Merge pull request #187634 from lgayhardt/azureml-tensor-pytorch

 Acrolinx and fix link text

Author: PRMerger16

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

@@ -7,7 +7,6 @@ ms.service: machine-learning
 ms.subservice: core
 ms.author: minxia
 author: mx-iao
-ms.reviewer: peterlu
 ms.date: 01/14/2020
 ms.topic: how-to
 
@@ -70,7 +69,7 @@ ws = Workspace.from_config()
 
 ### Get the data
 
-The dataset consists of about 120 training images each for turkeys and chickens, with 100 validation images for each class. We will download and extract the dataset as part of our training script `pytorch_train.py`. The images are a subset of the [Open Images v5 Dataset](https://storage.googleapis.com/openimages/web/index.html).
+The dataset consists of about 120 training images each for turkeys and chickens, with 100 validation images for each class. We'll download and extract the dataset as part of our training script `pytorch_train.py`. The images are a subset of the [Open Images v5 Dataset](https://storage.googleapis.com/openimages/web/index.html).
 
 ### Prepare training script
 
@@ -127,7 +126,7 @@ To see the packages included in the curated environment, you can write out the c
 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 will have to modify the environment to include the missing dependencies. Note that if the environment is modified, you will 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, e.g.:
+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')
 ```
@@ -158,7 +157,7 @@ dependencies:
 
 Create an Azure ML environment from this conda environment specification. The environment will be packaged into a Docker container at runtime.
 
-By default if no base image is specified, Azure ML will use a CPU image `azureml.core.environment.DEFAULT_CPU_IMAGE` as the base image. Since this example runs training on a GPU cluster, you will need to specify a GPU base image that has the necessary GPU drivers and dependencies. Azure ML maintains a set of base images published on Microsoft Container Registry (MCR) that you can use, see the [Azure/AzureML-Containers](https://github.com/Azure/AzureML-Containers) GitHub repo for more information.
+By default if no base image is specified, Azure ML will use a CPU image `azureml.core.environment.DEFAULT_CPU_IMAGE` as the base image. Since this example runs training on a GPU cluster, you'll need to specify a GPU base image that has the necessary GPU drivers and dependencies. Azure ML maintains a set of base images published on Microsoft Container Registry (MCR) that you can use. For more information, see [AzureML-Containers GitHub repo](https://github.com/Azure/AzureML-Containers).
 
 ```python
 pytorch_env = Environment.from_conda_specification(name='pytorch-1.6-gpu', file_path='./conda_dependencies.yml')

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

@@ -72,7 +72,7 @@ ws = Workspace.from_config()
 
 ### Create a file dataset
 
-A `FileDataset` object references one or multiple files in your workspace datastore or public urls. The files can be of any format, and the class provides you with the ability to download or mount the files to your compute. By creating a `FileDataset`, you create a reference to the data source location. If you applied any transformations to the data set, they will be stored in the data set as well. The data remains in its existing location, so no extra storage cost is incurred. See the [how-to](./how-to-create-register-datasets.md) guide on the `Dataset` package for more information.
+A `FileDataset` object references one or multiple files in your workspace datastore or public urls. The files can be of any format, and the class provides you with the ability to download or mount the files to your compute. By creating a `FileDataset`, you create a reference to the data source location. If you applied any transformations to the data set, they'll be stored in the data set as well. The data remains in its existing location, so no extra storage cost is incurred. For more information the `Dataset` package, see the [How to create register datasets article](./how-to-create-register-datasets.md).
 
 ```python
 from azureml.core.dataset import Dataset
@@ -141,7 +141,7 @@ To see the packages included in the curated environment, you can write out the c
 tf_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 will have to modify the environment to include the missing dependencies. Note that if the environment is modified, you will 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, e.g.:
+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
 tf_env = Environment.from_conda_specification(name='tensorflow-2.2-gpu', file_path='./conda_dependencies.yml')
 ```
@@ -169,7 +169,7 @@ dependencies:
 
 Create an Azure ML environment from this conda environment specification. The environment will be packaged into a Docker container at runtime.
 
-By default if no base image is specified, Azure ML will use a CPU image `azureml.core.environment.DEFAULT_CPU_IMAGE` as the base image. Since this example runs training on a GPU cluster, you will need to specify a GPU base image that has the necessary GPU drivers and dependencies. Azure ML maintains a set of base images published on Microsoft Container Registry (MCR) that you can use, see the [Azure/AzureML-Containers](https://github.com/Azure/AzureML-Containers) GitHub repo for more information.
+By default if no base image is specified, Azure ML will use a CPU image `azureml.core.environment.DEFAULT_CPU_IMAGE` as the base image. Since this example runs training on a GPU cluster, you'll need to specify a GPU base image that has the necessary GPU drivers and dependencies. Azure ML maintains a set of base images published on Microsoft Container Registry (MCR) that you can use, see the [Azure/AzureML-Containers](https://github.com/Azure/AzureML-Containers) GitHub repo for more information.
 
 ```python
 tf_env = Environment.from_conda_specification(name='tensorflow-2.2-gpu', file_path='./conda_dependencies.yml')
@@ -235,7 +235,9 @@ As the run is executed, it goes through the following stages:
 
 ## Register or download a model
 
-Once you've trained the model, you can register it to your workspace. Model registration lets you store and version your models in your workspace to simplify [model management and deployment](concept-model-management-and-deployment.md). Optional: by specifying the parameters `model_framework`, `model_framework_version`, and `resource_configuration`, no-code model deployment becomes available. This allows you to directly deploy your model as a web service from the registered model, and the `ResourceConfiguration` object defines the compute resource for the web service.
+Once you've trained the model, you can register it to your workspace. Model registration lets you store and version your models in your workspace to simplify [model management and deployment](concept-model-management-and-deployment.md).
+
+Optional: by specifying the parameters `model_framework`, `model_framework_version`, and `resource_configuration`, no-code model deployment becomes available. This allows you to directly deploy your model as a web service from the registered model, and the `ResourceConfiguration` object defines the compute resource for the web service.
 
 ```Python
 from azureml.core import Model
@@ -270,7 +272,7 @@ The deployment how-to contains a section on registering models, but you can skip
 
 ### (Preview) No-code model deployment
 
-Instead of the traditional deployment route, you can also use the no-code deployment feature (preview) for TensorFlow. By registering your model as shown above with the `model_framework`, `model_framework_version`, and `resource_configuration` parameters, you can simply use the `deploy()` static function to deploy your model.
+Instead of the traditional deployment route, you can also use the no-code deployment feature (preview) for TensorFlow. By registering your model as shown above with the `model_framework`, `model_framework_version`, and `resource_configuration` parameters, you can use the `deploy()` static function to deploy your model.
 
 ```python
 service = Model.deploy(ws, "tensorflow-web-service", [model])