Merge branch 'BV-AI-deploy-docker-image' of https://github.com/ovh/docs into BV-AI-deploy-docker-image

baaastijn · baaastijn · commit 02f39e7aa4c9 · 2023-03-29T21:38:54.000+02:00
diff --git a/pages/platform/ai/deploy_tuto_12_build_custom_image/guide.en-gb.md b/pages/platform/ai/deploy_tuto_12_build_custom_image/guide.en-gb.md
@@ -1,5 +1,5 @@
 ---
-title: AI Deploy - Tutorial -  Build & use custom Docker image
+title: AI Deploy - Tutorial -  Build & use a custom Docker image
 slug: deploy/build-use-custom-image
 excerpt: Explanations on how to build and use your own custom image
 section: AI Deploy - Tutorials
@@ -11,22 +11,22 @@ updated: 2023-03-29
 
 ## Objective
 
-This tutorial covers the process of building your own Docker image for AI deploy. After detailing major guidelines, we will cover a quick example.
+This tutorial covers the process of building your own Docker image for AI Deploy. After detailing major guidelines, we will cover a quick example.
 
 ## Requirements
 
 - Access to the [OVHcloud Control Panel](https://www.ovh.com/auth/?action=gotomanager&from=https://www.ovh.co.uk/&ovhSubsidiary=GB)
 - A **Public Cloud** project
 - A [user for AI Deploy](https://docs.ovh.com/gb/en/publiccloud/ai/users)
-- [Docker](https://www.docker.com/get-started) installed somewhere like your personal computer or a virtual machine
-- Some knowledge about building an image. Follow [official Docker Getting Started](https://docs.docker.com/get-started/)
+- [Docker](https://www.docker.com/get-started) installed on a personal computer or a virtual machine
+- Knowledge about building a Docker image (see the [official Getting Started guide](https://docs.docker.com/get-started/))
 
 ## Quick overview
 
 AI Deploy main goal is to simplify AI models or applications deployment, release them in production, with resiliency and security, in a few seconds.
 Each application is linked to compute resources such as CPUs or GPUs, and can be accessed through an HTTP Endpoint provided by AI Deploy for each app.
 
-In order to be deployed, your model or application **has to be containerized**, inside a Docker image. Containers provide isolation but also flexibility for your deployments.
+In order to be deployed, your model or application **has to be containerised**, inside a Docker image. Containers provide isolation but also flexibility for your deployments.
 The Docker images that you build can be deployed locally, with OVHcloud AI Deploy but also with cloud competitors such as AWS or GCP.
 
 Inside your Docker image, you are free to install almost anything and everything as long as you follow guidelines below.
@@ -44,34 +44,34 @@ AI Deploy accept images from **public** or **private** repositories. In short, w
 Instead of starting from scratch, feel free to start from an existing Docker image, as long as it is compliant with the following guidelines.
 For example, you can start from 'python', from 'alpine' or equivalent.
 
-If you need to work with GPU, please read the next paragraph.
+If you need to work with GPUs, please read the next paragraph.
 
 ### Use specific images with CUDA drivers for GPUs
 
 > [!primary]
 >
-> If you want to communicate with our **GPU** hardware in your **AI Deploy apps**, the base image should have **cuda drivers installed on**.
+> If you want to communicate with our **GPU** hardware in your **AI Deploy apps**, the base image should have **cuda drivers installed**.
 
 Here is a potential list of official base images (featuring **cuda drivers**) that you can use:
 
 - [pytorch/pytorch:latest](https://hub.docker.com/r/pytorch/pytorch)
 - [tensorflow/tensorflow:latest-gpu](https://hub.docker.com/r/tensorflow/tensorflow)
-- [huggingface/transformers-pytorch-gpu:latest](https://hub.docker.com/r/huggingface/transformers-pytorch-gpu/) docker pull huggingface/transformers-pytorch-gpu:latest
+- [huggingface/transformers-pytorch-gpu:latest](https://hub.docker.com/r/huggingface/transformers-pytorch-gpu/) (docker pull huggingface/transformers-pytorch-gpu:latest)
 - [mxnet/python](https://hub.docker.com/r/mxnet/python)
 - [nvidia/cuda](https://hub.docker.com/r/nvidia/cuda)
 
-For example if you want to start from the base image `tensorflow/tensorflow:latest-gpu`:
+For example, if you want to start from the base image `tensorflow/tensorflow:latest-gpu`:
 
 ```{.console}
 FROM tensorflow/tensorflow:latest-gpu
 ```
 
 ### Use the linux/amd64 architecture
 
-Your Docker image has to support at least `linux/amd64` platform to be deployed correctly. Otherwise deployment will fail.
+Your Docker image has to support at least the `linux/amd64` platform to be deployed correctly. Otherwise deployment will fail.
 
 When you invoke a build, you can set the `--platform` flag to specify the target platform for the build output, `linux/amd64`.
-This is especially relevant if you use Apple new computers (M1/M2/... chipsets) or ARM-based computers.
+This is especially relevant if you use newer Apple computers (M1/M2/... chipsets) or ARM-based computers.
 
 ```{.console}
 docker buildx build --platform linux/amd64,linux/arm64 ...
@@ -81,12 +81,12 @@ More information can be found in the [official Docker documentation](https://doc
 
 ### Create an OVHcloud user and a working directory
 
-Deployed containers are not run as root user, but by an “OVHcloud” user with **UID 42420**. 
+Deployed containers are not run as root, but by an “OVHcloud” user with **UID 42420**. 
 It means that if you want to be able to write in a specific directory at runtime, you will have to give it specific rights. 
 
-This is the case in the vast majority of use-cases.
+This is the case in the vast majority of use cases.
 
-You can do it with the following instruction:
+You can do it with the following instructions:
 
 
 ```{.console}
@@ -128,24 +128,24 @@ RUN pip install -r requirements.txt
 ### Manage output data effectively (S3, ...)
 
 Just like AI Notebooks and AI Training, AI Deploy is easily connected to remote storage such as S3 object storage containers at launch.
-However, unlike AI Notebooks and AI Training, AI Deploy **does NOT** synchronize data back to your remote storage.
+However, unlike AI Notebooks and AI Training, AI Deploy **does NOT** synchronise data back to your remote storage.
 
 If you need to write data somewhere, for example output from your AI model (generated images), your code application should include storage connection.
-For example, you can use Python `Boto3` library when using Python and S3. 
+For example, you can use the Python `Boto3` library when using Python and S3. 
 
 Be careful, **if you write data directly in your working directory, it will be lost when you stop your application**.
 
 ### Use environment variables for dynamic values
 
 > [!primary]
 >
-> For sensitive data such as password or token, consider using [Docker Secrets](https://docs.docker.com/engine/swarm/secrets/).
+> For sensitive data such as passwords or tokens, consider using [Docker Secrets](https://docs.docker.com/engine/swarm/secrets/).
 
-Sometimes, instead of hardcoding a variable inside a Dockerfile, it is much more powerful to pass variables during deployment. Docker provides natively this option with `--env` argument, and OVHcloud AI tools follow the same logic.
+Sometimes, instead of hardcoding a variable inside a Dockerfile, it is much more powerful to pass variables during deployment. Docker provides this option natively through the `--env` argument, and OVHcloud AI tools follow the same logic.
 
-During AI Deploy app creation, you will be able to pass environment variables via CLI, API or UI in the control panel. In your Dockerfile, you can gather theses variables with the `ENV` value
+During AI Deploy app creation, you will be able to pass environment variables via CLI, API or UI in the control panel. In your Dockerfile, you can gather theses variables with the `ENV` value.
 
-For example, you can launch a new app with two variable like this:
+For example, you can launch a new app with two variables like this:
 
 ```
 ovhai run app <my_docker_image> -e LANGUAGE=english TOKEN=12345678 
@@ -172,35 +172,34 @@ ADD . $foo
 ### Exposing your model or application with an API
 
 Inside your Dockerfile, you will need to expose your model or application so anyone can use it. The easiest way is to expose API via REST endpoint.
+The most popular open source frameworks for exposing APIs are [Flask]() and [Fast API]().
 
-Most famous and open source frameworks for exposing API are [Flask](https://flask.palletsprojects.com/) and [FastAPI](https://fastapi.tiangolo.com/).
-
-You can find a basic example in the next chapter, and more advanced tutorials in [AI Deploy documentation](https://docs.ovh.com/gb/en/publiccloud/ai/).
+You can find a basic example in the section below, and more advanced tutorials in our [AI Deploy documentation](https://docs.ovh.com/gb/en/publiccloud/ai/).
 
 ### Exposing your application with a web frontend
 
 While an API is useful for automation and code, sometimes you will need to expose your application or model through a web interface.
 
-AI Deploy is fully compliant with multiple frontend frameworks, such as [Streamlit](https://streamlit.io/), [Gradio](https://gradio.app/) or [Taipy](https://www.taipy.io/
-).
+AI Deploy is fully compliant with multiple frontend frameworks, such as [Streamlit](https://streamlit.io/), [Gradio](https://gradio.app/) or [Taipy](https://www.taipy.io/).
 You can of course also build your own frontend with your favourite tools, such as HTML/CSS.
-You can find a basic example in the next chapter, and more advanced tutorials in [AI Deploy documentation](https://docs.ovh.com/gb/en/publiccloud/ai/).
 
-## Basic example: write your own Dockerfile and build your image
+You can find a basic example in the section below, and more advanced tutorials in our [AI Deploy documentation](https://docs.ovh.com/gb/en/publiccloud/ai/).
+
+## Basic example: Write your own Dockerfile and build your image
 
-Here, we will build a basic docker image, following the guidelines.
+Here we will build a basic Docker image, following the guidelines.
 
 ### Prepare the Dockerfile
 
-Create a new file and name it `Dockerfile` following guidelines.
+Create a new file and name it `Dockerfile`, following the guidelines.
 
 1. First you need to choose a base image to start from. 
-2. Install what you need as dependencies with `apt` or `pip`. Bash command instructions on your Dockerfile should begin with `RUN` prefix.
-3. Copy files from your local directory inside docker image with the `COPY` prefix.
-4. Allow user OVHcloud UID 42420 to get specific rights 
-5. Run your script
+2. Install what you need as dependencies with `apt` or `pip`. Bash command instructions on your Dockerfile should begin with the `RUN` prefix.
+3. Copy files from your local directory inside the Docker image with the `COPY` prefix.
+4. Allow user "OVHcloud UID 42420" to get specific rights.
+5. Run your script.
 
-A basic example can be summarized like this:
+A basic example can be summarised like this:
 
 ```{.console}
 # Start from official Python image since we don't need GPU
@@ -216,7 +215,7 @@ RUN apt-get update && apt-get install -y vim git
 ADD example.py /workspace/
 ADD dataset.csv /workspace/
 
-# Create a HOME dedicated to the OVHcloud user (42420:42420). mandatory step
+# Create a HOME dedicated to the OVHcloud user (42420:42420). Mandatory step
 RUN chown -R 42420:42420 /workspace
 ENV HOME=/workspace
 
@@ -226,28 +225,28 @@ CMD [ "python3" , "/workspace/example.py" ]
 
 ### Build your Docker image
 
-Once your **Dockerfile** is complete and match your needs, you have to choose a name and build the image using the following command in the same directory:
+Once your **Dockerfile** is complete and matches your needs, you have to choose a name and build the image using the following command in the same directory:
 
 ```{.console}
 docker build . -t <image-identifier>
 ```
 
 > [!primary]
 >
-> The dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory.
+> The dot argument `.` indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory.
 
 > [!primary]
 >
 > The `-t` argument allows you to choose the identifier to give to your image. Usually, image identifiers are composed of a **name** and a **version tag** `<name>:<version>`.
 
 > [!warning]
 >
-> Please make sure that the docker image you will push in order to run containers using AI products respects the **linux/amd64** architecture. You could, for instance, build your image using **buildx** as follows:
+> Please make sure that the Docker image you will push in order to run containers using AI products respects the **linux/amd64** architecture. You could, for instance, build your image using **buildx** as follows:
 >
 > `docker buildx build --platform linux/amd64 ...`
 >
 
-## Test it locally (Optional)
+## Test it locally (optional)
 
 If you want to verify that your built image is working properly, run the following command:
 
@@ -257,17 +256,17 @@ docker run --rm -it --user=42420:42420 <image-identifier>
 
 > [!warning]
 >
-> Don't forget the `--user=42420:42420` argument if you want to simulate the exact same behavior that will occur on **AI Deploy apps **. It executes the docker container as the specific OVHcloud user (user **42420:42420**).
+> Don't forget the `--user=42420:42420` argument if you want to simulate the exact same behaviour that will occur on **AI Deploy apps**. It executes the Docker container as the specific OVHcloud user (user **42420:42420**).
 
-### Push image in the registry of your choice
+### Push the image to the registry of your choice
 
 Pushing your image to a registry is needed in order for AI Deploy to pull it.
 
 AI Deploy provides a default registry called **Shared registry** where users are able to push their custom images. It is linked with every project by default.
 
-If you prefer using your own private docker registry instead of the shared one, feel free to use it. Just don't forget to [add your registry in your AI Tools project](https://docs.ovh.com/gb/en/publiccloud/ai/training/add-private-registry) before using it.
+If you prefer using your own private Docker registry instead of the shared one, feel free to use it. Just don't forget to [add your registry in your AI Tools project](https://docs.ovh.com/gb/en/publiccloud/ai/training/add-private-registry) before using it.
 
-The basic commands to push a docker image to a registry is:
+The basic commands to push a Docker image to a registry is:
 
 ```{.console}
 # Add a new registry into OVHcloud AI Tools
@@ -279,7 +278,7 @@ docker tag <image-identifier> <registry>/<image-identifier>
 docker push <registry>/<image-identifier>
 ```
 
-Example: if you want to push an image named `custom-image` inside a registry `registry.gra.training.ai.cloud.ovh.net` :
+Example: If you want to push an image named `custom-image` inside a registry `registry.gra.training.ai.cloud.ovh.net`:
 
 ```{.console}
 # Add a new registry into OVHcloud AI Tools
@@ -303,4 +302,4 @@ If you want to know the exact commands to push on the shared registry, please co
 
 Please send us your questions, feedback and suggestions to improve the service:
 
-- On the OVHcloud [Discord server](https://discord.gg/ovhcloud)
+- On the OVHcloud [Discord server](https://discord.gg/ovhcloud)
