Merge pull request #95444 from nibaccam/refresh

Article refresh

Author: GitHubber17

articles/machine-learning/service/how-to-monitor-tensorboard.md

@@ -8,7 +8,7 @@ ms.subservice: core
 ms.topic: conceptual
 author: maxluk
 ms.author: maxluk
-ms.date: 06/28/2019
+ms.date: 11/08/2019
 ---
 
 # Visualize experiment runs and metrics with TensorBoard and Azure Machine Learning
@@ -36,16 +36,18 @@ How you launch TensorBoard with Azure Machine Learning experiments depends on th
 
         * Complete the [Tutorial: Setup environment and workspace](tutorial-1st-experiment-sdk-setup.md) to create a dedicated notebook server pre-loaded with the SDK and the sample repository.
 
-        * In the samples folder on the notebook server, find  two completed and expanded notebooks by navigating to this directory: **how-to-use-azureml > training-with-deep-learning**.
-        * export-run-history-to-run-history.ipynb
-        * tensorboard.ipynb
+        * In the samples folder on the notebook server, find  two completed and expanded notebooks by navigating to these directories:
+            * **how-to-use-azureml > training-with-deep-learning > export-run-history-to-tensorboard > export-run-history-to-tensorboard.ipynb**
+
+            * **how-to-use-azureml > track-and-monitor-experiments > tensorboard.ipynb**
 
     * Your own Juptyer notebook server
-          * [Install the Azure Machine Learning SDK](https://docs.microsoft.com/python/api/overview/azure/ml/install?view=azure-ml-py) with the `tensorboard` extra
-          * [Create an Azure Machine Learning workspace](how-to-manage-workspace.md).  
-          * [Create a workspace configuration file](how-to-configure-environment.md#workspace).
+       * [Install the Azure Machine Learning SDK](https://docs.microsoft.com/python/api/overview/azure/ml/install?view=azure-ml-py) with the `tensorboard` extra
+        * [Create an Azure Machine Learning workspace](how-to-manage-workspace.md).  
+        * [Create a workspace configuration file](how-to-configure-environment.md#workspace).
 
 <a name="direct"></a>
+
 ## Option 1: Directly view run history in TensorBoard
 
 This option works for experiments that natively outputs log files consumable by TensorBoard, such as PyTorch, Chainer, and TensorFlow experiments. If that is not the case of your experiment, use [the `export_to_tensorboard()` method](#export) instead.

articles/machine-learning/service/how-to-train-ml-models.md

@@ -9,7 +9,7 @@ ms.service: machine-learning
 ms.subservice: core
 ms.topic: conceptual
 ms.reviewer: sgilley
-ms.date: 04/19/2019
+ms.date: 11/08/2019
 ms.custom: seodec18
 
 ---
@@ -133,7 +133,8 @@ For a notebook that trains a scikit-learn model by using estimator, see:
 * [tutorials/img-classification-part1-training.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/tutorials/img-classification-part1-training.ipynb)
 
 For notebooks on training models by using deep-learning-framework specific estimators, see:
-* [how-to-use-azureml/training-with-deep-learning](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/training-with-deep-learning)
+
+* [how-to-use-azureml/ml-frameworks](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks)
 
 [!INCLUDE [aml-clone-in-azure-notebook](../../../includes/aml-clone-for-examples.md)]
 

articles/machine-learning/service/how-to-use-secrets-in-runs.md

@@ -9,27 +9,27 @@ ms.reviewer: larryfr
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: conceptual
-ms.date: 08/23/2019
+ms.date: 11/08/2019
 ms.custom: seodec18
 
 ---
 
 # Use secrets in training runs
 [!INCLUDE [applies-to-skus](../../../includes/aml-applies-to-basic-enterprise-sku.md)]
 
-In this article, you learn how to use secrets in training runs securely. For example, to connect to an external database to query training data, you would need to pass username and password to the remote run context. Coding such values into training scripts in cleartext is insecure as it would expose the secret. 
+In this article, you learn how to use secrets in training runs securely. For example, to connect to an external database to query training data, you would need to pass your username and password to the remote run context. Coding such values into training scripts in cleartext is insecure as it would expose the secret. 
 
 Instead, your Azure Machine Learning Workspace has [Azure Key Vault](https://docs.microsoft.com/azure/key-vault/key-vault-overview) as associated resource. This Key Vault can be used for passing secrets to remote runs securely through a set of APIs in Azure Machine Learning Python SDK.
 
 The basic flow for using secrets is:
  1. On local computer, log in to Azure and connect to your Workspace.
  2. On local computer, set a secret in Workspace Key Vault.
  3. Submit a remote run.
- 4. Within remote run, get the secret from Key Value and use it.
+ 4. Within the remote run, get the secret from Key Value and use it.
 
 ## Set secrets
 
-In Azure Machine Learning Python SDK, the [Keyvault](https://docs.microsoft.com/python/api/azureml-core/azureml.core.keyvault.keyvault?view=azure-ml-py) class contains methods for setting secrets. In your local Python session, first obtain a reference to Workspace Key Vault, and then use [set_secret](https://docs.microsoft.com/python/api/azureml-core/azureml.core.keyvault.keyvault?view=azure-ml-py#set-secret-name--value-) method to set a secret by name and value.
+In the Azure Machine Learning Python SDK, the [Keyvault](https://docs.microsoft.com/python/api/azureml-core/azureml.core.keyvault.keyvault?view=azure-ml-py) class contains methods for setting secrets. In your local Python session, first obtain a reference to Workspace Key Vault, and then use [set_secret](https://docs.microsoft.com/python/api/azureml-core/azureml.core.keyvault.keyvault?view=azure-ml-py#set-secret-name--value-) method to set a secret by name and value.
 
 ```python
 from azureml.core import Workspace
@@ -41,15 +41,15 @@ keyvault = ws.get_default_keyvault()
 keyvault.set_secret(name="mysecret", value = my_secret)
 ```
 
-Do not put the secret value in Python code as it is insecure to store it in file as cleartext. Instead, obtain the secret value from environment variable, for example Azure DevOps build secret, or from interactive user input.
+Do not put the secret value in your Python code as it is insecure to store it in file as cleartext. Instead, obtain the secret value from an environment variable, for example Azure DevOps build secret, or from interactive user input.
 
-You can list secret names using [list_secrets](https://docs.microsoft.com/python/api/azureml-core/azureml.core.keyvault.keyvault?view=azure-ml-py#list-secrets--) method. The __set_secret__ method updates the secret value if the name already exists.
+You can list secret names using the [list_secrets](https://docs.microsoft.com/python/api/azureml-core/azureml.core.keyvault.keyvault?view=azure-ml-py#list-secrets--) method. The __set_secret__ method updates the secret value if the name already exists.
 
 ## Get secrets
 
-In your local code, you can use [Keyvault.get_secret](https://docs.microsoft.com/python/api/azureml-core/azureml.core.keyvault.keyvault?view=azure-ml-py#get-secret-name-) method to get the secret value by name.
+In your local code, you can use the[Keyvault.get_secret](https://docs.microsoft.com/python/api/azureml-core/azureml.core.keyvault.keyvault?view=azure-ml-py#get-secret-name-) method to get the secret value by name.
 
-In runs submitted using [Experiment.submit](https://docs.microsoft.com/python/api/azureml-core/azureml.core.experiment.experiment?view=azure-ml-py#submit-config--tags-none----kwargs-), use [Run.get_secret](https://docs.microsoft.com/python/api/azureml-core/azureml.core.run.run?view=azure-ml-py#get-secret-name-) method. Because a submitted run is aware of its Workspace, this method shortcuts the Workspace instantiation and returns the secret value directly.
+In runs submitted using  [Experiment.submit](https://docs.microsoft.com/python/api/azureml-core/azureml.core.experiment.experiment?view=azure-ml-py#submit-config--tags-none----kwargs-), use the [Run.get_secret](https://docs.microsoft.com/python/api/azureml-core/azureml.core.run.run?view=azure-ml-py#get-secret-name-) method. Because a submitted run is aware of its Workspace, this method shortcuts the Workspace instantiation and returns the secret value directly.
 
 ```python
 # Code in submitted run