.model_name\n", + " for q in input_strings:\n", + " request.text.append(q)\n", + " response = self.riva_nlp.TransformText(request)\n", + "\n", + " return response.text[0]\n", + "\n", + "def run_punct_capit(server,model,query):\n", + " print(\"Client app to test punctuation and capitalization on Riva\")\n", + " client = BertPunctuatorClient(server, model_name=model)\n", + " result = client.run(query)\n", + " print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "labeled-western", + "metadata": {}, + "outputs": [], + "source": [ + "run_punct_capit(server=\"localhost:50051\",\n", + " model=\"riva_punctuation\",\n", + " query=\"how are you doing\")" + ] + }, + { + "cell_type": "markdown", + "id": "lined-roberts", + "metadata": {}, + "source": [ + "You can stop all docker container before shutting down the jupyter kernel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "hybrid-album", + "metadata": {}, + "outputs": [], + "source": [ + "!docker stop $(docker ps -a -q)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/PC/punctuation-and-capitalization-training.ipynb b/ai/RIVA/English/Python/jupyter_notebook/PC/punctuation-and-capitalization-training.ipynb new file mode 100644 index 000000000..90e9e63ae --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/PC/punctuation-and-capitalization-training.ipynb @@ -0,0 +1,674 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[Home Page](../START_HERE_RIVA_HACKATHON.ipynb)\n", + "\n", + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[1]\n", + "[2](punctuation-and-capitalization-deployment.ipynb)\n", + "[3](punctuation-and-capitalization-Exercise.ipynb)\n", + "     \n", + "     \n", + "     \n", + "     \n", + "[Next Notebook](punctuation-and-capitalization-deployment.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Punctuation And Capitalization using Transfer Learning Toolkit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The goal of this toolkit is to reduce that 80 hour workload to an 8 hour workload, which can enable data scientist to have considerably more train-test iterations in the same time frame.\n", + "\n", + "In this notebook, you will learn how to leverage the simplicity and convenience of TLT to:\n", + "- Take a BERT model and __Train/Finetune__ it on a dataset for Punctuation and Capitalization task\n", + "- Run __Inference__\n", + "- __Export__ the model to the ONNX format, or export in the format that is suitable for deployment in Riva\n", + "\n", + "The earlier section in this notebook gives a brief introduction to the Punctuation and Capitalization task and the dataset being used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "Please follow the steps shown in the setup notebook to install the TLT package" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##check for tlt install and availabiility\n", + "!tlt info --verbose" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check if the GPU device(s) are available." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Punctuation And Capitalization using TLT \n", + "### Task Description\n", + "\n", + "Automatic Speech Recognition (ASR) systems typically generate text with no punctuation and capitalization of the words. This tutorial explains how to implement a model that will predict punctuation and capitalization for each word in a sentence to make ASR output more readable and to boost performance of the named entity recognition, machine translation or text-to-speech models. We'll show how to train a model for this task using a pre-trained BERT model. For every word in our training dataset we’re going to predict:\n", + "\n", + "- punctuation mark that should follow the word \n", + "- whether the word should be capitalized" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### TLT Workflow\n", + "### Setting TLT Mounts\n", + "\n", + "Once the TLT gets installed, the next step is to set up the directory mounts that is the ``source`` and ``destination``. The ``source_mount`` will store all the data, pre-trained models and scripts pertaining to Punctuation and Capitalization task. Please select an empty directory for ``source_mount``. \n", + "The ``destination_mount`` is the directory to which ``source_mount`` will be mapped to, inside the container.\n", + "\n", + "The TLT launcher uses docker container under the hood, and for our data and results directory to be visible to the docker, they must be mapped.\n", + "\n", + "The launcher can be configured using the config file ``.tlt_mounts.json``. Apart from the mounts you can also configure additional options like the Environmental Variables and amount of Shared Memory available to the TLT launcher.\n", + "\n", + "``Important Note:`` The code below creates a sample ``.tlt_mounts.json`` file. Here, we can map directories in which we save the data, specs, results and cache. You must configure it for your specific case to make sure both your data and results are correctly mapped to the docker. **Please also ensure that the source directories exist on your machine!**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%bash\n", + "tee ~/.tlt_mounts.json <<'EOF' \n", + "{\n", + " \"Mounts\":[\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/data\"\n", + " },\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/specs\"\n", + " },\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/results\"\n", + " },\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/root/.cache\"\n", + " }\n", + " ]\n", + "}\n", + "EOF" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make sure the source directories exist, if not, create them\n", + "! mkdir \n", + "! mkdir \n", + "! mkdir " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The rest of the notebook exemplifies the simplicity of the TLT workflow. \n", + "Users with any level of Deep Learning knowledge can get started building their own custom models using a simple specification file. It's essentially just one command each to run data download and preprocessing, training, fine-tuning, evaluation, inference, and export. \n", + "All configurations happen through YAML specification files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Configuration/Specification Files\n", + "\n", + "All commands in TLT lies in the YAML specification files. There are sample specification files already available for you to use directly or as reference to create your own YAML specification files. \n", + "\n", + "Through these specification files, you can tune many a lot of things like the model, dataset, hyperparameters, optimizer etc.\n", + "\n", + "Each command (like download_and_convert, train, finetune, evaluate etc.) should have a dedicated specification file with configurations pertinent to it.\n", + "\n", + "Here is an example of the training spec file:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "save_to: trained-model.tlt\n", + "\n", + "trainer:\n", + " max_epochs: 5\n", + " \n", + "model:\n", + " punct_label_ids:\n", + " O: 0\n", + " ',': 1\n", + " '.': 2\n", + " '?': 3\n", + "\n", + " capit_label_ids:\n", + " O: 0\n", + " U: 1 \n", + "\n", + " tokenizer:\n", + " tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece =\n", + " vocab_file: null # path to vocab file \n", + " tokenizer_model: null # only used if tokenizer is sentencepiece\n", + " special_tokens: null\n", + "\n", + " language_model:\n", + " pretrained_model_name: bert-base-uncased\n", + " lm_checkpoint: null\n", + " config_file: null # json file, precedence over config\n", + " config: null \n", + "\n", + " punct_head:\n", + " punct_num_fc_layers: 1\n", + " fc_dropout: 0.1\n", + " activation: 'relu'\n", + " use_transformer_init: true\n", + "\n", + " capit_head:\n", + " capit_num_fc_layers: 1\n", + " fc_dropout: 0.1\n", + " activation: 'relu'\n", + " use_transformer_init: true\n", + "\n", + "# Data dir containing dataset.\n", + "data_dir: ???\n", + "\n", + "training_ds:\n", + " text_file: text_train.txt\n", + " labels_file: labels_train.txt\n", + " shuffle: true\n", + " num_samples: -1 # number of samples to be considered, -1 means the whole the dataset\n", + " batch_size: 64\n", + "\n", + "validation_ds:\n", + " text_file: text_dev.txt\n", + " labels_file: labels_dev.txt\n", + " shuffle: false\n", + " num_samples: -1 # number of samples to be considered, -1 means the whole the dataset\n", + " batch_size: 64\n", + "\n", + "optim:\n", + " name: adam\n", + " lr: 1e-5\n", + " weight_decay: 0.00\n", + "\n", + " sched:\n", + " name: WarmupAnnealing\n", + " # Scheduler params\n", + " warmup_steps: null\n", + " warmup_ratio: 0.1\n", + " last_epoch: -1\n", + "\n", + " # pytorch lightning args\n", + " monitor: val_loss\n", + " reduce_on_plateau: false \n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Set Relevant Paths\n", + "Please set these paths according to your environment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NOTE: The following paths are set from the perspective of the TLT Docker. \n", + "\n", + "# The data is saved here\n", + "DATA_DIR='/data'\n", + "\n", + "# The configuration files are stored here\n", + "SPECS_DIR='/specs/punctuation_and_capitalization'\n", + "\n", + "# The results are saved at this path\n", + "RESULTS_DIR='/results/punctuation_and_capitalization'\n", + "\n", + "# Set your encryption key, and use the same key for all commands\n", + "KEY='tlt_encode'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Downloading Specs\n", + "We can proceed to downloading the spec files. The user may choose to modify/rewrite these specs, or even individually override them through the launcher. You can download the default spec files by using the `download_specs` command.
\n", + "\n", + "The -o argument indicating the folder where the default specification files will be downloaded, and -r that instructs the script where to save the logs. **Make sure the -o points to an empty folder!**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt punctuation_and_capitalization download_specs \\\n", + " -r $RESULTS_DIR/ \\\n", + " -o $SPECS_DIR/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Dataset \n", + "\n", + "This model can work with any dataset as long as it follows the format specified below. The training and evaluation data is divided into _2 files: text.txt and labels.txt_. Each line of the __text.txt__ file contains text sequences, where words are separated with spaces: [WORD] [SPACE] [WORD] [SPACE] [WORD], for example:\n", + "\n", + "when is the next flight to new york
\n", + "the next flight is ...
\n", + "...
\n", + "\n", + "The __labels.txt__ file contains corresponding labels for each word in text.txt, the labels are separated with spaces. Each label in labels.txt file consists of 2 symbols:\n", + "\n", + "- the first symbol of the label indicates what punctuation mark should follow the word (where O means no punctuation needed);\n", + "- the second symbol determines if a word needs to be capitalized or not (where U indicates that the word should be upper cased, and O - no capitalization needed.)\n", + "In this tutorial, we are considering only commas, periods, and question marks the rest punctuation marks were removed. To use more punctuation marks, update the dataset to include desired labels, no changes to the model needed.\n", + "\n", + "Each line of the __labels.txt__ should follow the format: [LABEL] [SPACE] [LABEL] [SPACE] [LABEL] (for labels.txt). For example, labels for the above text.txt file should be:\n", + "\n", + "OU OO OO OO OO OO OU ?U
\n", + "OU OO OO OO ...
\n", + "...\n", + "\n", + "The complete list of all possible labels for this task used in this tutorial is: OO, ,O, .O, ?O, OU, ,U, .U, ?U." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download and preprocess the data \n", + "\n", + "In this notebook we are going to use a subset of English examples from the [Tatoeba collection of sentences](https://tatoeba.org/eng). \n", + "\n", + "Downloading and preprocessing the data using TLT is as simple as configuring YAML specification file and running the ``download_and_convert_tatoeba`` command. The code cell below uses the default `download_and_convert_tatoeba.yaml` available for the users as a reference. \n", + "\n", + "The configurations in the specification file can be easily overridden using the tlt-launcher CLI as shown below. For instance, we override the ``source_data_dir`` and ``target_data_dir`` configurations.\n", + "\n", + "We encourage you to take a look at the YAML files we have provided." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After executing the cell below, your data folder will contain the following 4 files:\n", + "- labels_dev.txt\n", + "- labels_train.txt\n", + "- text_dev.txt\n", + "- text_train.txt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### To download and convert the dataset\n", + "!tlt punctuation_and_capitalization download_and_convert_tatoeba \\\n", + " -e $SPECS_DIR/download_and_convert_tatoeba.yaml \\\n", + " -r $RESULTS_DIR/download_and_convert_tatoeba \\\n", + " source_data_dir=$DATA_DIR \\\n", + " target_data_dir=$DATA_DIR" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Training \n", + "\n", + "In the Punctuation and Capitalization Model, we are jointly training two token-level classifiers on top of the pretrained [BERT](https://arxiv.org/pdf/1810.04805.pdf) model:\n", + "\n", + "- one classifier to predict punctuation and\n", + "- the other one - capitalization.\n", + "\n", + "Training a model using TLT is as simple as configuring your spec file and running the train command. The code cell below uses the default train.yaml available for users as reference. It is configured by default to use the ``bert-base-uncased`` pretrained model. Additionally, these configurations could easily be overridden using the tlt-launcher CLI as shown below. For instance, below we override the trainer.max_epochs, training_ds.num_samples and validation_ds.num_samples configurations to suit our needs. We encourage you to take a look at the .yaml spec files we provide!\n", + "\n", + "The command for training is very similar to the of ``download_and_convert_tatoeba``. Instead of ``tlt punctuation_and_capitalization download_and_convert_tatoeba``, we use ``tlt punctuation_and_capitalization train`` instead. The ``tlt punctuation_and_capitalization train`` command has the following arguments:\n", + "\n", + "- ``-e`` : Path to the spec file\n", + "- ``-g`` : Number of GPUs to use\n", + "- ``-k`` : User specified encryption key to use while saving/loading the model\n", + "- ``-r`` : Path to the folder where the outputs should be written. Make sure this is mapped in the tlt_mounts.json\n", + "- Any overrides to the spec file eg. trainer.max_epochs\n", + "\n", + "More details about these arguments are present in the [TLT Getting Started Guide](https://docs.nvidia.com/tlt/tlt-user-guide/index.html).\n", + "\n", + "``NOTE:`` All file paths corresponds to the destination mounted directory that is visible in the TLT docker container used in backend." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### To train the dataset with BERT-base-uncased model\n", + "!tlt punctuation_and_capitalization train \\\n", + " -e $SPECS_DIR/train.yaml \\\n", + " -g 4 \\\n", + " -r $RESULTS_DIR/train \\\n", + " data_dir=$DATA_DIR \\\n", + " trainer.max_epochs=2 \\\n", + " training_ds.num_samples=-1 \\\n", + " validation_ds.num_samples=-1 \\\n", + " -k $KEY" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The train command produces a .tlt file called ``trained-model.tlt`` saved at ``$RESULTS_DIR/checkpoints/trained-model.tlt``" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Other tips and tricks:\n", + "\n", + "- To accelerate the training without loss of quality, it is possible to train with these parameters: ``trainer.amp_level=\"O1\"`` and ``trainer.precision=16`` for reduced precision.\n", + "- The batch size ``training_ds.batch_size`` may influence the validation accuracy. Larger batch sizes are faster to train with, however, you may get slightly better results with smaller batches.\n", + "- You can also change the optimizer parameter ``optim.name`` and can see its effect on the punctuation and capitalization task by the change in accuracy.\n", + "- You can specify the number of layers in the head of the model ``model.punct_head.punct_num_fc_layers`` and ``model.capit_head.capit_num_fc_layers``." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Finetuning\n", + "\n", + "As stated above the command for all the tasks are very similar but have different YAML specification files that can be tweaked.\n", + "\n", + "Note: If you wish to proceed with a trained dataset for better inference results, you can find a .nemo model [here](\n", + "https://ngc.nvidia.com/catalog/collections/nvidia:nemotrainingframework).\n", + "\n", + "Simply re-name the .nemo file to .tlt and pass it through the finetune pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### To finetune on the dataset\n", + "!tlt punctuation_and_capitalization finetune \\\n", + " -e $SPECS_DIR/finetune.yaml \\\n", + " -g 4 \\\n", + " -m $RESULTS_DIR/train/checkpoints/trained-model.tlt \\\n", + " -r $RESULTS_DIR/finetune \\\n", + " data_dir=$DATA_DIR \\\n", + " trainer.max_epochs=3 \\\n", + " -k $KEY" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The train command produces a .tlt file called ``finetuned-model.tlt`` saved in the results directory." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Evaluation\n", + "\n", + "To evaluate our TLT model we will run the command below. It is always advisable to look at the YAML file for evaluate to understand the command in a better way." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### For evaluation\n", + "!tlt punctuation_and_capitalization evaluate \\\n", + " -e $SPECS_DIR/evaluate.yaml \\\n", + " -g 4 \\\n", + " -m $RESULTS_DIR/finetune/checkpoints/finetuned-model.tlt \\\n", + " data_dir=$DATA_DIR \\\n", + " -r $RESULTS_DIR/evaluate \\\n", + " -k $KEY" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On evaluating the model you will get some results and based on that we can either retrain the model for more epochs or continue with the inference." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Inference\n", + "\n", + "Inference using a TLT trained and fine-tuned model can be done by ``tlt punctuation_and_capitalization infer`` command. It is again advisable to look at the infer.yaml file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### For inference\n", + "!tlt punctuation_and_capitalization infer \\\n", + " -e $SPECS_DIR/infer.yaml \\\n", + " -g 4 \\\n", + " -m $RESULTS_DIR/finetune/checkpoints/finetuned-model.tlt \\\n", + " -r $RESULTS_DIR/infer \\\n", + " -k $KEY" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Export to ONNX\n", + "[ONNX](https://onnx.ai/) is a popular open format for machine learning models. It enables interoperability between different frameworks, making the path to production much easier. \n", + "\n", + "TLT provides commands to export the .tlt model to the ONNX format in an .eonnx archive. The `export_format` configuration can be set to `ONNX` to achieve this.\n", + "\n", + "The tlt export command for ``punctuation_and_capitalization`` is shown in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### For export to ONNX\n", + "!tlt punctuation_and_capitalization export \\\n", + " -e $SPECS_DIR/export.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/finetune/checkpoints/finetuned-model.tlt \\\n", + " -r $RESULTS_DIR/export \\\n", + " -k $KEY \\\n", + " export_format=ONNX" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This command exports the model as ``exported-model.eonnx`` which is essentially an archive containing the .onnx model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Inference using ONNX\n", + "\n", + "TLT provides the capability to use the exported .eonnx model for inference. The command ``tlt punctuation_and_capitalization infer_onnx`` is very similar to the inference command for .tlt models. Again, the input file used is just for demo purposes, you may choose to try out your own custom input." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### For inference using ONNX\n", + "!tlt punctuation_and_capitalization infer_onnx \\\n", + " -e $SPECS_DIR/infer_onnx.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/export/exported-model.eonnx \\\n", + " -r $RESULTS_DIR/infer_onnx \\\n", + " -k $KEY" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Export to Riva\n", + "\n", + "With TLT, you can also export your model in a format that can deployed using [NVIDIA Riva](https://developer.nvidia.com/riva), a highly performant application framework for multi-modal conversational AI services using GPUs. The same command for exporting to ONNX can be used here. The only small variation is the configuration for ``export_format`` in the spec file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### For export to RIVA\n", + "!tlt punctuation_and_capitalization export \\\n", + " -e $SPECS_DIR/export.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/finetune/checkpoints/finetuned-model.tlt \\\n", + " -r $RESULTS_DIR/export_riva \\\n", + " export_format=JARVIS \\\n", + " export_to=punct-capit-model.riva \\\n", + " -k $KEY" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model is exported as ``punct-capit-model.riva`` which is in a format suited for deployment in Riva." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### What next?\n", + "\n", + "You can use TLT to build custom models for your own NLP applications." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/QA/Backup.ipynb b/ai/RIVA/English/Python/jupyter_notebook/QA/Backup.ipynb new file mode 100644 index 000000000..e84e04788 --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/QA/Backup.ipynb @@ -0,0 +1,249 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Previous Notebook](question-answering-deployment.ipynb)\n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[Home Page](../START_HERE_RIVA_BOOTCAMP.ipynb)\n", + "\n", + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[1](question-answering-training.ipynb)\n", + "[2](question-answering-deployment.ipynb)\n", + "[3]\n", + "     \n", + "     \n", + "     \n", + "     " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "# Wikipedia Question Answering\n", + "\n", + "This notebook walks through using Riva NLP Services to generate an answer to an incoming user question using Riva' Question and Answering modul by querying Wikipedia for a summary of the topic in question.\n", + "\n", + "First make sure the Wikipedia API is installed. Alter the bellow command if you use a different package manager. Then we import the required packages.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "##!pip install wikipedia\n", + "##already installed in container" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import wikipedia as wiki\n", + "\n", + "import grpc\n", + "\n", + "import riva_api.riva_nlp_pb2 as rnlp\n", + "import riva_api.riva_nlp_pb2_grpc as rnlp_srv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Wikipedia Summary\n", + "Next we accept user input. We use the Wikipedia API to find the most relevant articles. For the purpose of this example, we combine the top few article summaries. You can change the number of articles with `max_articles_combine`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Getting summary for: Computer science\n", + "Getting summary for: Outline of computer science\n", + "Getting summary for: List of unsolved problems in computer science\n" + ] + } + ], + "source": [ + "input_query = \"What is Computer Science?\"\n", + "\n", + "wiki_articles = wiki.search(input_query)\n", + "max_articles_combine = 3\n", + "combined_summary = \"\"\n", + "\n", + "if len(wiki_articles) == 0:\n", + " print(\"ERROR: Could not find any matching results in Wikipedia.\")\n", + "else:\n", + " for article in wiki_articles[:min(len(wiki_articles), max_articles_combine)]:\n", + " print(f\"Getting summary for: {article}\")\n", + " combined_summary += \"\\n\" + wiki.summary(article)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Query Riva Server\n", + "\n", + "Lastly, we define the GRPC channel to send a request to the Riva server, and define a corresponding request. This allows us to query the Riva server with the given input query and the context taken from Wikipedia.\n", + "\n", + "Make sure you set your grpc channel to the appropriate port." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query: What is Computer Science?\n", + "Answer: the study of algorithmic processes, computational machines and computation itself.\n" + ] + } + ], + "source": [ + "# Alter if using a different port\n", + "channel = grpc.insecure_channel('localhost:50051')\n", + "\n", + "riva_nlp = rnlp_srv.RivaLanguageUnderstandingStub(channel)\n", + "\n", + "req = rnlp.NaturalQueryRequest()\n", + "\n", + "req.query = input_query\n", + "req.context = combined_summary\n", + "resp = riva_nlp.NaturalQuery(req)\n", + "\n", + "print(f\"Query: {input_query}\")\n", + "print(f\"Answer: {resp.results[0].answer}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exercise: We will now use some paragraphs from the SQUAD dataset to generate Answers to the given questions using our model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Paragraph 1:\n", + "\"For a long time, it was thought that the Amazon rainforest was only ever sparsely populated, as it was impossible to sustain a large population through agriculture given the poor soil. Archeologist Betty Meggers was a prominent proponent of this idea, as described in her book Amazonia: Man and Culture in a Counterfeit Paradise. She claimed that a population density of 0.2 inhabitants per square kilometre (0.52/sq mi) is the maximum that can be sustained in the rainforest through hunting, with agriculture needed to host a larger population. However, recent anthropological findings have suggested that the region was actually densely populated. Some 5 million people may have lived in the Amazon region in AD 1500, divided between dense coastal settlements, such as that at Marajó, and inland dwellers. By 1900 the population had fallen to 1 million and by the early 1980s it was less than 200,000.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Paragraph 2: \n", + "\"The first European to travel the length of the Amazon River was Francisco de Orellana in 1542. The BBC's Unnatural Histories presents evidence that Orellana, rather than exaggerating his claims as previously thought, was correct in his observations that a complex civilization was flourishing along the Amazon in the 1540s. It is believed that the civilization was later devastated by the spread of diseases from Europe, such as smallpox. Since the 1970s, numerous geoglyphs have been discovered on deforested land dating between AD 0–1250, furthering claims about Pre-Columbian civilizations. Ondemar Dias is accredited with first discovering the geoglyphs in 1977 and Alceu Ranzi with furthering their discovery after flying over Acre. The BBC's Unnatural Histories presented evidence that the Amazon rainforest, rather than being a pristine wilderness, has been shaped by man for at least 11,000 years through practices such as forest gardening and terra preta.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Question 1: What is the name of the book written by Archeologist Betty Meggers?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Question 2: What is the maximum square miles did Betty Meggers claim that can be sustained in the rainforest?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Question 3: What would be needed to host a larger population?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Question 4: Which findings suggested that the region was densely populated?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Question 5: Who was the first European to travel the Amazon River?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Question 6: How long since it's been that geoglyphs were first discovered on deforested land?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "## first, import required libraries and configure ports according to your connection\n", + "##next, initialize variables with the input queries and questions\n", + "\n", + "##your solution goes here" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/QA/question-answering-Exercise.ipynb b/ai/RIVA/English/Python/jupyter_notebook/QA/question-answering-Exercise.ipynb new file mode 100644 index 000000000..e84e04788 --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/QA/question-answering-Exercise.ipynb @@ -0,0 +1,249 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Previous Notebook](question-answering-deployment.ipynb)\n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[Home Page](../START_HERE_RIVA_BOOTCAMP.ipynb)\n", + "\n", + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[1](question-answering-training.ipynb)\n", + "[2](question-answering-deployment.ipynb)\n", + "[3]\n", + "     \n", + "     \n", + "     \n", + "     " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "# Wikipedia Question Answering\n", + "\n", + "This notebook walks through using Riva NLP Services to generate an answer to an incoming user question using Riva' Question and Answering modul by querying Wikipedia for a summary of the topic in question.\n", + "\n", + "First make sure the Wikipedia API is installed. Alter the bellow command if you use a different package manager. Then we import the required packages.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "##!pip install wikipedia\n", + "##already installed in container" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import wikipedia as wiki\n", + "\n", + "import grpc\n", + "\n", + "import riva_api.riva_nlp_pb2 as rnlp\n", + "import riva_api.riva_nlp_pb2_grpc as rnlp_srv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Wikipedia Summary\n", + "Next we accept user input. We use the Wikipedia API to find the most relevant articles. For the purpose of this example, we combine the top few article summaries. You can change the number of articles with `max_articles_combine`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Getting summary for: Computer science\n", + "Getting summary for: Outline of computer science\n", + "Getting summary for: List of unsolved problems in computer science\n" + ] + } + ], + "source": [ + "input_query = \"What is Computer Science?\"\n", + "\n", + "wiki_articles = wiki.search(input_query)\n", + "max_articles_combine = 3\n", + "combined_summary = \"\"\n", + "\n", + "if len(wiki_articles) == 0:\n", + " print(\"ERROR: Could not find any matching results in Wikipedia.\")\n", + "else:\n", + " for article in wiki_articles[:min(len(wiki_articles), max_articles_combine)]:\n", + " print(f\"Getting summary for: {article}\")\n", + " combined_summary += \"\\n\" + wiki.summary(article)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Query Riva Server\n", + "\n", + "Lastly, we define the GRPC channel to send a request to the Riva server, and define a corresponding request. This allows us to query the Riva server with the given input query and the context taken from Wikipedia.\n", + "\n", + "Make sure you set your grpc channel to the appropriate port." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query: What is Computer Science?\n", + "Answer: the study of algorithmic processes, computational machines and computation itself.\n" + ] + } + ], + "source": [ + "# Alter if using a different port\n", + "channel = grpc.insecure_channel('localhost:50051')\n", + "\n", + "riva_nlp = rnlp_srv.RivaLanguageUnderstandingStub(channel)\n", + "\n", + "req = rnlp.NaturalQueryRequest()\n", + "\n", + "req.query = input_query\n", + "req.context = combined_summary\n", + "resp = riva_nlp.NaturalQuery(req)\n", + "\n", + "print(f\"Query: {input_query}\")\n", + "print(f\"Answer: {resp.results[0].answer}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exercise: We will now use some paragraphs from the SQUAD dataset to generate Answers to the given questions using our model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Paragraph 1:\n", + "\"For a long time, it was thought that the Amazon rainforest was only ever sparsely populated, as it was impossible to sustain a large population through agriculture given the poor soil. Archeologist Betty Meggers was a prominent proponent of this idea, as described in her book Amazonia: Man and Culture in a Counterfeit Paradise. She claimed that a population density of 0.2 inhabitants per square kilometre (0.52/sq mi) is the maximum that can be sustained in the rainforest through hunting, with agriculture needed to host a larger population. However, recent anthropological findings have suggested that the region was actually densely populated. Some 5 million people may have lived in the Amazon region in AD 1500, divided between dense coastal settlements, such as that at Marajó, and inland dwellers. By 1900 the population had fallen to 1 million and by the early 1980s it was less than 200,000.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Paragraph 2: \n", + "\"The first European to travel the length of the Amazon River was Francisco de Orellana in 1542. The BBC's Unnatural Histories presents evidence that Orellana, rather than exaggerating his claims as previously thought, was correct in his observations that a complex civilization was flourishing along the Amazon in the 1540s. It is believed that the civilization was later devastated by the spread of diseases from Europe, such as smallpox. Since the 1970s, numerous geoglyphs have been discovered on deforested land dating between AD 0–1250, furthering claims about Pre-Columbian civilizations. Ondemar Dias is accredited with first discovering the geoglyphs in 1977 and Alceu Ranzi with furthering their discovery after flying over Acre. The BBC's Unnatural Histories presented evidence that the Amazon rainforest, rather than being a pristine wilderness, has been shaped by man for at least 11,000 years through practices such as forest gardening and terra preta.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Question 1: What is the name of the book written by Archeologist Betty Meggers?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Question 2: What is the maximum square miles did Betty Meggers claim that can be sustained in the rainforest?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Question 3: What would be needed to host a larger population?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Question 4: Which findings suggested that the region was densely populated?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Question 5: Who was the first European to travel the Amazon River?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Question 6: How long since it's been that geoglyphs were first discovered on deforested land?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "## first, import required libraries and configure ports according to your connection\n", + "##next, initialize variables with the input queries and questions\n", + "\n", + "##your solution goes here" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/QA/question-answering-deployment.ipynb b/ai/RIVA/English/Python/jupyter_notebook/QA/question-answering-deployment.ipynb new file mode 100644 index 000000000..3301b9f75 --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/QA/question-answering-deployment.ipynb @@ -0,0 +1,226 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[Home Page](../START_HERE_RIVA_BOOTCAMP.ipynb)\n", + "\n", + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[1](question-answering-training.ipynb)\n", + "[2]\n", + "[3](question-answering-Exercise.ipynb)\n", + "     \n", + "     \n", + "     \n", + "     \n", + "[Next Notebook](question-answering-Exercise.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deploying a Question Answering Model in Riva" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Transfer Learning Toolkit](https://developer.nvidia.com/transfer-learning-toolkit) (TLT) provides the capability to export your model in a format that can deployed using [NVIDIA Riva](https://developer.nvidia.com/riva), a highly performant application framework for multi-modal conversational AI services using GPUs. \n", + "\n", + "This tutorial explores taking an .riva model, the result of `tlt question_answering export` command, and leveraging the Riva ServiceMaker framework to aggregate all the necessary artifacts for Riva deployment to a target environment. Once the model is deployed in Riva, you can issue inference requests to the server. We will demonstrate how quick and straightforward this whole process is. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Learning Objectives\n", + "In this notebook, you will learn how to: \n", + "- Use Riva ServiceMaker to take a TLT exported .riva and generate a model [resources](https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart)\n", + "- Deploy the model(s) locally on the Riva Server\n", + "- Send inference requests from a demo client using Riva API bindings." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Pre-requisites" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To follow along, please make sure:\n", + "- You have access to NVIDIA NGC, and are able to download the Riva Quickstart [resources](https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart)\n", + "- Have an .riva model file that you wish to deploy. You can obtain this from `tlt export` (with `export_format=RIVA`). Please refer the tutorial on *Question Answering using Transfer Learning Toolkit* for more details on training and exporting an .riva model.\n", + "- You have followed the steps in the setup notebook and have a running instance of the RIVA server ready" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Riva-deploy\n", + "\n", + "The deployment tool takes as input one or more Riva Model Intermediate Representation (RMIR) files and a target model repository directory. It creates an ensemble configuration specifying the pipeline for the execution and finally writes all those assets to the output model repository directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Syntax: riva-deploy -f dir-for-rmir/model.rmir:key output-dir-for-repository\n", + "!docker run --rm --gpus 1 -v $MODEL_LOC:/data $RIVA_SM_CONTAINER -- \\\n", + " riva-deploy -f /data/question-answering.rmir:$KEY /data/models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remember to modify config.sh to enable relevant Riva services (nlp for Question Answering model), provide the encryption key, and path to the model repository (`riva_model_loc`) generated in the previous step among other configurations. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Run Inference\n", + "Once the Riva server is up and running with your models, you can send inference requests querying the server. \n", + "\n", + "To send GRPC requests, you can install Riva Python API bindings for client. This is available as a pip .whl with the QuickStart.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# IMPORTANT: Set the name of the whl file\n", + "RIVA_API_WHL = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Install client API bindings\n", + "!cd $RIVA_DIR && pip install $RIVA_API_WHL" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following code sample shows how you can perform inference using Riva Python API gRPC bindings:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import grpc\n", + "import riva_api.riva_nlp_pb2 as rnlp\n", + "import riva_api.riva_nlp_pb2_grpc as rnlp_srv\n", + "\n", + "\n", + "grpc_server = \"localhost:50051\"\n", + "channel = grpc.insecure_channel(grpc_server)\n", + "riva_nlp = rnlp_srv.RivaLanguageUnderstandingStub(channel)\n", + "\n", + "req = rnlp.NaturalQueryRequest()\n", + "\n", + "test_context = \"In 2010 the Amazon rainforest experienced another severe drought, in some ways more extreme than the 2005 drought.\"\\\n", + " \"The affected region was approximate 1,160,000 square miles (3,000,000 km2) of rainforest, compared to 734,000 square miles (1,900,000 km2)\" \\\n", + " \" in 2005. The 2010 drought had three epicenters where vegetation died off, whereas in 2005 the drought was focused on the southwestern part.\" \\\n", + " \" The findings were published in the journal Science. In a typical year the Amazon absorbs 1.5 gigatons of carbon dioxide; during 2005\" \\\n", + " \"instead 5 gigatons were released and in 2010 8 gigatons were released.\"\n", + "req.query = \"How many tons of carbon are absorbed the Amazon in a typical year?\"\n", + "req.context = test_context\n", + "resp = riva_nlp.NaturalQuery(req)\n", + "print(resp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`NOTE`: You could also run the above inference code from inside the Riva Client container. The QuickStart provides a script `riva_start_client.sh` to run the container. It has more examples for different services." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " You can stop all docker container before shutting down the jupyter kernel. **Caution: The following command will stop all running containers**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!docker stop $(docker ps -a -q)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## What's next?\n", + "You could train your own custom models in TLT and deploy them in Riva! You could scale up your deployment using Kubernetes with the Riva AI Services Helm Chart, which will pull the relevant Images and download model artifacts from NGC, generate the model repository, start and expose the Riva speech services." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/QA/question-answering-training.ipynb b/ai/RIVA/English/Python/jupyter_notebook/QA/question-answering-training.ipynb new file mode 100644 index 000000000..658ca0725 --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/QA/question-answering-training.ipynb @@ -0,0 +1,913 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[Home Page](../START_HERE_RIVA_BOOTCAMP.ipynb)\n", + "\n", + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[1]\n", + "[2](question-answering-deployment.ipynb)\n", + "[3](question-answering-Exercise.ipynb)\n", + "     \n", + "     \n", + "     \n", + "     \n", + "[Next Notebook](question-answering-deployment.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Question Answering using Transfer Learning Toolkit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Transfer learning* is the process of transferring learned features from one application to another. It is a commonly used training technique where you use a model trained on one task and re-train to use it on a different task.\n", + "\n", + "**Transfer Learning Toolkit (TLT)** is a simple and easy-to-use Python based AI toolkit for taking purpose-built AI models and customizing them with users' own data.\n", + "\n", + "
<\\center>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "In this notebook, you will learn how to leverage the simplicity and convenience of TLT to:\n", + "- Take a [BERT](https://arxiv.org/pdf/1810.04805.pdf) QA model and [**Train/Finetune**](#training) it on the [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) dataset\n", + "- Run [**Inference**](#inference)\n", + "- [**Export**](#export-onnx) the model for the [ONNX](https://onnx.ai/) format, or [export](#export-riva) in a format suitable for deployment in [Riva](https://developer.nvidia.com/riva).\n", + "\n", + "The earlier sections in the notebook give a brief introduction to the QA task, the SQuAD dataset and BERT. If you are already familiar with these, and want to jump right into the meat of the matter, you can start at section on [Data Preparation](#prepare-data)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Pre-requisites\n", + "For ease of use, please install TLT inside a python virtual environment. We recommend performing this step first and then launching the notebook from the virtual environment.\n", + "\n", + "Let's install TLT. It is a simple pip install!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! pip install nvidia-pyindex\n", + "! pip install nvidia-tlt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To see the docker image versions and the tasks that tlt can perform, use the `tlt info` command." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt info --verbose" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to installing TLT package, please make sure of the following software requirements:\n", + "\n", + "1. python 3.6.9\n", + "2. docker-ce > 19.03.5\n", + "3. docker-API 1.40\n", + "4. nvidia-container-toolkit > 1.3.0-1\n", + "5. nvidia-container-runtime > 3.4.0-1\n", + "6. nvidia-docker2 > 2.5.0-1\n", + "7. nvidia-driver >= 455.23" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check if the GPU device(s) is visible" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Question Answering (QA)\n", + "\n", + "### Task Description\n", + "The Question Answering task in NLP pertains to building a model which can answer questions posed in natural language. Many datasets (including [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/), the dataset we use in this notebook) pose this as a reading comprehension task i.e. given a question and a context, the goal is to predict the span within the context with a start and end position which indicates the answer to the question. For every word in the training dataset we predict:\n", + "- likelihood this word is the start of the span\n", + "- likelihood this word is the end of the span" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The SQuAD Dataset\n", + "\n", + "[SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) is a large dataset for QA consisting of reading passages obtained from high-quality Wikipedia articles. With each passage, the dataset contains accompanying reading comprehension questions based on the content of the passage. For each question, there are one or more answers. These questions and corresponding answers were obtained through crowdsourcing.\n", + "\n", + "\n", + "The SQuAD format consists of a JSON file for each dataset split. Each title has one or multiple paragraph entries, each consisting of the text - \"context\", and question-answer entries. Each question-answer entry has:\n", + "\n", + "- a question\n", + "- a boolean flag \"is_impossible\" which shows if the question is answerable or not\n", + "- a globally unique id\n", + "- in case the question is answerable one answer entry, which contains the text span and its starting character index in the context. If not answerable, the \"answers\" list is empty" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "```\n", + "{\n", + " \"data\": [\n", + " {\n", + " \"title\": \"Super_Bowl_50\", \n", + " \"paragraphs\": [\n", + " {\n", + " \"context\": \"Super Bowl 50 was an American football game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24\\u201310 to earn their third Super Bowl title. The game was played on February 7, 2016, at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California. As this was the 50th Super Bowl, the league emphasized the \\\"golden anniversary\\\" with various gold-themed initiatives, as well as temporarily suspending the tradition of naming each Super Bowl game with Roman numerals (under which the game would have been known as \\\"Super Bowl L\\\"), so that the logo could prominently feature the Arabic numerals 50.\", \n", + " \"qas\": [\n", + " {\n", + " \"question\": \"Where did Super Bowl 50 take place?\", \n", + " \"is_impossible\": \"false\", \n", + " \"id\": \"56be4db0acb8001400a502ee\", \n", + " \"answers\": [\n", + " {\n", + " \"answer_start\": \"403\", \n", + " \"text\": \"Santa Clara, California\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"question\": \"What was the winning score of the Super Bowl 50?\", \n", + " \"is_impossible\": \"true\", \n", + " \"id\": \"56be4db0acb8001400a502ez\", \n", + " \"answers\": [\n", + " ]\n", + " }\n", + " ]\n", + " }\n", + " ]\n", + " }\n", + " ]\n", + "}\n", + "...\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The evaluation files (for validation and testing) follow the above format except for it can provide more than one answer to the same question. The inference file follows the above format except for it does not require the \"answers\" and \"is_impossible\" keywords." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BERT Model for QA\n", + "In this notebook, we will show how to use a pre-trained [BERT](https://arxiv.org/pdf/1810.04805.pdf) (Bidirectional Encoder Representations from Transformers) model for QA leveraging TLT. The BERT model has made major breakthroughs in Natural Language Understanding in recent years. For most applications, the model is typically trained in two phases, pre-training and fine-tuning. \n", + "- The BERT core model can be pre-trained on large, generic datasets to generate dense vector representations of input sentence(s). \n", + "- It can be quickly fine-tuned to perform a wide variety of tasks such as question/answering, sentiment analysis, or named entity recognition.\n", + "\n", + "The figure below shows a high-level block diagram of pre-training and fine-tuning BERT for QA.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In alignment with the above, for pre-training we can take one of two approaches. We can either pre-train the BERT model with our own data, or use a model pre-trained by Nvidia. After we obtain a pre-trained model, the next step would be to fine-tune it for the QA task and run inference on the fine-tuned model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### Preparing the dataset\n", + "The SQuAD dataset is available [here](https://rajpurkar.github.io/SQuAD-explorer/). You will find that there are 2 versions of the Squad datasets: v1.1 and v2.0. \n", + "\n", + "- SQuAD 1.1, the older version of the SQuAD dataset, contains 100,000+ question-answer pairs on 500+ articles.\n", + "- SQuAD 2.0 dataset combines the 100,000 questions in SQuAD 1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Downloading the dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For convenience, you may use the code below to download the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# IMPORTANT NOTE: Set path to a folder where you want you data to be saved\n", + "DATA_DOWNLOAD_DIR = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# Create the data and results directories if they don't exist\n", + "if not os.path.exists(DATA_DOWNLOAD_DIR):\n", + " os.makedirs(DATA_DOWNLOAD_DIR)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import urllib.request\n", + "\n", + "# simple utility function to download the squad dataset\n", + "def download_squad(save_path):\n", + " save_path = save_path + '/squad'\n", + "\n", + " if not os.path.exists(save_path):\n", + " os.makedirs(save_path)\n", + "\n", + " if not os.path.exists(save_path + '/v1.1'):\n", + " os.makedirs(save_path + '/v1.1')\n", + "\n", + " if not os.path.exists(save_path + '/v2.0'):\n", + " os.makedirs(save_path + '/v2.0')\n", + " \n", + " # urls for both SQuAD v1.1 and v2.0. You may modify the dict below if you choose to download only one of the two.\n", + " download_urls = {\n", + " 'https://rajpurkar.github.io/SQuAD-explorer' '/dataset/train-v1.1.json': 'v1.1/train-v1.1.json',\n", + " 'https://rajpurkar.github.io/SQuAD-explorer' '/dataset/dev-v1.1.json': 'v1.1/dev-v1.1.json',\n", + " 'https://rajpurkar.github.io/SQuAD-explorer' '/dataset/train-v2.0.json': 'v2.0/train-v2.0.json',\n", + " 'https://rajpurkar.github.io/SQuAD-explorer' '/dataset/dev-v2.0.json': 'v2.0/dev-v2.0.json',\n", + " }\n", + " \n", + " for item in download_urls:\n", + " url = item\n", + " file = download_urls[item]\n", + " print('Downloading:', url)\n", + " if os.path.isfile(save_path + '/' + file):\n", + " print('** Download file already exists, skipping download **')\n", + " else:\n", + " response = urllib.request.urlopen(url)\n", + " with open(save_path + '/' + file, \"wb\") as handle:\n", + " handle.write(response.read())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This will download both v1.1 and v2.0 versions of SQuAD dataset\n", + "download_squad(DATA_DOWNLOAD_DIR)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Verify that the data is present\n", + "!ls $DATA_DOWNLOAD_DIR/squad/v1.1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## TLT workflow\n", + "The rest of the notebook shows what a sample TLT workflow looks like." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setting TLT Mounts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that our dataset has been downloaded, an important step in using TLT is to set up the directory mounts. The TLT launcher uses docker containers under the hood, and **for our data and results directory to be visible to the docker, they need to be mapped**. The launcher can be configured using the config file `~/.tlt_mounts.json`. Apart from the mounts, you can also configure additional options like the Environment Variables and amount of Shared Memory available to the TLT launcher.
\n", + "\n", + "`IMPORTANT NOTE:` The code below creates a sample `~/.tlt_mounts.json` file. Here, we can map directories in which we save the data, specs, results and cache. You should configure it for your specific case such your these directories are correctly visible to the docker container. **Please also ensure that the source directories exist on your machine!**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%bash\n", + "tee ~/.tlt_mounts.json <<'EOF'\n", + "{\n", + " \"Mounts\":[\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/data\"\n", + " },\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/specs\"\n", + " },\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/results\"\n", + " },\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/root/.cache\"\n", + " }\n", + " ]\n", + "}\n", + "EOF" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make sure the source directories exist, if not, create them\n", + "! mkdir \n", + "! mkdir \n", + "! mkdir " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The rest of the notebook exemplifies the simplicity of the TLT workflow. Users with basic knowledge of Deep Learning can get started building their own custom models using a simple specification file. It's essentially just one command each to run data preprocessing, training, fine-tuning, evaluation, inference, and export! All configurations happen through YAML spec files
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Configuration/Specification Files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The essence of all commands in TLT lies in the YAML spec files. There are sample spec files already available for you to use directly or as reference to create your own. Through these spec files, you can tune many knobs like the model, dataset, hyperparameters, optimizer etc. Each command (like train, finetune, evaluate etc.) should have a dedicated spec file with configurations pertinent to it.
\n", + "\n", + "Here is an example of the training spec file:\n", + "\n", + "---\n", + "```\n", + "trainer:\n", + " max_epochs: 100\n", + "\n", + "model:\n", + " tokenizer:\n", + " tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece\n", + " vocab_file: null # path to vocab file \n", + " tokenizer_model: null # only used if tokenizer is sentencepiece\n", + " special_tokens: null\n", + "\n", + " language_model:\n", + " pretrained_model_name: bert-base-uncased\n", + " lm_checkpoint: null\n", + " config_file: null # json file, precedence over config\n", + " config: null\n", + "\n", + " token_classifier:\n", + " num_layers: 1\n", + " dropout: 0.0\n", + " num_classes: 2\n", + " activation: relu\n", + " log_softmax: false\n", + " use_transformer_init: true\n", + "\n", + "\n", + "training_ds:\n", + " file: ??? # e.g. squad/v1.1/train-v2.0.json\n", + " batch_size: 12 # per GPU\n", + "\n", + "...\n", + "```\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Convert\n", + "For the QA task, the commands in TLT accepts data in the SQuAD JSON format (refer `Preparing the dataset` section above). If you have your data in any other format, be sure to convert it in the SQuAD format. Since we are using the SQuAD dataset for this notebook, we don't need to convert the data into any other format. We can proceed with training/fine-tuning directly." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Set Relevant Paths\n", + "Set these paths according to your environment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NOTE: The following paths are set from the perspective of the TLT Docker. \n", + "\n", + "# The data is saved here\n", + "DATA_DIR='/data/squad'\n", + "\n", + "# The configuration files are stored here\n", + "SPECS_DIR='/specs/question_answering'\n", + "\n", + "# The results are saved at this path\n", + "RESULTS_DIR='/results/question_answering'\n", + "\n", + "# Set your encryption key, and use the same key for all commands\n", + "KEY='tlt_encode'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Downloading Specs\n", + "We can proceed to downloading the spec files. The user may choose to modify/rewrite these specs, or even individually override them through the launcher. You can download the default spec files by using the `download_specs` command.
\n", + "\n", + "The -o argument indicating the folder where the default specification files will be downloaded, and -r that instructs the script where to save the logs. **Make sure the -o points to an empty folder!**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt question_answering download_specs \\\n", + " -r $RESULTS_DIR \\\n", + " -o $SPECS_DIR" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### Training\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Training a model using TLT is as simple as configuring your spec file and running the train command. The code cell below uses the default train.yaml available for users as reference. It is configured by default to use the `bert-base-uncased` pretrained model. Additionally, these configurations could easily be overridden using the tlt-launcher CLI as shown below. For instance, below we override the `training_ds.file`, `validation_ds.file`, `trainer.max_epochs`, `training_ds.num_workers` and `validation_ds.num_workers` configurations to suit our needs. We encourage you to take a look at the .yaml spec files we provide!
\n", + "\n", + "For training a QA model in TLT, we use the `tlt question_answering train` command with the following args:\n", + "- `-e`: Path to the spec file\n", + "- `-g`: Number of GPUs to use\n", + "- `-k`: User specified encryption key to use while saving/loading the model\n", + "- `-r`: Path to a folder where the outputs should be written. Make sure this is mapped in tlt_mounts.json\n", + "- Any overrides to the spec file eg. trainer.max_epochs
\n", + "\n", + "More details about these arguments are present in the [TLT Getting Started Guide](https://docs.nvidia.com/tlt/tlt-user-guide/index.html)
\n", + "`NOTE:` All file paths corresponds to the destination mounted directory that is visible in the TLT docker container used in backend.
\n", + "\n", + "Also worth noting is that the first time you run training on the dataset, it will run pre-processing and save that processed data in the same directory as the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Since this is a demonstration, we just train for 1 epoch below. You may need to train for more depending on your dataset.\n", + "!tlt question_answering train \\\n", + " -e $SPECS_DIR/train_bert.yaml \\\n", + " -g 1 \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/train \\\n", + " training_ds.file=$DATA_DIR/v1.1/train-v1.1.json \\\n", + " validation_ds.file=$DATA_DIR/v1.1/dev-v1.1.json \\\n", + " trainer.max_epochs=1 \\\n", + " training_ds.num_workers=4 \\\n", + " validation_ds.num_workers=4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The train command produces a .tlt file called `trained-model.tlt` saved at `$RESULTS_DIR/train/checkpoints/trained-model.tlt`. This file can be fed directly into the fine-tuning stage as we see in the next block." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Other tips and tricks:\n", + "- To accelerate the training without loss of quality, it is possible to train with these parameters: `trainer.amp_level=\"O1\"` and `trainer.precision=16` for reduced precision.\n", + "- The batch size (`training_ds.batch_size`) may influence the validation accuracy. Larger batch sizes are faster to train with, however, you may get slightly better results with smaller batches.\n", + "- You can use the parameter: `trainer.val_check_interval` to define how many times per epoch to see validation accuracy metric calculated and printed. For instance, using `trainer.val_check_interval=0.25` will show the metric 4 times per epoch." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Fine-Tuning\n", + "Like many other NLP tasks, since we begin with a pretrained BERT model the step shown above for (re)training with your custom data should do the trick. However, TLT does provide a command for fine-tuning if your use-case demands that. Instead of `tlt question_answering train`, we use `tlt question_answering finetune` instead. We also specify the spec file corresponding to fine-tuning. All commands in TLT follow a similar pattern, streamlining the workflow even further!\n", + "\n", + "Note: If you wish to proceed with a trained dataset for better inference results, you can find a .nemo model [here](\n", + "https://ngc.nvidia.com/catalog/collections/nvidia:nemotrainingframework).\n", + "\n", + "Simply re-name the .nemo file to .tlt and pass it through the finetune pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt question_answering finetune \\\n", + " -e $SPECS_DIR/finetune.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/train/checkpoints/trained-model.tlt \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/finetune \\\n", + " finetuning_ds.file=$DATA_DIR/v2.0/train-v2.0.json \\\n", + " validation_ds.file=$DATA_DIR/v2.0/dev-v2.0.json \\\n", + " trainer.max_epochs=1 \\\n", + " finetuning_ds.num_workers=4 \\\n", + " validation_ds.num_workers=4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This command will generate a fine-tuned model `finetuned-model.tlt` at `$RESULTS_DIR/finetune/checkpoints`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### Evaluation\n", + "The evaluation spec .yaml is as simple as:\n", + "\n", + "```\n", + "test_ds:\n", + " file: ??? # e.g. squad/v1.1/dev-v1.1.json \n", + " batch_size: 32\n", + " shuffle: false\n", + " num_samples: 500\n", + "```\n", + "\n", + "Below, we use `tlt question_answering evaluate` and override the test data configuration by specifying `test_ds.file`. Other arguments follow the same pattern as before!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt question_answering evaluate \\\n", + " -e $SPECS_DIR/evaluate.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/train/checkpoints/trained-model.tlt \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/evaluate \\\n", + " test_ds.file=$DATA_DIR/v2.0/dev-v2.0.json \\\n", + " test_ds.batch_size=32 \\\n", + " test_ds.num_samples=500" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output of Evaluation should give the exact match/f1 scores for each data point. Remember that we had trained for just 1 epoch since this is a demonstration!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### Inference\n", + "Inference using a .tlt trained or fine-tuned model uses the `tlt question_answering infer` command.
\n", + "The infer.yaml is also very uncomplicated:\n", + "```\n", + "# Name of file containing data used as inputs during the inference.\n", + "infer_ds:\n", + " file: ??? # e.g. squad/v1.1/dev-v1.1.json\n", + "\n", + "```\n", + "\n", + "We use the SQuAD 2.0 evaluation file for the sake of demonstration, you can also try out your own custom inputs as an exercise!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt question_answering infer \\\n", + " -e $SPECS_DIR/infer.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/train/checkpoints/trained-model.tlt \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/infer \\\n", + " infer_ds.file=$DATA_DIR/v2.0/dev-v2.0.json" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results of inference are saved at `$RESULTS_DIR/infer/prediction.txt` in the format:\n", + "```\n", + "{\n", + " : ,\n", + " ...\n", + "}\n", + "```\n", + "A file with n-best results for each question is also saved at `$RESULTS_DIR/infer/nbest.txt` in the format:\n", + "```\n", + "{\n", + " : [\n", + " {\n", + " \"text\": ,\n", + " \"probability\": 0.9576789114427999,\n", + " \"start_logit\": 7.168248653411865,\n", + " \"end_logit\": 6.93817138671875\n", + " },\n", + " {\n", + " \"text\": ,\n", + " \"probability\": 0.02714239211951417,\n", + " \"start_logit\": 7.168248653411865,\n", + " \"end_logit\": 3.374755620956421\n", + " },\n", + " ...\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### Export to ONNX" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[ONNX](https://onnx.ai/) is a popular open format for machine learning models. It enables interoperability between different frameworks, easing the path to production. \n", + "\n", + "TLT provides commands to export the .tlt model to the ONNX format in an .eonnx archive. The `export_format` configuration can be set to `ONNX` to achieve this.\n", + "\n", + "Sample usage of the `tlt question_answering export` command is shown in the following code cell. The export.yaml file we use looks like:\n", + "```\n", + "# Name of the .tlt EFF archive to be loaded/model to be exported.\n", + "restore_from: trained-model.tlt\n", + "\n", + "# Set export format: ONNX | JARVIS\n", + "export_format: JARVIS\n", + "\n", + "# Output EFF archive containing ONNX.\n", + "export_to: exported-model.eonnx\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt question_answering export \\\n", + " -e $SPECS_DIR/export.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/train/checkpoints/trained-model.tlt \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/export \\\n", + " export_format=ONNX" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This command exports the model as `exported-model.eonnx` which is essentially an archive containing the .onnx model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Inference using ONNX" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TLT provides the capability to use the exported .eonnx model for inference. The command `tlt question_answering infer_onnx` is very similar to the inference command for .tlt models. Again, the input file used are just for demo purposes, you may choose to try out their custom input!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt question_answering infer_onnx \\\n", + " -e $SPECS_DIR/infer_onnx.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/export/exported-model.eonnx \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/infer_onnx \\\n", + " input_file=$DATA_DIR/v2.0/dev-v2.0.json" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output of this `infer_onnx` is similar to that of the `infer` command before. A `prediction.txt` and a `nbest.txt` file is generated. You can configure the exact name/location of these results files in the spec yaml." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### Export to Riva" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With TLT, you can also export your model in a format that can deployed using [Nvidia Riva](https://developer.nvidia.com/riva), a highly performant application framework for multi-modal conversational AI services using GPUs! The same command for exporting to ONNX can be used here. The only small variation is the configuration for `export_format` in the spec file!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt question_answering export \\\n", + " -e $SPECS_DIR/export.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/train/checkpoints/trained-model.tlt \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/export_riva \\\n", + " export_format=JARVIS \\\n", + " export_to=qa-model.riva" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model is exported as `qa-model.riva` which is in a format suited for deployment in Riva." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## What's Next?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You could use TLT to build custom models for your own applications, or you could deploy the custom model to Nvidia Riva!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/References-Riva.ipynb b/ai/RIVA/English/Python/jupyter_notebook/References-Riva.ipynb new file mode 100644 index 000000000..9f87aadf8 --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/References-Riva.ipynb @@ -0,0 +1,59 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Return to Home Page](START_HERE_RIVA.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "\n", + "- https://ngc.nvidia.com/catalog/collections/nvidia:jarvis/\n", + "- https://developer.nvidia.com/nvidia-jarvis\n", + "- https://ngc.nvidia.com/catalog/models/nvidia:tlt-jarvis:speechtotext_english_citrinet\n", + "- https://developer.nvidia.com/blog/introducing-jarvis-framework-for-gpu-accelerated-conversational-ai-apps/\n", + "- http://man.hubwiz.com/docset/LibROSA.docset/Contents/Resources/Documents/core.html#audio-processing\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Return to Home Page](START_HERE_RIVA.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/START_HERE_RIVA.ipynb b/ai/RIVA/English/Python/jupyter_notebook/START_HERE_RIVA.ipynb new file mode 100644 index 000000000..e1b5e0ae5 --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/START_HERE_RIVA.ipynb @@ -0,0 +1,123 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GPU BOOTCAMP FOR RIVA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This repository consists of gpu bootcamp material for RIVA. The RIVA frameworks of libraries, APIs and models allows you to build, optimize and deploy voice and speech centric applications and models based entirely on GPUs. In this series you can access RIVA learning resources in the form of labs. The modules covered in this Bootcamp are SpeechToText, Intent Slot Classification, Named Entity Recognition, Question-Answering and Challenge. To access each module individually, you can refer to the respective folders in this repository. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Contents\n", + "\n", + "This RIVA Bootcamp will be covering some interesting libraries that demonstrate the capabilities of the frameworks and tasks related to building voice based applications and interfaces. Each module has a backup exercise notebook that you can refer to incase you wish to recover some cells in a lab that you deleted by mistake. Here are the modules in this tutorial:\n", + "\n", + "- Introduction\n", + " - [Introduction to RIVA](Intro/Intro_To_Riva.ipynb)\n", + "\n", + "- Setup\n", + " - [RIVA Setup and basic configuration](Intro/riva-setup.ipynb)\n", + "\n", + "\n", + "- SpeechToText\n", + " - [SpeechToText Training](STT/speech-to-text-training.ipynb)\n", + " - [SpeechToText Deployment](STT/speech-to-text-deployment.ipynb)\n", + " - [Exercise](STT/speechtotext-Exercise.ipynb)\n", + " - [Backup](STT/Backup.ipynb)\n", + " \n", + "\n", + "- Question Answering\n", + " - [Question Answering Training](QA/question-answering-training.ipynb)\n", + " - [Question Answering Deployment](QA/question-answering-deployment.ipynb)\n", + " - [Exercise](QA/question-answering-Exercise.ipynb)\n", + " - [Backup](QA/Backup.ipynb)\n", + "\n", + "\n", + "- Named Entity Recognition (Token Classification)\n", + " - [Named Entity Recognition Training](NER/token-classification-training.ipynb)\n", + " - [Named Entity Recognition Deployment](NER/token-classification-deployment.ipynb)\n", + " - [Exercise](NER/token-classification-Exercise.ipynb)\n", + " - [Backup](NER/Backup.ipynb)\n", + "\n", + "- Text Classification \n", + " - [Text CLassification Training](NER/token-classification-training.ipynb)\n", + " - [Text Classification Deployment](NER/token-classification-deployment.ipynb)\n", + " - [Exercise](NER/token-classification-Exercise.ipynb)\n", + " - [Backup](NER/Backup.ipynb)\n", + "\n", + "- Intent Slot Classification\n", + " - [Intent Slot Classification Training](NER/token-classification-training.ipynb)\n", + " - [Intent Slot Classification Deployment](NER/token-classification-deployment.ipynb)\n", + " - [Exercise](NER/token-classification-Exercise.ipynb)\n", + " - [Backup](NER/Backup.ipynb)\n", + "\n", + "- Punctuation \n", + " - [Punctuation Training](NER/token-classification-training.ipynb)\n", + " - [Punctuation Deployment](NER/token-classification-deployment.ipynb)\n", + " - [Exercise](NER/token-classification-Exercise.ipynb)\n", + " - [Backup](NER/Backup.ipynb)\n", + "\n", + "- Challenge\n", + "\n", + " - Semantic Index Builder \n", + " - [Challenge](Challenge/Challenge.ipynb)\n", + " - [Backup](Challenge/Backup.ipynb)\n", + "\n", + "- [Acknowledgement and References](References-Riva.ipynb)\n", + "\n", + " \n", + "\n", + "\n", + "Note: The challenge is an extra module that you can try after learning the individual modules to test your knowledge.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For more information on RIVA, feel free to explore the official resources and documentation here:\n", + "- https://developer.nvidia.com/riva\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Licensing\n", + " \n", + "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/STT/Backup.ipynb b/ai/RIVA/English/Python/jupyter_notebook/STT/Backup.ipynb new file mode 100644 index 000000000..84162c3a9 --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/STT/Backup.ipynb @@ -0,0 +1,282 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Previous Notebook](speech-to-text-deployment.ipynb)\n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[Home Page](../START_HERE_JARVIS.ipynb)\n", + "\n", + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[1](speech-to-text-training.ipynb)\n", + "[2](speech-to-text-deployment.ipynb)\n", + "[3]\n", + "     \n", + "     \n", + "     \n", + "     " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TLT - ASR Riva Exercise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Transfer Learning Toolkit](https://developer.nvidia.com/transfer-learning-toolkit) (TLT) provides the capability to export your model in a format that can deployed using [NVIDIA Riva](https://developer.nvidia.com/riva), a highly performant application framework for multi-modal conversational AI services using GPUs. \n", + "\n", + "This tutorial explores taking an .riva model, the result of `tlt speech_to_text` command, and leveraging the Riva ServiceMaker framework to aggregate all the necessary artifacts for Riva deployment to a target environment. Once the model is deployed in Riva, you can issue inference requests to the server. We will demonstrate how quick and straightforward this whole process is.\n", + "\n", + "At the end of this tutorial is a quick exercise to give you a hands-on experience of using the Jarvis ASR Service." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Learning Objectives\n", + "In this notebook, you will learn how to: \n", + "- Send inference requests from a demo client using Riva API bindings.\n", + "- Batch transcribe a set of files using the Riva ASR API" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Pre-requisites" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To follow along, please make sure:\n", + "- You have completed the previous notebook or have a pretrained model and environment ready to deploy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Start Riva Server\n", + "### note: this step is for reference, we have already covered this in the setup phase\n", + "Once the model repository is generated, we are ready to start the Riva server. From this step onwards you need to download the Riva QuickStart Resource from NGC. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### config.sh snippet\n", + "```\n", + "# Enable or Disable Riva Services \n", + "service_enabled_asr=true ## MAKE CHANGES HERE\n", + "service_enabled_nlp=false ## MAKE CHANGES HERE\n", + "service_enabled_tts=false ## MAKE CHANGES HERE\n", + "\n", + "# Specify one or more GPUs to use\n", + "# specifying more than one GPU is currently an experimental feature, and may result in undefined behaviours.\n", + "gpus_to_use=\"device=0\"\n", + "\n", + "# Specify the encryption key to use to deploy models\n", + "MODEL_DEPLOY_KEY=\"tlt_encode\" ## MAKE CHANGES HERE\n", + "\n", + "# Locations to use for storing models artifacts\n", + "#\n", + "# If an absolute path is specified, the data will be written to that location\n", + "# Otherwise, a docker volume will be used (default).\n", + "#\n", + "# riva_init.sh will create a `rmir` and `models` directory in the volume or\n", + "# path specified. \n", + "#\n", + "# RMIR ($riva_model_loc/rmir)\n", + "# Riva uses an intermediate representation (RMIR) for models\n", + "# that are ready to deploy but not yet fully optimized for deployment. Pretrained\n", + "# versions can be obtained from NGC (by specifying NGC models below) and will be\n", + "# downloaded to $riva_model_loc/rmir by `riva_init.sh`\n", + "# \n", + "# Custom models produced by NeMo or TLT and prepared using riva-build\n", + "# may also be copied manually to this location $(riva_model_loc/rmir).\n", + "#\n", + "# Models ($riva_model_loc/models)\n", + "# During the riva_init process, the RMIR files in $riva_model_loc/rmir\n", + "# are inspected and optimized for deployment. The optimized versions are\n", + "# stored in $riva_model_loc/models. The riva server exclusively uses these\n", + "# optimized versions.\n", + "riva_model_loc=\"\" ## MAKE CHANGES HERE (Replace with MODEL_LOC) \n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "---\n", + "## Run Inference\n", + "Once the Riva server is up and running with your models, you can send inference requests querying the server. \n", + "\n", + "To send GRPC requests, you can install Riva Python API bindings for client. This is available as a pip .whl with the QuickStart.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect to Riva server and run inference\n", + "Now we actually query the Riva server, let's get started. The following cell queries the riva server(using grpc) to yield a result." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import argparse\n", + "import grpc\n", + "import time\n", + "import riva_api.audio_pb2 as ra\n", + "import riva_api.riva_asr_pb2 as rasr\n", + "import riva_api.riva_asr_pb2_grpc as rasr_srv\n", + "import wave\n", + "\n", + "audio_file = \"\"\n", + "##change the following to reflect your parameters\n", + "server = \"localhost:50051\"\n", + "\n", + "wf = wave.open(audio_file, 'rb')\n", + "with open(audio_file, 'rb') as fh:\n", + " data = fh.read()\n", + "\n", + "channel = grpc.insecure_channel(server)\n", + "client = rasr_srv.RivaSpeechRecognitionStub(channel)\n", + "config = rasr.RecognitionConfig(\n", + " encoding=ra.AudioEncoding.LINEAR_PCM,\n", + " sample_rate_hertz=wf.getframerate(),\n", + " language_code=\"en-US\",\n", + " max_alternatives=1,\n", + " enable_automatic_punctuation=False,\n", + " audio_channel_count=1\n", + ")\n", + "\n", + "request = rasr.RecognizeRequest(config=config, audio=data)\n", + "\n", + "response = client.Recognize(request)\n", + "print(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exercise: Transcribe the following files in the data folder: \n", + "- sp01sn15.wav\n", + "- sp02sn15.wav\n", + "- sp03sn15.wav\n", + "- sp04sn15.wav\n", + "- sp05sn15.wav\n", + "- sp06sn15.wav\n", + "\n", + "The following snippet and template will be helpful" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##code snippet to inspect an audio file and listen to it\n", + "\n", + "import IPython.display as ipd\n", + "import io\n", + "import librosa\n", + "\n", + "# read in an audio file from local disk\n", + "path = \"/path/to/sample.wav\"\n", + "audio, sr = librosa.core.load(path, sr=None)\n", + "with io.open(path, 'rb') as fh:\n", + " content = fh.read()\n", + "ipd.Audio(path)\n", + "\n", + "print (sr) ##sampling rate, comment out to listen to file\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import argparse\n", + "import grpc\n", + "import time\n", + "import riva_api.audio_pb2 as ra\n", + "import riva_api.riva_asr_pb2 as rasr\n", + "import riva_api.riva_asr_pb2_grpc as rasr_srv\n", + "import wave\n", + "\n", + "server = : \n", + "\n", + "## for each file in the list, do the following:\n", + "\n", + "## define channel\n", + "\n", + "## define client\n", + "\n", + "## define configuration\n", + "\n", + "## note: you may need to change this depending on the file. \n", + "## you can inspect a file with the code snippet in the previous cell\n", + "\n", + "## send a request to the server\n", + "\n", + "## call the recognize method on the request object\n", + "\n", + "## append result to results text object/file\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/STT/speech-to-text-deployment.ipynb b/ai/RIVA/English/Python/jupyter_notebook/STT/speech-to-text-deployment.ipynb new file mode 100644 index 000000000..8f7851cea --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/STT/speech-to-text-deployment.ipynb @@ -0,0 +1,202 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[Home Page](../START_HERE_RIVA_BOOTCAMP.ipynb)\n", + "\n", + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[1](speech-to-text-training.ipynb)\n", + "[2]\n", + "[3](speechtotext-Exercise.ipynb)\n", + "     \n", + "     \n", + "     \n", + "     \n", + "[Next Notebook](speechtotext-Exercise.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TLT - ASR Riva Deployment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Transfer Learning Toolkit](https://developer.nvidia.com/transfer-learning-toolkit) (TLT) provides the capability to export your model in a format that can deployed using [NVIDIA Riva](https://developer.nvidia.com/riva), a highly performant application framework for multi-modal conversational AI services using GPUs. \n", + "\n", + "This tutorial explores taking an .riva model, the result of `tlt speech_to_text` command, and leveraging the Riva ServiceMaker framework to aggregate all the necessary artifacts for Riva deployment to a target environment. Once the model is deployed in Riva, you can issue inference requests to the server. We will demonstrate how quick and straightforward this whole process is. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Learning Objectives\n", + "In this notebook, you will learn how to: \n", + "- Use Riva ServiceMaker to take a TLT exported .riva and convert it to .rmir\n", + "- Deploy the model(s) locally on the Riva Server\n", + "- Send inference requests from a demo client using Riva API bindings." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Pre-requisites" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To follow along, please make sure:\n", + "- You have access to NVIDIA NGC, and are able to download the Riva Quickstart [resources](https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart)\n", + "- Have an .riva model file that you wish to deploy. You can obtain this from `tlt export` (with `export_format=RIVA`). Please refer the tutorial on *Automatic Speech Recognition using Transfer Learning Toolkit* for more details on training and exporting an .riva model.\n", + "- You have followed the steps in the setup notebook and have a running instance of the RIVA server ready" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Riva-deploy\n", + "\n", + "The deployment tool takes as input one or more Riva Model Intermediate Representation (RMIR) files and a target model repository directory. It creates an ensemble configuration specifying the pipeline for the execution and finally writes all those assets to the output model repository directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Syntax: riva-deploy -f dir-for-rmir/model.rmir:key output-dir-for-repository\n", + "! docker run --rm --gpus 0 -v $MODEL_LOC:/data $RIVA_SM_CONTAINER -- \\\n", + " riva-deploy -f /data/asr.rmir:$KEY /data/models/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Run Inference\n", + "Once the Riva server is up and running with your models, you can send inference requests querying the server. \n", + "\n", + "To send GRPC requests, you can install Riva Python API bindings for client. This is available as a pip .whl with the QuickStart.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Install client API bindings\n", + "! cd $RIVA_DIR && pip install " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect to Riva server and run inference\n", + "Now we actually query the Riva server, let's get started. The following cell queries the riva server(using grpc) to yield a result." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import argparse\n", + "import grpc\n", + "import time\n", + "import riva_api.audio_pb2 as ra\n", + "import riva_api.riva_asr_pb2 as rasr\n", + "import riva_api.riva_asr_pb2_grpc as rasr_srv\n", + "import wave\n", + "\n", + "audio_file = \"\"\n", + "server = \"localhost:50051\"\n", + "\n", + "wf = wave.open(audio_file, 'rb')\n", + "with open(audio_file, 'rb') as fh:\n", + " data = fh.read()\n", + "\n", + "channel = grpc.insecure_channel(server)\n", + "client = rasr_srv.RivaSpeechRecognitionStub(channel)\n", + "config = rasr.RecognitionConfig(\n", + " encoding=ra.AudioEncoding.LINEAR_PCM,\n", + " sample_rate_hertz=wf.getframerate(),\n", + " language_code=\"en-US\",\n", + " max_alternatives=1,\n", + " enable_automatic_punctuation=False,\n", + " audio_channel_count=1\n", + ")\n", + "\n", + "request = rasr.RecognizeRequest(config=config, audio=data)\n", + "\n", + "response = client.Recognize(request)\n", + "print(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can stop all docker container before shutting down the jupyter kernel. **Caution: The following command will stop all running containers**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! docker stop $(docker ps -a -q)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/STT/speech-to-text-training.ipynb b/ai/RIVA/English/Python/jupyter_notebook/STT/speech-to-text-training.ipynb new file mode 100644 index 000000000..87be98d2b --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/STT/speech-to-text-training.ipynb @@ -0,0 +1,701 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[Home Page](../START_HERE_RIVA_BOOTCAMP.ipynb)\n", + "\n", + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[1]\n", + "[2](speech-to-text-deployment.ipynb)\n", + "[3](speechtotext-Exercise.ipynb)\n", + "     \n", + "     \n", + "     \n", + "     \n", + "[Next Notebook](speech-to-text-deployment.ipynb)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Automatic Speech Recognition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Automatic Speech Recognition(ASR) is often the first step in a building a Conversational AI model. An ASR model converts audible speech into text. The main metric for these models is to reduce Word Error Rate (WER) while transcribing the text. Simply put, the goal is to take an audio file and trancribe it.\n", + "\n", + "In this work, we are going to dicuss two models, [QuartzNet](https://arxiv.org/pdf/1910.10261.pdf) and [Jasper (Just Another SPeech Recognizer) model](https://arxiv.org/abs/1904.03288), both of which are end to end ASR models which take in audio and produce text.\n", + "\n", + "Jasper architectures consist of a repeated block structure that utilizes 1D convolutions. In a Jasper_KxR model, R sub-blocks (consisting of a 1D convolution, batch norm, ReLU, and dropout) are grouped into a single block, which is then repeated K times. We also have a one extra block at the beginning and a few more at the end that are invariant of K and R, and we use CTC loss.\n", + "\n", + "The QuartzNet is better variant of Jasper with a key difference that it uses time-channel separable 1D convolutions. This allows it to dramatically reduce number of weights while keeping similar accuracy.\n", + "\n", + "![QuartzNet with CTC](https://developer.nvidia.com/blog/wp-content/uploads/2020/05/quartznet-model-architecture-1-625x742.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Transfer Learning Toolkit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Transfer Learning Toolkit (TLT) is a python based AI toolkit for taking purpose-built pre-trained AI models and customizing them with your own data. \n", + "\n", + "Transfer learning extracts learned features from an existing neural network to a new one. Transfer learning is often used when creating a large training dataset is not feasible. \n", + "\n", + "Developers, researchers and software partners building intelligent vision AI apps and services, can bring their own data to fine-tune pre-trained models instead of going through the hassle of training from scratch." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Transfer Learning Toolkit](https://developer.nvidia.com/sites/default/files/akamai/embedded-transfer-learning-toolkit-software-stack-1200x670px.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The goal of this toolkit is to reduce that 80 hour workload to an 8 hour workload, which can enable data scientist to have considerably more train-test iterations in the same time frame.\n", + "\n", + "Let's see this in action with a use case for Automatic Speech Recognition!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Let's Dig in: ASR using TLT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ensure you have tlt installed and running (see setup notebook)\n", + "The next step is to setup the mounts for TLT. The TLT launcher uses docker containers under the hood, and **for our data and results directory to be visible to the docker, they need to be mapped**. The launcher can be configured using the config file `~/.tlt_mounts.json`. Apart from the mounts, you can also configure additional options like the Environment Variables and amount of Shared Memory available to the TLT launcher.
\n", + "\n", + "`IMPORTANT NOTE:` The code below creates a sample `~/.tlt_mounts.json` file. Here, we can map directories in which we save the data, specs, results and cache. You should configure it for your specific case such your these directories are correctly visible to the docker container." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! mkdir \n", + "! mkdir \n", + "! mkdir " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%bash\n", + "tee ~/.tlt_mounts.json <<'EOF'\n", + "{\n", + " \"Mounts\":[\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/data\"\n", + " },\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/specs\"\n", + " },\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/results\"\n", + " },\n", + " {\n", + " \"source\": \"/home//.cache\",\n", + " \"destination\": \"/root/.cache\"\n", + " }\n", + " ],\n", + " \"DockerOptions\": {\n", + " \"shm_size\": \"16G\",\n", + " \"ulimits\": {\n", + " \"memlock\": -1,\n", + " \"stack\": 67108864\n", + " }\n", + " }\n", + "}\n", + "EOF" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can check the docker image versions and the tasks that tlt can perform. You can also check this out with a `tlt --help` or" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! tlt info --verbose" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set Relevant Paths" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NOTE: The following paths are set from the perspective of the TLT Docker.\n", + "\n", + "# The data is saved here\n", + "DATA_DIR = \"/data\"\n", + "SPECS_DIR = \"/specs\"\n", + "RESULTS_DIR = \"/results\"\n", + "\n", + "# Set your encryption key, and use the same key for all commands\n", + "KEY = 'tlt_encode'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that everything is setup, we would like to take a bit of time to explain the tlt interface for ease of use. The command structure can be broken down as follows: `tlt `
\n", + "\n", + "Let's see this in further detail." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Downloading Specs\n", + "TLT's Conversational AI Toolkit works off of spec files which make it easy to edit hyperparameters on the fly. We can proceed to downloading the spec files. The user may choose to modify/rewrite these specs, or even individually override them through the launcher. You can download the default spec files by using the `download_specs` command.
\n", + "\n", + "The -o argument indicating the folder where the default specification files will be downloaded, and -r that instructs the script where to save the logs. **Make sure the -o points to an empty folder!**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! tlt speech_to_text download_specs \\\n", + " -r $RESULTS_DIR/speech_to_text \\\n", + " -o $SPECS_DIR/speech_to_text" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the purposes of demonstration we will use the popular AN4 dataset. Let's download it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pre-Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This step converts the mp3 files into wav files and splits the data into training and testing sets. It also generates a \"meta-data\" file to be consumed by the dataloader for training and testing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! tlt speech_to_text dataset_convert \\\n", + " -e $SPECS_DIR/speech_to_text/dataset_convert_an4.yaml \\\n", + " source_data_dir=$DATA_DIR/an4 \\\n", + " target_data_dir=$DATA_DIR/an4_converted" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a listen to a sample audio file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# change path of the file here\n", + "import IPython.display as ipd\n", + "path = DATA_DIR + '/an4_converted/wavs/an268-mbmg-b.wav'\n", + "ipd.Audio(path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As previously discussed, there are two models we would like to discuss, the QuartzNet model and the Jasper Model. Training commands for both of them are simillar. Let's have a look!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have a very neat interface which allows the end user to configure training parameters from the command line interface.
\n", + "\n", + "The process of opening the training script; finding the parameters of interest(which might be spread across multiple files), making the changes needed, and double checking everything is being replaced by a much more easy to use and visible command line interface.\n", + "\n", + "For instance if the number of epochs are needed to be modified along with a change in learning rate, the user can add `trainer.max_epochs=10` and `optim.lr=0.02` and train the model. Sample commands are given below.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A list of some of the customizable parameters along with their default values is as follows:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "trainer:
\n", + "
    \n", + "
  • gpus: 1
    • \n", + "
  • num_nodes: 1
    • \n", + "
  • max_epochs: 5
    • \n", + "
  • max_steps: null
    • \n", + "
  • checkpoint_callback: false
    • \n", + "
\n", + "\n", + "training_ds:\n", + "
    \n", + "
  • sample_rate: 16000
    • \n", + "
  • labels: [\" \", \"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\", \"j\", \"k\", \"l\", \"m\",\n", + " \"n\", \"o\", \"p\", \"q\", \"r\", \"s\", \"t\", \"u\", \"v\", \"w\", \"x\", \"y\", \"z\", \"'\"]
    • \n", + "
  • batch_size: 32
    • \n", + "
  • trim_silence: true
    • \n", + "
  • max_duration: 16.7
    • \n", + "
  • shuffle: true
    • \n", + "
  • is_tarred: false
    • \n", + "
  • tarred_audio_filepaths: null
    • \n", + "
\n", + "\n", + "validation_ds:\n", + "
    \n", + "
  • sample_rate: 16000
    • \n", + "
  • labels: [\" \", \"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\", \"j\", \"k\", \"l\", \"m\",\n", + " \"n\", \"o\", \"p\", \"q\", \"r\", \"s\", \"t\", \"u\", \"v\", \"w\", \"x\", \"y\", \"z\", \"'\"]
    • \n", + "
  • batch_size: 32
    • \n", + "
  • shuffle: false
    • \n", + "
\n", + "optim:\n", + "
    \n", + "
  • name: novograd
    • \n", + "
  • lr: 0.01
    • \n", + "
  • betas: [0.8, 0.5]
    • \n", + "
  • weight_decay: 0.001
    • \n", + "
\n", + "\n", + "The steps below might take considerable time depending on the GPU being used. For best experience, we recommend using an A100 GPU." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For training aN ASR model in TLT, we use the `tlt speech_to_text train` command with the following args:\n", + "
    \n", + "
  • -e : Path to the spec file
    • \n", + "
  • -g : Number of GPUs to use
    • \n", + "
  • -r : Path to the results folder
    • \n", + "
  • -m : Path to the model
    • \n", + "
  • -k : User specified encryption key to use while saving/loading the model
    • \n", + "
  • Any overrides to the spec file eg. trainer.max_epochs
    • \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training QuartzNet 15x5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt speech_to_text train \\\n", + " -e $SPECS_DIR/speech_to_text/train_quartznet.yaml \\\n", + " -g 1 \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/quartznet/train \\\n", + " training_ds.manifest_filepath=$DATA_DIR/an4_converted/train_manifest.json \\\n", + " validation_ds.manifest_filepath=$DATA_DIR/an4_converted/test_manifest.json \\\n", + " trainer.max_epochs=1 \\\n", + " training_ds.num_workers=4 \\\n", + " validation_ds.num_workers=4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training Jasper 10x5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt speech_to_text train \\\n", + " -e $SPECS_DIR/speech_to_text/train_jasper.yaml \\\n", + " -g 1 \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/jasper/train \\\n", + " training_ds.manifest_filepath=$DATA_DIR/an4_converted/train_manifest.json \\\n", + " validation_ds.manifest_filepath=$DATA_DIR/an4_converted/test_manifest.json \\\n", + " trainer.max_epochs=1 \\\n", + " training_ds.num_workers=4 \\\n", + " validation_ds.num_workers=4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ASR evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a model trained, we need to check how well it performs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt speech_to_text evaluate \\\n", + " -e $SPECS_DIR/speech_to_text/evaluate.yaml \\\n", + " -g 1 \\\n", + " -k $KEY \\\n", + " -m $RESULTS_DIR/quartznet/train/checkpoints/trained-model.tlt \\\n", + " -r $RESULTS_DIR/quartznet/evaluate \\\n", + " test_ds.manifest_filepath=$DATA_DIR/an4_converted/test_manifest.json" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ASR finetuning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the model is trained and evaluated and there is a need for fine tuning, the following command can be used to fine tune the ASR model. This step can also be used for transfer learning by making changes in the `train.json` and `dev.json` files to add new data.\n", + "\n", + "The list for customizations is same as the training parameters with the exception for parameters which affect the model architecture. Also, instead of `training_ds` we have `finetuning_ds`\n", + "\n", + "Note: If you wish to proceed with a trained dataset for better inference results, you can find a .nemo model [here](\n", + "https://ngc.nvidia.com/catalog/collections/nvidia:nemotrainingframework).\n", + "\n", + "Simply re-name the .nemo file to .tlt and pass it through the finetune pipeline.\n", + "\n", + "**Note: The finetune spec files contain specifics to finetune the English model we just trained to Russian. If you wish to proceed with English, please make the changes in the spec file *finetune.yaml* which you can find in the SPEC_DIR folder you mapped. Be sure to delete older finetuning checkpoints if you choose to change the language after finetuning it as is.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt speech_to_text finetune \\\n", + " -e $SPECS_DIR/speech_to_text/finetune.yaml \\\n", + " -g 1 \\\n", + " -k $KEY \\\n", + " -m $RESULTS_DIR/quartznet/train/checkpoints/trained-model.tlt \\\n", + " -r $RESULTS_DIR/quartznet/finetune \\\n", + " finetuning_ds.manifest_filepath=$DATA_DIR/an4_converted/train_manifest.json \\\n", + " validation_ds.manifest_filepath=$DATA_DIR/an4_converted/test_manifest.json \\\n", + " trainer.max_epochs=1 \\\n", + " finetuning_ds.num_workers=20 \\\n", + " validation_ds.num_workers=20 \\\n", + " trainer.gpus=1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ASR model export" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With TLT, you can also export your model in a format that can deployed using Nvidia RIVA, a highly performant application framework for multi-modal conversational AI services using GPUs! The same command for exporting to ONNX can be used here. The only small variation is the configuration for `export_format` in the spec file!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Export to ONNX" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt speech_to_text export \\\n", + " -e $SPECS_DIR/speech_to_text/export.yaml \\\n", + " -g 1 \\\n", + " -k $KEY \\\n", + " -m $RESULTS_DIR/quartznet/train/checkpoints/trained-model.tlt \\\n", + " -r $RESULTS_DIR/quartznet/export \\\n", + " export_format=ONNX" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Export to Riva" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt speech_to_text export \\\n", + " -e $SPECS_DIR/speech_to_text/export.yaml \\\n", + " -g 1 \\\n", + " -k $KEY \\\n", + " -m $RESULTS_DIR/quartznet/train/checkpoints/trained-model.tlt \\\n", + " -r $RESULTS_DIR/quartznet/riva \\\n", + " export_format=RIVA \\\n", + " export_to=asr-model.ejrvs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ASR Inference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might have to work with the infer.yaml file to select the files you want for inference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt speech_to_text infer \\\n", + " -e $SPECS_DIR/speech_to_text/infer.yaml \\\n", + " -g 1 \\\n", + " -k $KEY \\\n", + " -m $RESULTS_DIR/quartznet/train/checkpoints/trained-model.tlt \\\n", + " -r $RESULTS_DIR/quartznet/infer \\\n", + " file_paths=[$path]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ASR Inference using ONNX" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TLT provides the capability to use the exported .eonnx model for inference. The command `tlt speech_to_text infer_onnx` is very similar to the inference command for .tlt models. Again, the inputs in the spec file used is just for demo purposes, you may choose to try out your custom input!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt speech_to_text infer_onnx \\\n", + " -e $SPECS_DIR/speech_to_text/infer_onnx.yaml \\\n", + " -g 1 \\\n", + " -k $KEY \\\n", + " -m $RESULTS_DIR/quartznet/export/exported-model.eonnx \\\n", + " -r $RESULTS_DIR/infer_onnx \\\n", + " file_paths=[$path]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What's Next?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " You could use TLT to build custom models for your own applications, or you could deploy the custom model to Nvidia Riva!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[Home Page](../START_HERE_RIVA_BOOTCAMP.ipynb)\n", + "\n", + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[1]\n", + "[2](speech-to-text-deployment.ipynb)\n", + "[3](speechtotext-Exercise.ipynb)\n", + "     \n", + "     \n", + "     \n", + "     \n", + "[Next Notebook](speech-to-text-deployment.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/STT/speechtotext-Exercise.ipynb b/ai/RIVA/English/Python/jupyter_notebook/STT/speechtotext-Exercise.ipynb new file mode 100644 index 000000000..2d3565ca2 --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/STT/speechtotext-Exercise.ipynb @@ -0,0 +1,282 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Previous Notebook](speech-to-text-deployment.ipynb)\n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[Home Page](../START_HERE_RIVA_BOOTCAMP.ipynb)\n", + "\n", + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[1](speech-to-text-training.ipynb)\n", + "[2](speech-to-text-deployment.ipynb)\n", + "[3]\n", + "     \n", + "     \n", + "     \n", + "     " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TLT - ASR Riva Exercise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Transfer Learning Toolkit](https://developer.nvidia.com/transfer-learning-toolkit) (TLT) provides the capability to export your model in a format that can deployed using [NVIDIA Riva](https://developer.nvidia.com/riva), a highly performant application framework for multi-modal conversational AI services using GPUs. \n", + "\n", + "This tutorial explores taking an .riva model, the result of `tlt speech_to_text` command, and leveraging the Riva ServiceMaker framework to aggregate all the necessary artifacts for Riva deployment to a target environment. Once the model is deployed in Riva, you can issue inference requests to the server. We will demonstrate how quick and straightforward this whole process is.\n", + "\n", + "At the end of this tutorial is a quick exercise to give you a hands-on experience of using the Jarvis ASR Service." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Learning Objectives\n", + "In this notebook, you will learn how to: \n", + "- Send inference requests from a demo client using Riva API bindings.\n", + "- Batch transcribe a set of files using the Riva ASR API" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Pre-requisites" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To follow along, please make sure:\n", + "- You have completed the previous notebook or have a pretrained model and environment ready to deploy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Start Riva Server\n", + "### note: this step is for reference, we have already covered this in the setup phase\n", + "Once the model repository is generated, we are ready to start the Riva server. From this step onwards you need to download the Riva QuickStart Resource from NGC. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### config.sh snippet\n", + "```\n", + "# Enable or Disable Riva Services \n", + "service_enabled_asr=true ## MAKE CHANGES HERE\n", + "service_enabled_nlp=false ## MAKE CHANGES HERE\n", + "service_enabled_tts=false ## MAKE CHANGES HERE\n", + "\n", + "# Specify one or more GPUs to use\n", + "# specifying more than one GPU is currently an experimental feature, and may result in undefined behaviours.\n", + "gpus_to_use=\"device=0\"\n", + "\n", + "# Specify the encryption key to use to deploy models\n", + "MODEL_DEPLOY_KEY=\"tlt_encode\" ## MAKE CHANGES HERE\n", + "\n", + "# Locations to use for storing models artifacts\n", + "#\n", + "# If an absolute path is specified, the data will be written to that location\n", + "# Otherwise, a docker volume will be used (default).\n", + "#\n", + "# riva_init.sh will create a `rmir` and `models` directory in the volume or\n", + "# path specified. \n", + "#\n", + "# RMIR ($riva_model_loc/rmir)\n", + "# Riva uses an intermediate representation (RMIR) for models\n", + "# that are ready to deploy but not yet fully optimized for deployment. Pretrained\n", + "# versions can be obtained from NGC (by specifying NGC models below) and will be\n", + "# downloaded to $riva_model_loc/rmir by `riva_init.sh`\n", + "# \n", + "# Custom models produced by NeMo or TLT and prepared using riva-build\n", + "# may also be copied manually to this location $(riva_model_loc/rmir).\n", + "#\n", + "# Models ($riva_model_loc/models)\n", + "# During the riva_init process, the RMIR files in $riva_model_loc/rmir\n", + "# are inspected and optimized for deployment. The optimized versions are\n", + "# stored in $riva_model_loc/models. The riva server exclusively uses these\n", + "# optimized versions.\n", + "riva_model_loc=\"\" ## MAKE CHANGES HERE (Replace with MODEL_LOC) \n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "---\n", + "## Run Inference\n", + "Once the Riva server is up and running with your models, you can send inference requests querying the server. \n", + "\n", + "To send GRPC requests, you can install Riva Python API bindings for client. This is available as a pip .whl with the QuickStart.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect to Riva server and run inference\n", + "Now we actually query the Riva server, let's get started. The following cell queries the riva server(using grpc) to yield a result." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import argparse\n", + "import grpc\n", + "import time\n", + "import riva_api.audio_pb2 as ra\n", + "import riva_api.riva_asr_pb2 as rasr\n", + "import riva_api.riva_asr_pb2_grpc as rasr_srv\n", + "import wave\n", + "\n", + "audio_file = \"\"\n", + "##change the following to reflect your parameters\n", + "server = \"localhost:50051\"\n", + "\n", + "wf = wave.open(audio_file, 'rb')\n", + "with open(audio_file, 'rb') as fh:\n", + " data = fh.read()\n", + "\n", + "channel = grpc.insecure_channel(server)\n", + "client = rasr_srv.RivaSpeechRecognitionStub(channel)\n", + "config = rasr.RecognitionConfig(\n", + " encoding=ra.AudioEncoding.LINEAR_PCM,\n", + " sample_rate_hertz=wf.getframerate(),\n", + " language_code=\"en-US\",\n", + " max_alternatives=1,\n", + " enable_automatic_punctuation=False,\n", + " audio_channel_count=1\n", + ")\n", + "\n", + "request = rasr.RecognizeRequest(config=config, audio=data)\n", + "\n", + "response = client.Recognize(request)\n", + "print(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exercise: Transcribe the following files in the data folder: \n", + "- sp01sn15.wav\n", + "- sp02sn15.wav\n", + "- sp03sn15.wav\n", + "- sp04sn15.wav\n", + "- sp05sn15.wav\n", + "- sp06sn15.wav\n", + "\n", + "The following snippet and template will be helpful" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##code snippet to inspect an audio file and listen to it\n", + "\n", + "import IPython.display as ipd\n", + "import io\n", + "import librosa\n", + "\n", + "# read in an audio file from local disk\n", + "path = \"/path/to/sample.wav\"\n", + "audio, sr = librosa.core.load(path, sr=None)\n", + "with io.open(path, 'rb') as fh:\n", + " content = fh.read()\n", + "ipd.Audio(path)\n", + "\n", + "print (sr) ##sampling rate, comment out to listen to file\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import argparse\n", + "import grpc\n", + "import time\n", + "import riva_api.audio_pb2 as ra\n", + "import riva_api.riva_asr_pb2 as rasr\n", + "import riva_api.riva_asr_pb2_grpc as rasr_srv\n", + "import wave\n", + "\n", + "server = : \n", + "\n", + "## for each file in the list, do the following:\n", + "\n", + "## define channel\n", + "\n", + "## define client\n", + "\n", + "## define configuration\n", + "\n", + "## note: you may need to change this depending on the file. \n", + "## you can inspect a file with the code snippet in the previous cell\n", + "\n", + "## send a request to the server\n", + "\n", + "## call the recognize method on the request object\n", + "\n", + "## append result to results text object/file\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/TC/text-classification-Exercise.ipynb b/ai/RIVA/English/Python/jupyter_notebook/TC/text-classification-Exercise.ipynb new file mode 100644 index 000000000..74fb968fa --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/TC/text-classification-Exercise.ipynb @@ -0,0 +1,242 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Text Classification in Riva - Exercise\n", + "\n", + "\n", + "\n", + "## Learning Objectives\n", + "In this notebook, you will learn how to: \n", + "- Use Riva ServiceMaker to take a TLT exported .riva and convert it to .rmir\n", + "- Deploy the model(s) locally on the Riva Server\n", + "- Send inference requests from a demo client using Riva API bindings and use the service to perform some sample inferencing..\n", + "\n", + "## Pre-requisites\n", + "To follow along, please make sure:\n", + "- You have access to NVIDIA NGC, and are able to download the Riva Quickstart [resources]https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart/)\n", + "- Have an .riva model file that you wish to deploy. You can obtain this from `tlt export` (with `export_format=RIVA`). Please refer the tutorial on *Text Classification using Transfer Learning Toolkit* for more details on training and exporting an .riva model which was covered in the previous notebook.\n", + "- Have followed the steps in the setup notebook to setup and deploy an instance of the RIVA Framework and have a running RIVA server\n", + "- Have followed the steps show in the training and deployment notebooks if you are using a custom model for inferencing.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### . Riva-deploy\n", + "\n", + "The deployment tool takes as input one or more Riva Model Intermediate Representation (RMIR) files and a target model repository directory. It creates an ensemble configuration specifying the pipeline for the execution and finally writes all those assets to the output model repository directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Syntax: riva-deploy -f dir-for-rmir/model.rmir:key output-dir-for-repository\n", + "! docker run --rm --gpus 0 -v $MODEL_LOC:/data $RIVA_SM_CONTAINER -- \\\n", + " riva-deploy -f /data/tc-model.rmir:$KEY /data/models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Start Riva Server\n", + "Once the model repository is generated, we are ready to start the Riva server. This step is covered in the setup notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run Inference\n", + "Once the Riva server is up and running with your models, you can send inference requests querying the server. \n", + "\n", + "To send GRPC requests, you can install Riva Python API bindings for client. This is available as a pip .whl with the QuickStart." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following code sample shows how you can perform inference using Riva Python API gRPC bindings:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import grpc\n", + "import argparse\n", + "import os\n", + "import riva_api.riva_nlp_pb2 as rnlp\n", + "import riva_api.riva_nlp_pb2_grpc as rnlp_srv\n", + "\n", + "\n", + "class BertTextClassifyClient(object):\n", + " def __init__(self, grpc_server, model_name):\n", + " # generate the correct model based on precision and whether or not ensemble is used\n", + " print(\"Using model: {}\".format(model_name))\n", + "\n", + " self.model_name = model_name\n", + " self.channel = grpc.insecure_channel(grpc_server)\n", + " self.riva_nlp = rnlp_srv.RivaLanguageUnderstandingStub(self.channel)\n", + "\n", + " self.has_bos_eos = False\n", + "\n", + " # use the text_classification network to return top-1 classes for intents/sequences\n", + " def postprocess_labels_server(self, ct_response):\n", + " results = []\n", + "\n", + " for i in range(0, len(ct_response.results)):\n", + " intent_str = ct_response.results[i].labels[0].class_name\n", + " intent_conf = ct_response.results[i].labels[0].score\n", + "\n", + " results.append((intent_str, intent_conf))\n", + "\n", + " return results\n", + "\n", + " # accept a list of strings, return a list of tuples ('intent', scores)\n", + " def run(self, input_strings):\n", + " if isinstance(input_strings, str):\n", + " # user probably passed a single string instead of a list/iterable\n", + " input_strings = [input_strings]\n", + "\n", + " # get intent of the query\n", + " request = rnlp.TextClassRequest()\n", + " request.model.model_name = self.model_name\n", + " for q in input_strings:\n", + " request.text.append(q)\n", + " ct_response = self.riva_nlp.ClassifyText(request)\n", + "\n", + " return self.postprocess_labels_server(ct_response)\n", + "\n", + "\n", + "def run_text_classify(server, model, query):\n", + " print(\"Client app to test text classification on Riva\")\n", + " client = BertTextClassifyClient(server, model_name=model)\n", + " result = client.run(query)\n", + " print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Model Name will depend on the dataset and the domain on which the model was trained. \n", + "# Please check `docker logs ` and replace is accordingly (There will \n", + "# be a table of models with their status displayed next to them) Check the documentation\n", + "# for more information.\n", + "\n", + "run_text_classify(server=\"localhost:50051\",\n", + " model=\"\",\n", + " query=\"How is the weather tomorrow?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`NOTE`: You could also run the above inference code from inside the Riva Client container. The QuickStart provides a script `riva_start_client.sh` to run the container. It has more examples for different services." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can stop all docker container before shutting down the jupyter kernel. Caution: The following command will stop all running containers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! docker stop $(docker ps -a -q)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The following shows how to the the Python API for text classification" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Submit a TextClassRequest for text classification.\n", + "# Riva NLP comes with a default text_classification domain called \"domain_misty\" which consists of \n", + "# 4 classes: meteorology, personality, weather and nomatch\n", + "\n", + "request = rnlp.TextClassRequest()\n", + "request.model.model_name = \"riva_text_classification_domain\" # If you have deployed a custom model \n", + " # with the `--domain_name` parameter in ServiceMaker's `riva-build` command \n", + " # then you should use \"riva_text_classification_\"\n", + " # where is the name you provided to the \n", + " # domain_name parameter. In this case the domain_name is \"domain\"\n", + "request.text.append(\"Is it going to snow in Burlington, Vermont tomorrow night?\")\n", + "request.text.append(\"What causes rain?\")\n", + "request.text.append(\"What is your favorite season?\")\n", + "ct_response = riva_nlp.ClassifyText(request)\n", + "print(ct_response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise\n", + "\n", + "Use the Text classification Service from RIVA to classify the following sentences:\n", + "- There are many people who would like to live in a sunny location.\n", + "- Where are the hottest deserts located?\n", + "- What do you like to do for fun in your free time?\n", + "- What is the capital of Peru?\n", + "- Will it rain tomorrow in San Francisco?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/TC/text-classification-deployment.ipynb b/ai/RIVA/English/Python/jupyter_notebook/TC/text-classification-deployment.ipynb new file mode 100644 index 000000000..5909352ee --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/TC/text-classification-deployment.ipynb @@ -0,0 +1,220 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deploying Text Classification Model in Riva\n", + "\n", + "[Transfer Learning Toolkit](https://developer.nvidia.com/transfer-learning-toolkit) (TLT) provides the capability to export your model in a format that can deployed using [NVIDIA Riva](https://developer.nvidia.com/riva), a highly performant application framework for multi-modal conversational AI services using GPUs. \n", + "\n", + "This tutorial explores taking an .riva model, the result of `tlt text_classification export` command, and leveraging the Riva ServiceMaker framework to aggregate all the necessary artifacts for Riva deployment to a target environment. Once the model is deployed in Riva, you can issue inference requests to the server. We will demonstrate how quick and straightforward this whole process is. \n", + "\n", + "## Learning Objectives\n", + "In this notebook, you will learn how to: \n", + "- Use Riva ServiceMaker to take a TLT exported .riva and convert it to .rmir\n", + "- Deploy the model(s) locally on the Riva Server\n", + "- Send inference requests from a demo client using Riva API bindings..\n", + "\n", + "## Pre-requisites\n", + "To follow along, please make sure:\n", + "- You have access to NVIDIA NGC, and are able to download the Riva Quickstart [resources]https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart/)\n", + "- Have an .riva model file that you wish to deploy. You can obtain this from `tlt export` (with `export_format=RIVA`). Please refer the tutorial on *Text Classification using Transfer Learning Toolkit* for more details on training and exporting an .riva model which was covered in the previous notebook.\n", + "- Have followed the steps in the setup notebook to setup and deploy an instance of the RIVA Framework and have a running RIVA server\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### . Riva-deploy\n", + "\n", + "The deployment tool takes as input one or more Riva Model Intermediate Representation (RMIR) files and a target model repository directory. It creates an ensemble configuration specifying the pipeline for the execution and finally writes all those assets to the output model repository directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Syntax: riva-deploy -f dir-for-rmir/model.rmir:key output-dir-for-repository\n", + "! docker run --rm --gpus 0 -v $MODEL_LOC:/data $RIVA_SM_CONTAINER -- \\\n", + " riva-deploy -f /data/tc-model.rmir:$KEY /data/models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Start Riva Server\n", + "Once the model repository is generated, we are ready to start the Riva server. This step is covered in the setup notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run Inference\n", + "Once the Riva server is up and running with your models, you can send inference requests querying the server. \n", + "\n", + "To send GRPC requests, you can install Riva Python API bindings for client. This is available as a pip .whl with the QuickStart." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# IMPORTANT: Set the name of the whl file\n", + "RIVA_API_WHL = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Install client API bindings\n", + "!cd $RIVA_DIR && pip install $RIVA_API_WHL" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following code sample shows how you can perform inference using Riva Python API gRPC bindings:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import grpc\n", + "import argparse\n", + "import os\n", + "import riva_api.riva_nlp_pb2 as rnlp\n", + "import riva_api.riva_nlp_pb2_grpc as rnlp_srv\n", + "\n", + "\n", + "class BertTextClassifyClient(object):\n", + " def __init__(self, grpc_server, model_name):\n", + " # generate the correct model based on precision and whether or not ensemble is used\n", + " print(\"Using model: {}\".format(model_name))\n", + "\n", + " self.model_name = model_name\n", + " self.channel = grpc.insecure_channel(grpc_server)\n", + " self.riva_nlp = rnlp_srv.RivaLanguageUnderstandingStub(self.channel)\n", + "\n", + " self.has_bos_eos = False\n", + "\n", + " # use the text_classification network to return top-1 classes for intents/sequences\n", + " def postprocess_labels_server(self, ct_response):\n", + " results = []\n", + "\n", + " for i in range(0, len(ct_response.results)):\n", + " intent_str = ct_response.results[i].labels[0].class_name\n", + " intent_conf = ct_response.results[i].labels[0].score\n", + "\n", + " results.append((intent_str, intent_conf))\n", + "\n", + " return results\n", + "\n", + " # accept a list of strings, return a list of tuples ('intent', scores)\n", + " def run(self, input_strings):\n", + " if isinstance(input_strings, str):\n", + " # user probably passed a single string instead of a list/iterable\n", + " input_strings = [input_strings]\n", + "\n", + " # get intent of the query\n", + " request = rnlp.TextClassRequest()\n", + " request.model.model_name = self.model_name\n", + " for q in input_strings:\n", + " request.text.append(q)\n", + " ct_response = self.riva_nlp.ClassifyText(request)\n", + "\n", + " return self.postprocess_labels_server(ct_response)\n", + "\n", + "\n", + "def run_text_classify(server, model, query):\n", + " print(\"Client app to test text classification on Riva\")\n", + " client = BertTextClassifyClient(server, model_name=model)\n", + " result = client.run(query)\n", + " print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Model Name will depend on the dataset and the domain on which the model was trained. \n", + "# Please check `docker logs ` and replace is accordingly (There will \n", + "# be a table of models with their status displayed next to them) Check the documentation\n", + "# for more information.\n", + "\n", + "run_text_classify(server=\"localhost:50051\",\n", + " model=\"\",\n", + " query=\"How is the weather tomorrow?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`NOTE`: You could also run the above inference code from inside the Riva Client container. The QuickStart provides a script `riva_start_client.sh` to run the container. It has more examples for different services." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can stop all docker container before shutting down the jupyter kernel. Caution: The following command will stop all running containers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! docker stop $(docker ps -a -q)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What's next?\n", + "You could train your own custom models in TLT and deploy them in Riva! You could scale up your deployment using Kubernetes with the Riva AI Services Helm Chart, which will pull the relevant Images and download model artifacts from NGC, generate the model repository, start and expose the Riva speech services." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/jupyter_notebook/TC/text-classification-training.ipynb b/ai/RIVA/English/Python/jupyter_notebook/TC/text-classification-training.ipynb new file mode 100644 index 000000000..58f712ffb --- /dev/null +++ b/ai/RIVA/English/Python/jupyter_notebook/TC/text-classification-training.ipynb @@ -0,0 +1,757 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[Home Page](../START_HERE_RIVA_HACKATHON.ipynb)\n", + "\n", + "     \n", + "     \n", + "     \n", + "     \n", + "     \n", + "   \n", + "[1]\n", + "[2](text-classification-deployment.ipynb)\n", + "[3](text-classification-Exercise.ipynb)\n", + "     \n", + "     \n", + "     \n", + "     \n", + "[Next Notebook](text-classification-deployment.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Text Classification using Transfer Learning Toolkit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "In this notebook, you will learn how to leverage the simplicity and convenience of TLT to:\n", + "- Take a [BERT](https://arxiv.org/pdf/1810.04805.pdf) Text Classification model, [**Train**](#training) and [**Finetune**](#ft) it on the [SST-2 dataset](https://dl.fbaipublicfiles.com/glue/data/SST-2.zip)\n", + "- Run [**Evaluation**](#evaluation) and [**Inference**](#inference)\n", + "- [**Export**](#export-onnx) the model for the [ONNX](https://onnx.ai/) format, or [export](#export-riva) to deployment on [Riva](https://developer.nvidia.com/riva).\n", + "\n", + "The earlier sections in the notebook give a brief introduction to the Text Classification task and the SST-2 dataset. If you are already familiar with these, and want to jump right into the meat of the matter, you can start at section on [Download and preprocess the dataset](#prepare-data)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pre-requisites\n", + "\n", + "Please make sure that the steps in the setup notebook to install and deploy TLT are performed correctly\n", + "\n", + "For ease of use, please install TLT inside a python virtual environment. We recommend performing this step first and then launching the notebook from the virtual environment.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Text Classification\n", + "\n", + "### Task Description\n", + "Text Classification is one of the most common tasks in NLP, which is the process of categorizing the text into a group of words. By using NLP, text classification can automatically analyze text and then assign a set of predefined tags or categories based on its context. It is applied in a wide variety of applications, including sentiment analysis, spam filtering, news categorization, domain/intent detection for dialogue systems, etc. The dataset we use in this notebook, [SST-2](https://dl.fbaipublicfiles.com/glue/data/SST-2.zip), pose this as a sentiment analysis task, i.e. given a piece of text, the goal is to estimate its sentiment polarity based solely on its content. \n", + "\n", + "For every entry in the training dataset, we predict the sentiment label of the sentence." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transfer Learning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Transfer learning is a technique of extracting learned features from an existing neural network to a new one. It is a commonly used training technique where you use a model trained on one task and re-train to use it on a different task, when creating a large training dataset is not feasible. \n", + "\n", + "**Transfer Learning Toolkit (TLT)** is a simple and easy-to-use python based AI toolkit for taking purpose-built pre-trained AI models and customizing them with users' own data.\n", + "\n", + "\n", + "![Transfer Learning Toolkit](https://developer.nvidia.com/sites/default/files/akamai/embedded-transfer-learning-toolkit-software-stack-1200x670px.png)\n", + "\n", + "\n", + "Let's see this in action with a use case for text classification.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Datasets\n", + "This model supports text classification problems such as sentiment analysis or domain/intent detection for dialogue systems, as long as the data follows the format specified below.\n", + "\n", + "The Text Classification Model requires the data to be stored in TAB separated files (.tsv) with two columns of sentence and label. Each line of the data file contains text sequences, where words are separated with spaces and label separated with [TAB], i.e.:\n", + "\n", + " [WORD] [SPACE] [WORD] [SPACE] [WORD] [TAB] [LABEL]\n", + "\n", + "For example:\n", + "\n", + " hide new secretions from the parental units [TAB] 0\n", + "\n", + " that loves its characters and communicates something rather beautiful about human nature [TAB] 1\n", + "\n", + " ...\n", + "If your dataset is stored in another format, you need to convert it to this format to use the Text Classification Model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The SST-2 Dataset\n", + "\n", + "The Stanford Sentiment Treebank dataset contains 215,154 phrases with fine-grained sentiment labels in the parse trees of 11,855 sentences from movie reviews. Model performances are evaluated either based on a fine-grained (5-way) or binary classification model based on accuracy. The [SST-2](https://dl.fbaipublicfiles.com/glue/data/SST-2.zip) Binary classification version is identical to SST-1, but with neutral reviews deleted.\n", + "\n", + "The SST-2 format consists of a .tsv file for each dataset split, i.e. train, dev, and test data. Each entry has a space-separated sentence, followed by a tab and a label.\n", + "\n", + "For example:\n", + "\n", + " sentence\tlabel\n", + " \n", + " hexcruciatingly unfunny and pitifully unromantic \t0\n", + " \n", + " enriched by an imaginatively mixed cast of antic spirits \t1\n", + " \n", + " ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Download and preprocess the dataset \n", + "\n", + "We provide a function, download_sst2, for downloading the data. Please refer to the TLT workflow section for the [data preprocessing and conversion part](#dataset-convert)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Downloading the dataset\n", + "\n", + "For convenience, you may use the code below to download the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import urllib.request\n", + "import os\n", + "\n", + "# simple utility function to download the SST-2 dataset\n", + "def download_sst2(save_path):\n", + "\n", + " if not os.path.exists(save_path):\n", + " os.makedirs(save_path)\n", + " \n", + " # url and filename of the dataset\n", + " download_urls = {\n", + " 'https://dl.fbaipublicfiles.com/glue/data/SST-2.zip': 'SST-2.zip',\n", + " }\n", + " \n", + " for item in download_urls:\n", + " url = item\n", + " file = download_urls[item]\n", + " print('Downloading:', url)\n", + " if os.path.isfile(save_path + '/' + file):\n", + " print('** Download file already exists, skipping download **')\n", + " else:\n", + " response = urllib.request.urlopen(url)\n", + " with open(save_path + '/' + file, \"wb\") as handle:\n", + " handle.write(response.read())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# IMPORTANT Set the following path\n", + "DATA_DOWNLOAD_DIR = \"\"\n", + "\n", + "# This will download the SST-2 dataset\n", + "download_sst2(DATA_DOWNLOAD_DIR)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! unzip -o $DATA_DOWNLOAD_DIR/SST-2.zip -d {DATA_DOWNLOAD_DIR}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After executing the cell below, $DATA_DIR/SST-2 will contain the following 3 files and 1 folder:\n", + "- dev.tsv\n", + "- test.tsv\n", + "- train.tsv\n", + "- original" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Verify that the data is present\n", + "!ls $DATA_DOWNLOAD_DIR/SST-2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TLT workflow\n", + "The rest of the notebook shows what a sample TLT workflow looks like.\n", + "\n", + "### Setting TLT Mounts\n", + "\n", + "Now that our dataset has been downloaded, an important step in using TLT is to set up the directory mounts. The TLT launcher uses docker containers under the hood, and **for our data and results directory to be visible to the docker, they need to be mapped**. \n", + "\n", + "The launcher can be configured using the config file `~/.tlt_mounts.json`. Apart from the mounts, you can also configure additional options like the Environment Variables and amount of Shared Memory available to the TLT launcher.
\n", + "\n", + "`IMPORTANT NOTE:` The code below creates a sample `.tlt_mounts.json` file. Here, we can map directories in which we save the data, specs, results and cache. You should configure it for your specific case such your these directories are correctly visible to the docker container. **Please also ensure that the source directories exist on your machine!**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%bash\n", + "tee ~/.tlt_mounts.json <<'EOF'\n", + "{\n", + " \"Mounts\":[\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/data\"\n", + " },\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/specs\"\n", + " },\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/results\"\n", + " },\n", + " {\n", + " \"source\": \"\",\n", + " \"destination\": \"/root/.cache\"\n", + " }\n", + " ]\n", + "}\n", + "EOF" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make sure the source directories exist, if not, create them\n", + "! mkdir \n", + "! mkdir \n", + "! mkdir " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The rest of the notebook exemplifies the simplicity of the TLT workflow. \n", + "\n", + "Users with basic knowledge of Deep Learning can get started building their own custom models using a simple specification file. It's essentially just one command each to run data preprocessing, training, fine-tuning, evaluation, inference, and export! All configurations happen through YAML spec files.
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configuration/Specification Files\n", + "\n", + "The essence of all commands in TLT lies in the **YAML specification files**. There are sample specification files already available for you to use directly or as reference to create your own.\n", + "\n", + "Through these specification files, you can tune many knobs like the model, dataset, hyperparameters, optimizers, etc.\n", + "\n", + "Each command (like download_and_convert, train, finetune, evaluate, infer, etc.) should have a dedicated specification file with configurations pertinent to it. \n", + "\n", + "Here is an example of the training spec file:\n", + "\n", + "\n", + "```\n", + "trainer:\n", + " max_epochs: 100\n", + "\n", + "# Name of the .tlt file where trained model will be saved.\n", + "save_to: trained-model.tlt\n", + "\n", + "model:\n", + " # Labels that will be used to \"decode\" predictions.\n", + " labels:\n", + " \"0\": \"negative\"\n", + " \"1\": \"positive\"\n", + "\n", + " tokenizer:\n", + " tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece\n", + " vocab_file: null # path to vocab file \n", + " tokenizer_model: null # only used if tokenizer is sentencepiece\n", + " special_tokens: null\n", + "\n", + " language_model:\n", + " pretrained_model_name: bert-base-uncased\n", + " lm_checkpoint: null\n", + " config_file: null # json file, precedence over config\n", + " config: null \n", + "\n", + " classifier_head:\n", + " # This comes directly from number of labels/target classes.\n", + " num_output_layers: 2\n", + " fc_dropout: 0.1\n", + "\n", + "\n", + "training_ds:\n", + " file_path: ???\n", + " batch_size: 64\n", + " shuffle: true\n", + " num_samples: -1 # number of samples to be considered, -1 means all the dataset\n", + "\n", + "...\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setting relevant paths" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NOTE: The following paths are set from the perspective of the TLT Docker. \n", + "\n", + "# The data is saved here\n", + "DATA_DIR = '/data'\n", + "\n", + "# The configuration files are stored here\n", + "SPECS_DIR = '/specs/text_classification'\n", + "\n", + "# The results are saved at this path\n", + "RESULTS_DIR = '/results/text_classification'\n", + "\n", + "# Set your encryption key, and use the same key for all commands\n", + "KEY = 'tlt_encode'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that everything is setup, we would like to take a bit of time to explain the tlt interface for ease of use. The command structure can be broken down as follows: `tlt `
\n", + "\n", + "Let's see this in further detail." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Downloading Specs\n", + "We can proceed to downloading the spec files. The user may choose to modify/rewrite these specs, or even individually override them through the launcher. You can download the default spec files by using the `download_specs` command.
\n", + "\n", + "The -o argument indicating the folder where the default specification files will be downloaded, and -r that instructs the script where to save the logs. **Make sure the -o points to an empty folder!**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tlt text_classification download_specs \\\n", + " -r $RESULTS_DIR \\\n", + " -o $SPECS_DIR" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Dataset Convert\n", + "\n", + "The dataset conversion feature currently supports SST-2 and IMDB dataset. You need to specify the following parameters in the command:\n", + "\n", + "- dataset_name: \"sst2\" or \"imdb\"\n", + "- source_data_dir: directory path for the downloaded dataset\n", + "- target_data_dir: directory path for the processed dataset\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 1. For BERT dataset conversion:\n", + "!tlt text_classification dataset_convert \\\n", + " -e $SPECS_DIR/dataset_convert.yaml \\\n", + " -r $SPECS_DIR/dataset_convert \\\n", + " dataset_name=sst2 source_data_dir=$DATA_DIR/SST-2 target_data_dir=$DATA_DIR/sst2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Training\n", + "\n", + "Training a model using TLT is as simple as configuring your spec file and running the train command. The code cell below uses the default train.yaml available for users as reference. It is configured by default to use the `bert-base-uncased` pretrained model. Additionally, these configurations could easily be overridden using the tlt-launcher CLI as shown below. For instance, below we override the `training_ds.file`, `validation_ds.file`, `trainer.max_epochs`, `training_ds.num_workers` and `validation_ds.num_workers` configurations to suit our needs. We encourage you to take a look at the .yaml spec files we provide!
\n", + "\n", + "In order to get good results, you need to train for 20-50 epochs (depends on the size of the data). Training with 1 epoch in the tutorial is just for demonstration purposes.\n", + "\n", + "\n", + "For training a Text Classification model in TLT, we use the `tlt text_classification train` command with the following args:\n", + "- `-e`: Path to the spec file\n", + "- `-g`: Number of GPUs to use\n", + "- `-k`: User specified encryption key to use while saving/loading the model\n", + "- `-r`: Path to a folder where the outputs should be written. Make sure this is mapped in tlt_mounts.json\n", + "- Any overrides to the spec file eg. trainer.max_epochs\n", + "\n", + "More details about these arguments are present in the [TLT Getting Started Guide](https://docs.nvidia.com/tlt/tlt-user-guide/index.html)
\n", + "`NOTE:` All file paths corresponds to the destination mounted directory that is visible in the TLT docker container used in backend.
\n", + "\n", + "Also worth noting is that the first time you run training on the dataset, it will run pre-processing and save that processed data in the same directory as the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 2. For BERT training on SST-2:\n", + "!tlt text_classification train \\\n", + " -e $SPECS_DIR/train.yaml \\\n", + " -g 1 \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/train \\\n", + " training_ds.file_path=$DATA_DIR/sst2/train.tsv \\\n", + " validation_ds.file_path=$DATA_DIR/sst2/dev.tsv \\\n", + " model.class_labels.class_labels_file=$DATA_DIR/sst2/label_ids.csv \\\n", + " trainer.max_epochs=1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The train command produces a .tlt file called `trained-model.tlt` saved at `$RESULTS_DIR/train/checkpoints/trained-model.tlt`. This file can be fed directly into the fine-tuning stage as we see in the next block." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Other tips and tricks:\n", + "- To accelerate the training without loss of quality, it is possible to train with these parameters: `trainer.amp_level=\"O1\"` and `trainer.precision=16` for reduced precision.\n", + "- The batch size (`training_ds.batch_size`) may influence the validation accuracy. Larger batch sizes are faster to train with, however, you may get slightly better results with smaller batches." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Fine-Tuning\n", + "\n", + "The command for fine-tuning is very similar to that of training. Instead of `tlt text_classification train`, we use `tlt text_classification finetune` instead. We also specify the spec file corresponding to fine-tuning. All commands in TLT follow a similar pattern, streamlining the workflow even further!\n", + "\n", + "`Note:` we use SST-2 dataset here to showcase how to use the fine-tuning command, however we recommend bringing your own data for fine-tuning on the pre-trained models. You would need to make sure your dataset is downloaded and pre-processed so that it's ready for use.\n", + "\n", + "`Note:` If you wish to proceed with a trained dataset for better inference results, you can find a .nemo model [here](\n", + "https://ngc.nvidia.com/catalog/collections/nvidia:nemotrainingframework).\n", + "\n", + "Simply re-name the .nemo file to .tlt and pass it through the finetune pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 3. For BERT finetuning on SST-2:\n", + "!tlt text_classification finetune \\\n", + " -e $SPECS_DIR/finetune.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/train/checkpoints/trained-model.tlt \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/finetune \\\n", + " finetuning_ds.file_path=$DATA_DIR/sst2/train.tsv \\\n", + " validation_ds.file_path=$DATA_DIR/sst2/dev.tsv \\\n", + " trainer.max_epochs=1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This command will generate a fine-tuned model `finetuned-model.tlt` at `$RESULTS_DIR/finetune/checkpoints`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Evaluation\n", + "The evaluation spec .yaml is as simple as:\n", + "\n", + "```\n", + "test_ds:\n", + " file: ??? # e.g. $DATA_DIR/test.tsv\n", + " batch_size: 32\n", + " shuffle: false\n", + " num_samples: 500\n", + "```\n", + "\n", + "Below, we use `tlt text_classification evaluate` and override the test data configuration by specifying `test_ds.file_path`. Other arguments follow the same pattern as before!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 4. For BERT evaluation on SST-2:\n", + "!tlt text_classification evaluate \\\n", + " -e $SPECS_DIR/evaluate.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/train/checkpoints/trained-model.tlt \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/evaluate \\\n", + " test_ds.file_path=$DATA_DIR/SST-2/dev.tsv \\\n", + " test_ds.batch_size=32" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On evaluating the model you will get some results, and based on that you can either retrain the model for more epochs, or continue with the inference." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Inference\n", + "Inference using a .tlt trained or fine-tuned model uses the `tlt text_classification infer` command.
\n", + "The infer.yaml is also straightforward, which includes some \"simulated\" user input, here we start with a batch of four samples. \n", + "\n", + "- \"by the end of no such thing the audience , like beatrice , has a watchful affection for the monster .\"\n", + "- \"director rob marshall went out gunning to make a great one .\"\n", + "- \"uneasy mishmash of styles and genres .\"\n", + "- \"I love exotic science fiction / fantasy movies but this one was very unpleasant to watch . Suggestions and images of child abuse , mutilated bodies (live or dead) , other gruesome scenes , plot holes , boring acting made this a regretable experience , The basic idea of entering another person's mind is not even new to the movies or TV (An Outer Limits episode was better at exploring this idea) . i gave it 4 / 10 since some special effects were nice .\"\n", + "\n", + "We encourage you to try out custom inputs as an exercise." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 5. For BERT inference on user data:\n", + "!tlt text_classification infer \\\n", + " -e $SPECS_DIR/infer.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/finetune/checkpoints/finetuned-model.tlt \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/infer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Export to ONNX\n", + "\n", + "[ONNX](https://onnx.ai/) is a popular open format for machine learning models. It enables interoperability between different frameworks, making the path to production much easier. \n", + "\n", + "TLT provides commands to export the .tlt model to the ONNX format in an .eonnx archive. The `export_format` configuration can be set to `ONNX` to achieve this.\n", + "\n", + "Sample usage of the `tlt text_classification export` command is shown in the following code cell. The export.yaml file we use looks like:\n", + "```\n", + "# Name of the .tlt EFF archive to be loaded/model to be exported.\n", + "restore_from: finetuned-model.tlt\n", + "\n", + "# Set export format: ONNX | JARVIS\n", + "export_format: ONNX\n", + "\n", + "# Output EFF archive containing ONNX.\n", + "export_to: exported-model.eonnx\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 6. For export to ONNX:\n", + "!tlt text_classification export \\\n", + " -e $SPECS_DIR/export.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/finetune/checkpoints/finetuned-model.tlt \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/export \\\n", + " export_format=ONNX" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This command exports the model as `exported-model.eonnx` which is essentially an archive containing the .onnx model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Inference using ONNX\n", + "\n", + "TLT provides the capability to use the exported .eonnx model for inference. \n", + "\n", + "The command `tlt text_classification infer_onnx` is very similar to the inference command for .tlt models. Again, the input file includes some \"simulated\" user input, we start with a batch of four samples for demo purposes, and encourage you to try out custom inputs as an exercise." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 7. For inference using ONNX:\n", + "!tlt text_classification infer_onnx \\\n", + " -e $SPECS_DIR/infer_onnx.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/export/exported-model.eonnx \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/infer_onnx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Export to Riva\n", + "\n", + "With TLT, you can also export your model in a format that can deployed using [NVIDIA Riva](https://developer.nvidia.com/riva), a highly performant application framework for multi-modal conversational AI services using GPUs! The same command for exporting to ONNX can be used here. The only small variation is the configuration for `export_format` in the spec file!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 8. For export to Riva:\n", + "!tlt text_classification export \\\n", + " -e $SPECS_DIR/export.yaml \\\n", + " -g 1 \\\n", + " -m $RESULTS_DIR/train/checkpoints/trained-model.tlt \\\n", + " -k $KEY \\\n", + " -r $RESULTS_DIR/export_riva \\\n", + " export_format=JARVIS \\\n", + " export_to=tc-model.riva" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model is exported as `tc-model.riva` which is in a format suited for deployment in Riva." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What's Next?\n", + "\n", + "You can use TLT to build custom models for your own applications, or deploy the custom models to NVIDIA Riva." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ai/RIVA/English/Python/source_code/config.sh b/ai/RIVA/English/Python/source_code/config.sh new file mode 100644 index 000000000..c48344eb2 --- /dev/null +++ b/ai/RIVA/English/Python/source_code/config.sh @@ -0,0 +1,153 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +# Enable or Disable Riva Services +service_enabled_asr=true +service_enabled_nlp=true +service_enabled_tts=true + +# Specify one or more GPUs to use +# specifying more than one GPU is currently an experimental feature, and may result in undefined behaviours. +gpus_to_use="device=0" + +# Specify the encryption key to use to deploy models +MODEL_DEPLOY_KEY="tlt_encode" + +# Locations to use for storing models artifacts +# +# If an absolute path is specified, the data will be written to that location +# Otherwise, a docker volume will be used (default). +# +# riva_init.sh will create a `rmir` and `models` directory in the volume or +# path specified. +# +# RMIR ($riva_model_loc/rmir) +# Riva uses an intermediate representation (RMIR) for models +# that are ready to deploy but not yet fully optimized for deployment. Pretrained +# versions can be obtained from NGC (by specifying NGC models below) and will be +# downloaded to $riva_model_loc/rmir by `riva_init.sh` +# +# Custom models produced by NeMo or TLT and prepared using riva-build +# may also be copied manually to this location $(riva_model_loc/rmir). +# +# Models ($riva_model_loc/models) +# During the riva_init process, the RMIR files in $riva_model_loc/rmir +# are inspected and optimized for deployment. The optimized versions are +# stored in $riva_model_loc/models. The riva server exclusively uses these +# optimized versions. +riva_model_loc="riva-model-repo" + +# The default RMIRs are downloaded from NGC by default in the above $riva_rmir_loc directory +# If you'd like to skip the download from NGC and use the existing RMIRs in the $riva_rmir_loc +# then set the below $use_existing_rmirs flag to true. You can also deploy your set of custom +# RMIRs by keeping them in the riva_rmir_loc dir and use this quickstart script with the +# below flag to deploy them all together. +use_existing_rmirs=false + +# Ports to expose for Riva services +riva_speech_api_port="50051" +riva_vision_api_port="60051" + +# NGC orgs +riva_ngc_org="nvidia" +riva_ngc_team="riva" +riva_ngc_image_version="1.4.0-beta" +riva_ngc_model_version="1.4.0-beta" + +# Pre-built models listed below will be downloaded from NGC. If models already exist in $riva-rmir +# then models can be commented out to skip download from NGC + +########## ASR MODELS ########## + +models_asr=( +### Punctuation model + "${riva_ngc_org}/${riva_ngc_team}/rmir_nlp_punctuation_bert_base:${riva_ngc_model_version}" + +### Citrinet-1024 Streaming w/ CPU decoder, best latency configuration + "${riva_ngc_org}/${riva_ngc_team}/rmir_asr_citrinet_1024_asrset1p7_streaming:${riva_ngc_model_version}" + +### Citrinet-1024 Streaming w/ CPU decoder, best throughput configuration +# "${riva_ngc_org}/${riva_ngc_team}/rmir_asr_citrinet_1024_asrset1p7_streaming_throughput:${riva_ngc_model_version}" + +### Citrinet-1024 Offline w/ CPU decoder, + "${riva_ngc_org}/${riva_ngc_team}/rmir_asr_citrinet_1024_asrset1p7_offline:${riva_ngc_model_version}" + +### Jasper Streaming w/ CPU decoder, best latency configuration +# "${riva_ngc_org}/${riva_ngc_team}/rmir_asr_jasper_english_streaming:${riva_ngc_model_version}" + +### Jasper Streaming w/ CPU decoder, best throughput configuration +# "${riva_ngc_org}/${riva_ngc_team}/rmir_asr_jasper_english_streaming_throughput:${riva_ngc_model_version}" + +### Jasper Offline w/ CPU decoder +# "${riva_ngc_org}/${riva_ngc_team}/rmir_asr_jasper_english_offline:${riva_ngc_model_version}" + +### Quarztnet Streaming w/ CPU decoder, best latency configuration +# "${riva_ngc_org}/${riva_ngc_team}/rmir_asr_quartznet_english_streaming:${riva_ngc_model_version}" + +### Quarztnet Streaming w/ CPU decoder, best throughput configuration +# "${riva_ngc_org}/${riva_ngc_team}/rmir_asr_quartznet_english_streaming_throughput:${riva_ngc_model_version}" + +### Quarztnet Offline w/ CPU decoder +# "${riva_ngc_org}/${riva_ngc_team}/rmir_asr_quartznet_english_offline:${riva_ngc_model_version}" + +### Jasper Streaming w/ GPU decoder, best latency configuration +# "${riva_ngc_org}/${riva_ngc_team}/rmir_asr_jasper_english_streaming_gpu_decoder:${riva_ngc_model_version}" + +### Jasper Streaming w/ GPU decoder, best throughput configuration +# "${riva_ngc_org}/${riva_ngc_team}/rmir_asr_jasper_english_streaming_throughput_gpu_decoder:${riva_ngc_model_version}" + +### Jasper Offline w/ GPU decoder +# "${riva_ngc_org}/${riva_ngc_team}/rmir_asr_jasper_english_offline_gpu_decoder:${riva_ngc_model_version}" +) + +########## NLP MODELS ########## + +models_nlp=( +### Bert base Punctuation model + "${riva_ngc_org}/${riva_ngc_team}/rmir_nlp_punctuation_bert_base:${riva_ngc_model_version}" + +### BERT base Named Entity Recognition model fine-tuned on GMB dataset with class labels LOC, PER, ORG etc. + "${riva_ngc_org}/${riva_ngc_team}/rmir_nlp_named_entity_recognition_bert_base:${riva_ngc_model_version}" + +### BERT Base Intent Slot model fine-tuned on weather dataset. + "${riva_ngc_org}/${riva_ngc_team}/rmir_nlp_intent_slot_bert_base:${riva_ngc_model_version}" + +### BERT Base Question Answering model fine-tuned on Squad v2. + "${riva_ngc_org}/${riva_ngc_team}/rmir_nlp_question_answering_bert_base:${riva_ngc_model_version}" + +### Megatron345M Question Answering model fine-tuned on Squad v2. +# "${riva_ngc_org}/${riva_ngc_team}/rmir_nlp_question_answering_megatron:${riva_ngc_model_version}" + +### Bert base Text Classification model fine-tuned on 4class (weather, meteorology, personality, nomatch) domain model. + "${riva_ngc_org}/${riva_ngc_team}/rmir_nlp_text_classification_bert_base:${riva_ngc_model_version}" +) + +########## TTS MODELS ########## + +models_tts=( + "${riva_ngc_org}/${riva_ngc_team}/rmir_tts_fastpitch_hifigan_ljspeech:${riva_ngc_model_version}" +# "${riva_ngc_org}/${riva_ngc_team}/rmir_tts_tacotron_waveglow_ljspeech:${riva_ngc_model_version}" +) + +NGC_TARGET=${riva_ngc_org} +if [[ ! -z ${riva_ngc_team} ]]; then + NGC_TARGET="${NGC_TARGET}/${riva_ngc_team}" +else + team="\"\"" +fi + +# define docker images required to run Riva +image_client="nvcr.io/${NGC_TARGET}/riva-speech-client:${riva_ngc_image_version}" +image_speech_api="nvcr.io/${NGC_TARGET}/riva-speech:${riva_ngc_image_version}-server" + +# define docker images required to setup Riva +image_init_speech="nvcr.io/${NGC_TARGET}/riva-speech:${riva_ngc_image_version}-servicemaker" + +# daemon names +riva_daemon_speech="riva-speech" +riva_daemon_client="riva-client" diff --git a/ai/RIVA/English/Python/source_code/dataset.py b/ai/RIVA/English/Python/source_code/dataset.py new file mode 100644 index 000000000..1e8f808b1 --- /dev/null +++ b/ai/RIVA/English/Python/source_code/dataset.py @@ -0,0 +1,38 @@ +# Copyright (c) 2021 NVIDIA Corporation. All rights reserved. +""" Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * * Neither the name of NVIDIA CORPORATION nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AS IS'' AND ANY + * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR + * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR + * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY + * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + */ +""" + + +import gdown +import os + +## input files in data.zip +url = 'https://drive.google.com/uc?id=1WZ0rtXZ-uMLfy7htT0gaU4EQ_Rq61QTF&export=download' ##remote URL for dataset location, need internet access to download +output_python = '/bootcamp/datasets/data.zip' +gdown.download(url, output_python, quiet=False, proxy=None) +os.system("unzip data.zip -d /bootcamp/datasets/") ##path to dataset folder, modify as required diff --git a/ai/RIVA/English/Python/source_code/riva_init_new.sh b/ai/RIVA/English/Python/source_code/riva_init_new.sh new file mode 100644 index 000000000..e5baa360e --- /dev/null +++ b/ai/RIVA/English/Python/source_code/riva_init_new.sh @@ -0,0 +1,174 @@ +#!/bin/bash +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +get_ngc_key_from_environment() { + # first check the global NGC_API_KEY environment variable + local ngc_key=$NGC_API_KEY + # if env variable was not set, and a ~/.ngc/config exists + # try to get it from there + if [ -z "$ngc_key" ] && [[ -f "$HOME/.ngc/config" ]] + then + ngc_key=$(cat $HOME/.ngc/config | grep ^apikey | awk '{print $3}') + fi + echo $ngc_key +} + +docker_pull() { + image_exists=$(docker images --filter=reference=$1 -q | wc -l) + if [[ $image_exists -eq 1 ]]; then + echo " > Image $1 exists. Skipping." + return + fi + attempts=3 + echo " > Pulling $1. This may take some time..." + for ((i = 1 ; i <= $attempts ; i++)); do + docker pull -q $1 &> /dev/null + if [ $? -ne 0 ]; then + echo " > Attempt $i out of $attempts failed" + if [ $i -eq $attempts ]; then + echo "Error occurred pulling '$1'." + docker pull $1 + echo "Exiting." + exit 1 + else + echo " > Trying again..." + continue + fi + else + break + fi + done +} + +check_docker_version() { + version_string=$(docker version --format '{{.Server.Version}}') + if [ $? -ne 0 ]; then + echo "Unable to run Docker. Please check that Docker is installed and functioning." + exit 1 + fi + maj_ver=$(echo $version_string | awk -F. '{print $1}') + min_ver=$(echo $version_string | awk -F. '{print $2}') + if [ "$maj_ver" -lt "19" ] || ([ "$maj_ver" -eq "19" ] && [ "$min_ver" -lt "03" ]); then + echo "Docker version insufficient. Please use Docker 19.03 or later" + exit 1; + fi +} + +# BEGIN SCRIPT +check_docker_version + +# load config file +script_path="$( cd "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P )" +if [ -z "$1" ]; then + config_path="${script_path}/config.sh" +else + config_path=$(readlink -f $1) +fi + +if [[ ! -f $config_path ]]; then + echo 'Unable to load configuration file. Override path to file with -c argument.' + exit 1 +fi +source $config_path || exit 1 + +read -p 'Please enter your RIVA SERVER PORT NUMBER: ' riva_port + +sed -i 's/riva_speech_api_port="50051"/riva_speech_api_port="'$riva_port'"/' $config_path + +servername="riva_speech_$riva_port" +clientname="riva_client_$riva_port" + +echo $riva_port +echo $servername +echo $clientname + +sed -i 's/riva_daemon_speech="riva-speech"/riva_daemon_speech="'$servername'"/' $config_path +sed -i 's/riva_daemon_client="riva-client"/riva_daemon_client="'$clientname'"/' $config_path + +# automatically get NGC_API_KEY or request from user if necessary +NGC_API_KEY="$(get_ngc_key_from_environment)" +if [ -z "$NGC_API_KEY" ]; then + read -sp 'Please enter API key for ngc.nvidia.com: ' NGC_API_KEY + echo +fi + +# use the API key to run docker login for the NGC registry +# exit early if the key is invalid, because we won't be able to do anything +echo "Logging into NGC docker registry if necessary..." +echo $NGC_API_KEY | docker login -u '$oauthtoken' --password-stdin nvcr.io &> /dev/null +if [ $? -ne 0 ]; then + echo 'NGC API Key is invalid. Please check and try again.' + exit 1 +fi + +# pull all the requisite images we're going to need +echo "Pulling required docker images if necessary..." +echo "Note: This may take some time, depending on the speed of your Internet connection." +# pull the speech server if any of asr/nlp/tts services are requested +if [ "$service_enabled_asr" = true ] || [ "$service_enabled_nlp" = true ] || [ "$service_enabled_tts" = true ]; then + echo "> Pulling Riva Speech Server images." + docker_pull $image_speech_api + docker_pull $image_client + docker_pull $image_init_speech +fi + + +if [ "$use_existing_rmirs" = false ]; then + echo + echo "Downloading models (RMIRs) from NGC..." + echo "Note: this may take some time, depending on the speed of your Internet connection." + echo "To skip this process and use existing RMIRs set the location and corresponding flag in config.sh." + + # build up commands to download from NGC + if [ "$service_enabled_asr" = true ] || [ "$service_enabled_nlp" = true ] || [ "$service_enabled_tts" = true ]; then + gmr_speech_models="" + if [ "$service_enabled_asr" = true ]; then + for model in ${models_asr[@]}; do + gmr_speech_models+=" $model" + done + fi + if [ "$service_enabled_nlp" = true ]; then + for model in ${models_nlp[@]}; do + gmr_speech_models+=" $model" + done + fi + if [ "$service_enabled_tts" = true ]; then + for model in ${models_tts[@]}; do + gmr_speech_models+=" $model" + done + fi + + # download required images + docker run --init -it --rm --gpus '"'$gpus_to_use'"' \ + -v $riva_model_loc:/data \ + -e "NGC_CLI_API_KEY=$NGC_API_KEY" \ + -e "NGC_CLI_ORG=nvidia" \ + --name riva-service-maker \ + $image_init_speech download_ngc_models $gmr_speech_models + + if [ $? -ne 0 ]; then + echo "Error in downloading models." + exit 1 + fi + fi +fi + +# convert all rmirs +echo +echo "Converting RMIRs at $riva_model_loc/rmir to Riva Model repository." + +set -x +docker run --init -it --rm --gpus '"'$gpus_to_use'"' \ + -v $riva_model_loc:/data \ + -e "MODEL_DEPLOY_KEY=${MODEL_DEPLOY_KEY}" \ + --name riva-service-maker \ + $image_init_speech deploy_all_models /data/rmir /data/models + +echo +echo "Riva initialization complete. Run ./riva_start.sh to launch services." diff --git a/ai/RIVA/English/Python/source_code/riva_start_client_bootcamp.sh b/ai/RIVA/English/Python/source_code/riva_start_client_bootcamp.sh new file mode 100644 index 000000000..46482a584 --- /dev/null +++ b/ai/RIVA/English/Python/source_code/riva_start_client_bootcamp.sh @@ -0,0 +1,81 @@ +#!/bin/bash +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +get_ngc_key_from_environment() { + # first check the global NGC_API_KEY environment variable + local ngc_key=$NGC_API_KEY + # if env variable was not set, and a ~/.ngc/config exists + # try to get it from there + if [ -z "$ngc_key" ] && [[ -f "$HOME/.ngc/config" ]] + then + ngc_key=$(cat $HOME/.ngc/config | grep apikey | awk '{print $3}') + fi + echo $ngc_key +} + +docker_pull_and_login_quiet_exit_on_fail() { + image_exists=$(docker images --filter=reference=$1 -q | wc -l) + if [[ $image_exists -eq 1 ]]; then + echo " > Image $1 exists. Skipping pull." + return + fi + + # confirm we're logged in + # automatically get NGC_API_KEY or request from user if necessary + NGC_API_KEY="$(get_ngc_key_from_environment)" + if [ -z "$NGC_API_KEY" ]; then + read -sp 'Please enter API key for ngc.nvidia.com: ' NGC_API_KEY + echo + fi + + # use the API key to run docker login for the NGC registry + # exit early if the key is invalid, because we won't be able to do anything + echo "Logging into NGC docker registry if necessary..." + echo $NGC_API_KEY | docker login -u '$oauthtoken' --password-stdin nvcr.io &> /dev/null + if [ $? -ne 0 ]; then + echo 'NGC API Key is invalid. Please check and try again.' + exit 1 + fi + + echo " > Pulling $1. This may take some time..." + docker pull -q $1 &> /dev/null + if [ $? -ne 0 ]; then + echo "Error occurred pulling '$1'." + docker pull $1 + echo "Exiting." + exit 1 + fi +} + +script_path="$( cd "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P )" +if [ -z "$1" ]; then + config_path="${script_path}/config.sh" +else + config_path=$(readlink -f $1) +fi + +source $config_path + +read -p 'Please enter your JUPYTER PORT NUMBER: ' jupyter_port +read -p 'Please enter your RIVA PORT NUMBER: ' riva_port + +echo $riva_port + +docker_pull_and_login_quiet_exit_on_fail ${image_client} + + +cp -r /mnt/shared/jarvis_testing/bootcampnotebooks ./bootcamp + +docker run --init -it --privileged \ + -v /dev/bus/usb:/dev/bus/usb \ + -v /dev/snd:/dev/snd \ + -v $PWD/jupyter_notebook:/bootcamp \ + -p $jupyter_port:$jupyter_port -p 8000 -p 8001 -p 8002 -p $riva_port \ + --name ${riva_daemon_client} \ + ${image_client} diff --git a/ai/RIVA/Readme.md b/ai/RIVA/Readme.md new file mode 100644 index 000000000..847470ef8 --- /dev/null +++ b/ai/RIVA/Readme.md @@ -0,0 +1,54 @@ +# RIVA_Bootcamp + +## GPU Bootcamp for RIVA + +This repository consists of gpu bootcamp material for RIVA. The RIVA frameworks of libraries, APIs and models allows you to build, optimize and deploy voice and speech centric applications and models based entirely on GPUs. In this series you can access RIVA learning resources in the form of labs. The modules covered in this Bootcamp are SpeechToText, Intent Slot Classification, Named Entity Recognition, Question-Answering and Challenge. + +## Prerequisites +To run this tutorial you will need a machine with NVIDIA GPU. + +- Install the latest [Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker) or [Singularity](https://sylabs.io/docs/). + +- The base containers required for the lab requires users to create a NGC account and generate an API key (https://docs.nvidia.com/ngc/ngc-catalog-user-guide/index.html#registering-activating-ngc-account) + +## Pulling containers +To start with, you will have to pull the RIVA Docker container. +ngc registry resource download-version "nvidia/riva/riva_quickstart:1.4.0-beta" + +### Copy Docker Initialization Scripts +cp *.sh riva_quickstart_v1.4.0-beta +cd riva_quickstart_v1.4.0-beta + +### If running with multiple users/clients, please choose a UNIQUE Riva server port number for each user (default is 50051) + +### Initialize the RIVA platform +bash riva_init_new.sh +bash riva_start.sh + +### start client + +bash riva_start_bootcamp.sh + +### run the Jupyter notebook + +jupyter notebook --ip=0.0.0.0 --allow-root --notebook-dir=/bootcamp --no-browser --port=8888 --NotebookApp.token='' + +Then, open the jupyter notebook in browser: http://localhost:8888 +Start working on the lab by clicking on the `Start_Here_RIVA_BOOTCAMP.ipynb` notebook. + + +## Troubleshooting + +Q. Cannot run RIVA scripts + +A. The Riva platform requires Docker to be actively running. In case you are running rootless-docker you should ensure that the Docker daemon is active and has sufficient permissions + +Q. Invalid API key + +A. Pulling pretrained model containers from NGC requires a valid API key. To get this key, please login to you NGC account and generate a new valid API key and re initialize the Riva platform + + +# For more information about the Riva platform, models and services, please refer here + +# For more information about the Riva quickstart container, please refer here + diff --git a/hpc/openacc/English/C/jupyter_notebook/images/pgprof1.PNG b/hpc/openacc/English/C/jupyter_notebook/images/pgprof1.PNG index b003288a5..7bd304502 100644 Binary files a/hpc/openacc/English/C/jupyter_notebook/images/pgprof1.PNG and b/hpc/openacc/English/C/jupyter_notebook/images/pgprof1.PNG differ diff --git a/hpc/openacc/English/Fortran/jupyter_notebook/images/pgprof1.PNG b/hpc/openacc/English/Fortran/jupyter_notebook/images/pgprof1.PNG index b003288a5..7bd304502 100644 Binary files a/hpc/openacc/English/Fortran/jupyter_notebook/images/pgprof1.PNG and b/hpc/openacc/English/Fortran/jupyter_notebook/images/pgprof1.PNG differ