Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into HubSpokeBug

Author: siddomala

articles/machine-learning/how-to-train-tensorflow.md

@@ -23,7 +23,7 @@ ms.custom: sdkv2, event-tier1-build-2022
 
 In this article, learn how to run your [TensorFlow](https://www.tensorflow.org/overview) training scripts at scale using Azure Machine Learning Python SDK v2.
 
-This example code in this article train a TensorFlow model to classify handwritten digits using a deep neural network (DNN), register the model, and deploy it to an online endpoint.
+The example code in this article train a TensorFlow model to classify handwritten digits, using a deep neural network (DNN); register the model; and deploy it to an online endpoint.
 
 Whether you're developing a TensorFlow model from the ground-up or you're bringing an existing model into the cloud, you can use Azure Machine Learning to scale out open-source training jobs using elastic cloud compute resources. You can build, deploy, version, and monitor production-grade models with Azure Machine Learning.
 
@@ -32,7 +32,7 @@ Whether you're developing a TensorFlow model from the ground-up or you're bringi
 To benefit from this article, you'll need to:
 
 - Access an Azure subscription. If you don't have one already, [create a free account](https://azure.microsoft.com/free/).
-- Run the code in this article using either an Azure Machine Learning compute instance, or your own Jupyter notebook.
+- Run the code in this article using either an Azure Machine Learning compute instance or your own Jupyter notebook.
     - Azure Machine Learning compute instance - no downloads or installation necessary
         - Complete the [Quickstart: Get started with Azure Machine Learning](quickstart-create-resources.md) to create a dedicated notebook server pre-loaded with the SDK and the sample repository.
         - In the samples deep learning folder on the notebook server, find a completed and expanded notebook by navigating to this directory: **v2  > sdk > jobs > single-step > tensorflow > train-hyperparameter-tune-deploy-with-tensorflow**.
@@ -63,7 +63,7 @@ If `DefaultAzureCredential` doesn't work for you, see [`azure-identity reference
 
 [!notebook-python[](~/azureml-examples-v2samplesreorg/sdk/python/jobs/single-step/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb?name=credential)]
 
-If you prefer to use a browser to sign in and authenticate, you should remove the comments in the following code and use it instead.
+If you prefer to use a browser to sign in and authenticate, you should uncomment the following code and use it instead.
 
 ```python
 # Handle to the workspace
@@ -76,7 +76,7 @@ If you prefer to use a browser to sign in and authenticate, you should remove th
 
 Next, get a handle to the workspace by providing your Subscription ID, Resource Group name, and workspace name. To find these parameters:
 
-1. Look in the upper-right corner of the Azure Machine Learning studio toolbar for your workspace name.
+1. Look for your workspace name in the upper-right corner of the Azure Machine Learning studio toolbar.
 2. Select your workspace name to show your Resource Group and Subscription ID.
 3. Copy the values for Resource Group and Subscription ID into the code.
 
@@ -91,7 +91,7 @@ The result of running this script is a workspace handle that you'll use to manag
 
 AzureML needs a compute resource to run a job. This resource can be single or multi-node machines with Linux or Windows OS, or a specific compute fabric like Spark.
 
-In the following example script, we provision a Linux [`compute cluster`](/azure/machine-learning/how-to-create-attach-compute-cluster?tabs=python). You can see the [`Azure Machine Learning pricing`](https://azure.microsoft.com/pricing/details/machine-learning/) page for the full list of VM sizes and prices. Since we need a GPU cluster for this example, let's pick a *STANDARD_NC6* model and create an Azure ML compute.
+In the following example script, we provision a Linux [`compute cluster`](/azure/machine-learning/how-to-create-attach-compute-cluster?tabs=python). You can see the [`Azure Machine Learning pricing`](https://azure.microsoft.com/pricing/details/machine-learning/) page for the full list of VM sizes and prices. Since we need a GPU cluster for this example, let's pick a *STANDARD_NC6* model and create an AzureML compute.
 
 [!notebook-python[](~/azureml-examples-v2samplesreorg/sdk/python/jobs/single-step/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb?name=cpu_compute_target)]
 
@@ -130,8 +130,8 @@ During the pipeline run, you'll use MLFlow to log the parameters and metrics. To
 
 In the training script `tf_mnist.py`, we create a simple deep neural network (DNN). This DNN has:
 
-- An input layer with 28 * 28 = 784 neurons. Each neuron represents an image pixel;
-- Two hidden layers. The first hidden layer has 300 neurons and the second hidden layer has 100 neurons; and
+- An input layer with 28 * 28 = 784 neurons. Each neuron represents an image pixel.
+- Two hidden layers. The first hidden layer has 300 neurons and the second hidden layer has 100 neurons.
 - An output layer with 10 neurons. Each neuron represents a targeted label from 0 to 9.
 
 :::image type="content" source="media/how-to-train-tensorflow/neural-network.png" alt-text="Diagram showing a deep neural network with 784 neurons at the input layer, two hidden layers, and 10 neurons at the output layer.":::
@@ -149,8 +149,7 @@ You'll use the general purpose `command` to run the training script and perform
 
 [!notebook-python[](~/azureml-examples-v2samplesreorg/sdk/python/jobs/single-step/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb?name=job)]
 
-- The inputs for this command include the data location, batch size, number of neurons in the first and second layer, and learning rate.
-    - We've passed in the web path directly as an input.
+- The inputs for this command include the data location, batch size, number of neurons in the first and second layer, and learning rate. Notice that we've passed in the web path directly as an input.
 
 - For the parameter values:
     - provide the compute cluster `gpu_compute_target = "gpu-cluster"` that you created for running this command;
@@ -162,7 +161,7 @@ You'll use the general purpose `command` to run the training script and perform
 
 ### Submit the job
 
-It's now time to submit the job to run in AzureML. This time you'll use `create_or_update` on `ml_client.jobs`.
+It's now time to submit the job to run in AzureML. This time, you'll use `create_or_update` on `ml_client.jobs`.
 
 [!notebook-python[](~/azureml-examples-v2samplesreorg/sdk/python/jobs/single-step/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb?name=create_job)]
 
@@ -176,7 +175,7 @@ As the job is executed, it goes through the following stages:
 
 - **Preparing**: A docker image is created according to the environment defined. The image is uploaded to the workspace's container registry and cached for later runs. Logs are also streamed to the run history and can be viewed to monitor progress. If a curated environment is specified, the cached image backing that curated environment will be used.
 
-- **Scaling**: The cluster attempts to scale up if the cluster requires more nodes to execute the run than are currently available.
+- **Scaling**: The cluster attempts to scale up if it requires more nodes to execute the run than are currently available.
 
 - **Running**: All scripts in the script folder *src* are uploaded to the compute target, data stores are mounted or copied, and the script is executed. Outputs from *stdout* and the *./logs* folder are streamed to the run history and can be used to monitor the run.
 
@@ -225,7 +224,7 @@ For more information about deployment, see [Deploy and score a machine learning
 
 ### Create a new online endpoint
 
-As a first step, you need to create your online endpoint. The endpoint name must be unique in the entire Azure region. For this article, you'll create a unique name using a universally unique identifier (UUID).
+As a first step to deploying your model, you need to create your online endpoint. The endpoint name must be unique in the entire Azure region. For this article, you'll create a unique name using a universally unique identifier (UUID).
 
 [!notebook-python[](~/azureml-examples-v2samplesreorg/sdk/python/jobs/single-step/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb?name=online_endpoint_name)]
 
@@ -237,14 +236,14 @@ Once you've created the endpoint, you can retrieve it as follows:
 
 ### Deploy the model to the endpoint
 
-After you've created the endpoint, you can deploy the model with the entry script. An endpoint can have multiple deployments. The endpoint can then direct traffic to these deployments, using rules.
+After you've created the endpoint, you can deploy the model with the entry script. An endpoint can have multiple deployments. Using rules, the endpoint can then direct traffic to these deployments.
 
 In the following code, you'll create a single deployment that handles 100% of the incoming traffic. We've specified an arbitrary color name (*tff-blue*) for the deployment. You could also use any other name such as *tff-green* or *tff-red* for the deployment.
 The code to deploy the model to the endpoint does the following:
 
-- Deploys the best version of the model that you registered earlier;
-- Scores the model, using the `core.py` file; and
-- Uses the same curated environment (that you declared earlier) to perform inferencing.
+- deploys the best version of the model that you registered earlier;
+- scores the model, using the `core.py` file; and
+- uses the same curated environment (that you declared earlier) to perform inferencing.
 
 [!notebook-python[](~/azureml-examples-v2samplesreorg/sdk/python/jobs/single-step/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb?name=blue_deployment)]
 

articles/storage/files/storage-files-scale-targets.md

@@ -4,7 +4,7 @@ description: Learn about the capacity, IOPS, and throughput rates for Azure file
 author: khdownie
 ms.service: storage
 ms.topic: conceptual
-ms.date: 01/31/2022
+ms.date: 10/04/2022
 ms.author: kendownie
 ms.subservice: files
 ---
@@ -81,7 +81,7 @@ File scale targets apply to individual files stored in Azure file shares.
 | Maximum concurrent request rate | 1,000 IOPS | Up to 8,000<sup>1</sup> |
 | Maximum ingress for a file | 60 MiB/sec | 200 MiB/sec (Up to 1 GiB/s with SMB Multichannel)<sup>2</sup>|
 | Maximum egress for a file | 60 MiB/sec | 300 MiB/sec (Up to 1 GiB/s with SMB Multichannel)<sup>2</sup> |
-| Maximum concurrent handles | 2,000 handles | 2,000 handles  |
+| Maximum concurrent handles per file, directory, and share root | 2,000 handles | 2,000 handles  |
 
 <sup>1 Applies to read and write IOs (typically smaller IO sizes less than or equal to 64 KiB). Metadata operations, other than reads and writes, may be lower.</sup>
 <sup>2 Subject to machine network limits, available bandwidth, IO sizes, queue depth, and other factors. For details see [SMB Multichannel performance](./storage-files-smb-multichannel-performance.md).</sup>