Merge pull request #190239 from lgayhardt/azml-pytorch-0222

Pytorch freshness part 2

Author: PRMerger12

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

@@ -7,7 +7,7 @@ ms.service: machine-learning
 ms.subservice: core
 ms.author: minxia
 author: mx-iao
-ms.date: 01/14/2020
+ms.date: 02/28/2022
 ms.topic: how-to
 
 #Customer intent: As a Python PyTorch developer, I need to combine open-source with a cloud platform to train, evaluate, and deploy my deep learning models at scale. 
@@ -17,7 +17,7 @@ ms.topic: how-to
 
 In this article, learn how to run your [PyTorch](https://pytorch.org/) training scripts at enterprise scale using Azure Machine Learning.
 
-The example scripts in this article are used to classify chicken and turkey images to build a deep learning neural network (DNN) based on [PyTorch's transfer learning tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html). Transfer learning is a technique that applies knowledge gained from solving one problem to a different but related problem. Transfer learning shortens the training  process by requiring less data, time, and compute resources than training from scratch. See the [deep learning vs machine learning](./concept-deep-learning-vs-machine-learning.md#what-is-transfer-learning) article to learn more about transfer learning.
+The example scripts in this article are used to classify chicken and turkey images to build a deep learning neural network (DNN) based on [PyTorch's transfer learning tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html). Transfer learning is a technique that applies knowledge gained from solving one problem to a different but related problem. Transfer learning shortens the training  process by requiring less data, time, and compute resources than training from scratch. To learn more about transfer learning, see the [deep learning vs machine learning](./concept-deep-learning-vs-machine-learning.md#what-is-transfer-learning) article.
 
 Whether you're training a deep learning PyTorch 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.
 
@@ -88,8 +88,11 @@ shutil.copy('pytorch_train.py', project_folder)
 Create a compute target for your PyTorch job to run on. In this example, create a GPU-enabled Azure Machine Learning compute cluster.
 
 ```Python
+
+# Choose a name for your CPU cluster
 cluster_name = "gpu-cluster"
 
+# Verify that cluster does not exist already
 try:
     compute_target = ComputeTarget(workspace=ws, name=cluster_name)
     print('Found existing compute target')
@@ -98,11 +101,15 @@ except ComputeTargetException:
     compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', 
                                                            max_nodes=4)
 
+    # Create the cluster with the specified name and configuration
     compute_target = ComputeTarget.create(ws, cluster_name, compute_config)
 
+    # Wait for the cluster to complete, show the output log
     compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)
 ```
 
+If you instead want to create a CPU cluster, provide a different VM size to the vm_size parameter, such as STANDARD_D2_V2.
+
 [!INCLUDE [low-pri-note](../../includes/machine-learning-low-pri-vm.md)]
 
 For more information on compute targets, see the [what is a compute target](concept-compute-target.md) article.
@@ -113,7 +120,7 @@ To define the [Azure ML Environment](concept-environments.md) that encapsulates
 
 #### Use a curated environment
 
-Azure ML provides prebuilt, curated environments if you don't want to define your own environment. Azure ML has several CPU and GPU curated environments for PyTorch corresponding to different versions of PyTorch. For more info, see [here](resource-curated-environments.md).
+Azure ML provides prebuilt, [curated environments](resource-curated-environments.md) if you don't want to define your own environment. There are several CPU and GPU curated environments for PyTorch corresponding to different versions of PyTorch.
 
 If you want to use a curated environment, you can run the following command instead:
 
@@ -123,16 +130,19 @@ pytorch_env = Environment.get(workspace=ws, name=curated_env_name)
 ```
 
 To see the packages included in the curated environment, you can write out the conda dependencies to disk:
+
 ```python
 pytorch_env.save_to_directory(path=curated_env_name)
 ```
 
 Make sure the curated environment includes all the dependencies required by your training script. If not, you'll have to modify the environment to include the missing dependencies. If the environment is modified, you'll have to give it a new name, as the 'AzureML' prefix is reserved for curated environments. If you modified the conda dependencies YAML file, you can create a new environment from it with a new name, for example:
+
 ```python
 pytorch_env = Environment.from_conda_specification(name='pytorch-1.6-gpu', file_path='./conda_dependencies.yml')
 ```
 
 If you had instead modified the curated environment object directly, you can clone that environment with a new name:
+
 ```python
 pytorch_env = pytorch_env.clone(new_name='pytorch-1.6-gpu')
 ```
@@ -148,6 +158,7 @@ channels:
 - conda-forge
 dependencies:
 - python=3.6.2
+- pip=21.3.1
 - pip:
   - azureml-defaults
   - torch==1.6.0
@@ -177,7 +188,7 @@ For more information on creating and using environments, see [Create and use sof
 
 ### Create a ScriptRunConfig
 
-Create a [ScriptRunConfig](/python/api/azureml-core/azureml.core.scriptrunconfig) object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on. Any arguments to your training script will be passed via command line if specified in the `arguments` parameter.
+Create a [ScriptRunConfig](/python/api/azureml-core/azureml.core.scriptrunconfig) object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on. Any arguments to your training script will be passed via command line if specified in the `arguments` parameter. The following code will configure a single-node PyTorch job.
 
 ```python
 from azureml.core import ScriptRunConfig