fixing links

Larry Franks · Larry Franks · commit e0f58c27beba · 2022-09-30T14:41:34.000-04:00
diff --git a/articles/machine-learning/how-to-train-keras.md b/articles/machine-learning/how-to-train-keras.md
@@ -41,7 +41,7 @@ Run this code on either of these environments:
  - Your own Jupyter Notebook server
 
     - [Install the Azure Machine Learning SDK](/python/api/overview/azure/ml/install) (>= 1.15.0).
-    - [Create a workspace configuration file](v1/how-to-configure-environment-v1.md#workspace).
+    - [Create a workspace configuration file](v1/how-to-configure-environment-v1.md).
     - [Download the sample script files](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras) `keras_mnist.py` and `utils.py`
 
     You can also find a completed [Jupyter Notebook version](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb) of this guide on the GitHub samples page. The notebook includes expanded sections covering intelligent hyperparameter tuning, model deployment, and notebook widgets.
diff --git a/articles/machine-learning/how-to-train-pytorch.md b/articles/machine-learning/how-to-train-pytorch.md
@@ -34,7 +34,7 @@ Run this code on either of these environments:
 
  - Your own Jupyter Notebook server
     - [Install the Azure Machine Learning SDK](/python/api/overview/azure/ml/install) (>= 1.15.0).
-    - [Create a workspace configuration file](v1/how-to-configure-environment-v1.md#workspace).
+    - [Create a workspace configuration file](v1/how-to-configure-environment-v1.md).
     - [Download the sample script files](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch) `pytorch_train.py`
      
     You can also find a completed [Jupyter Notebook version](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) of this guide on the GitHub samples page. The notebook includes expanded sections covering intelligent hyperparameter tuning, model deployment, and notebook widgets.
diff --git a/articles/machine-learning/how-to-train-scikit-learn.md b/articles/machine-learning/how-to-train-scikit-learn.md
@@ -35,7 +35,7 @@ You can run this code in either an Azure Machine Learning compute instance, or y
  - Create a Jupyter Notebook server and run the code in the following sections.
 
     - [Install the Azure Machine Learning SDK](/python/api/overview/azure/ml/install) (>= 1.13.0).
-    - [Create a workspace configuration file](v1/how-to-configure-environment-v1.md#workspace).
+    - [Create a workspace configuration file](v1/how-to-configure-environment-v1.md).
 
 ## Set up the experiment
 
diff --git a/articles/machine-learning/how-to-train-tensorflow.md b/articles/machine-learning/how-to-train-tensorflow.md
@@ -33,7 +33,7 @@ Run this code on either of these environments:
 
 - Your own Jupyter Notebook server
     - [Install the Azure Machine Learning SDK](/python/api/overview/azure/ml/install) (>= 1.15.0).
-    - [Create a workspace configuration file](v1/how-to-configure-environment-v1.md#workspace).
+    - [Create a workspace configuration file](v1/how-to-configure-environment-v1.md).
     - [Download the sample script files](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow) `tf_mnist.py` and `utils.py`
 
     You can also find a completed [Jupyter Notebook version](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) of this guide on the GitHub samples page. The notebook includes expanded sections covering intelligent hyperparameter tuning, model deployment, and notebook widgets.
diff --git a/articles/machine-learning/v1/how-to-configure-environment-v1.md b/articles/machine-learning/v1/how-to-configure-environment-v1.md
@@ -97,7 +97,7 @@ To configure a local development environment or remote VM:
 
 1. Activate your newly created Python virtual environment.
 1. Install the [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/install).
-1. To configure your local environment to use your Azure Machine Learning workspace, [create a workspace configuration file](#workspace) or use an existing one.
+1. To configure your local environment to use your Azure Machine Learning workspace, [create a workspace configuration file](#local-and-dsvm-only-create-a-workspace-configuration-file) or use an existing one.
 
 Now that you have your local environment set up, you're ready to start working with Azure Machine Learning. See the [Azure Machine Learning Python getting started guide](tutorial-1st-experiment-hello-world.md) to get started.
 
@@ -207,7 +207,7 @@ To use the Data Science VM as a development environment:
         conda activate AzureML
         ```
 
-1. To configure the Data Science VM to use your Azure Machine Learning workspace, [create a workspace configuration file](#workspace) or use an existing one.
+1. To configure the Data Science VM to use your Azure Machine Learning workspace, [create a workspace configuration file](#local-and-dsvm-only-create-a-workspace-configuration-file) or use an existing one.
 
 Similar to local environments, you can use Visual Studio Code and the [Azure Machine Learning Visual Studio Code extension](#visual-studio-code) to interact with Azure Machine Learning.
 
diff --git a/articles/machine-learning/v1/how-to-deploy-inferencing-gpus.md b/articles/machine-learning/v1/how-to-deploy-inferencing-gpus.md
@@ -53,7 +53,7 @@ Inference, or model scoring, is the phase where the deployed model is used to ma
 To connect to an existing workspace, use the following code:
 
 > [!IMPORTANT]
-> This code snippet expects the workspace configuration to be saved in the current directory or its parent. For more information on creating a workspace, see [Create workspace resources](../quickstart-create-resources.md).   For more information on saving the configuration to file, see [Create a workspace configuration file](how-to-configure-environment-v1.md#workspace).
+> This code snippet expects the workspace configuration to be saved in the current directory or its parent. For more information on creating a workspace, see [Create workspace resources](../quickstart-create-resources.md).   For more information on saving the configuration to file, see [Create a workspace configuration file](how-to-configure-environment-v1.md).
 
 ```python
 from azureml.core import Workspace
diff --git a/articles/machine-learning/v1/how-to-train-keras.md b/articles/machine-learning/v1/how-to-train-keras.md
@@ -41,7 +41,7 @@ Run this code on either of these environments:
  - Your own Jupyter Notebook server
 
     - [Install the Azure Machine Learning SDK](/python/api/overview/azure/ml/install) (>= 1.15.0).
-    - [Create a workspace configuration file](how-to-configure-environment-v1.md#workspace).
+    - [Create a workspace configuration file](how-to-configure-environment-v1.md).
     - [Download the sample script files](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras) `keras_mnist.py` and `utils.py`
 
     You can also find a completed [Jupyter Notebook version](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb) of this guide on the GitHub samples page. The notebook includes expanded sections covering intelligent hyperparameter tuning, model deployment, and notebook widgets.
diff --git a/articles/machine-learning/v1/how-to-train-pytorch.md b/articles/machine-learning/v1/how-to-train-pytorch.md
@@ -34,7 +34,7 @@ Run this code on either of these environments:
 
  - Your own Jupyter Notebook server
     - [Install the Azure Machine Learning SDK](/python/api/overview/azure/ml/install) (>= 1.15.0).
-    - [Create a workspace configuration file](how-to-configure-environment-v1.md#workspace).
+    - [Create a workspace configuration file](how-to-configure-environment-v1.md).
     - [Download the sample script files](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch) `pytorch_train.py`
      
     You can also find a completed [Jupyter Notebook version](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) of this guide on the GitHub samples page. The notebook includes expanded sections covering intelligent hyperparameter tuning, model deployment, and notebook widgets.
diff --git a/articles/machine-learning/v1/how-to-train-scikit-learn.md b/articles/machine-learning/v1/how-to-train-scikit-learn.md
@@ -35,7 +35,7 @@ You can run this code in either an Azure Machine Learning compute instance, or y
  - Create a Jupyter Notebook server and run the code in the following sections.
 
     - [Install the Azure Machine Learning SDK](/python/api/overview/azure/ml/install) (>= 1.13.0).
-    - [Create a workspace configuration file](how-to-configure-environment-v1.md#workspace).
+    - [Create a workspace configuration file](how-to-configure-environment-v1.md).
 
 ## Set up the experiment
 
diff --git a/articles/machine-learning/v1/how-to-train-tensorflow.md b/articles/machine-learning/v1/how-to-train-tensorflow.md
@@ -33,7 +33,7 @@ Run this code on either of these environments:
 
 - Your own Jupyter Notebook server
     - [Install the Azure Machine Learning SDK](/python/api/overview/azure/ml/install) (>= 1.15.0).
-    - [Create a workspace configuration file](how-to-configure-environment-v1.md#workspace).
+    - [Create a workspace configuration file](how-to-configure-environment-v1.md).
     - [Download the sample script files](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow) `tf_mnist.py` and `utils.py`
 
     You can also find a completed [Jupyter Notebook version](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) of this guide on the GitHub samples page. The notebook includes expanded sections covering intelligent hyperparameter tuning, model deployment, and notebook widgets.
diff --git a/articles/machine-learning/v1/how-to-use-automlstep-in-pipelines.md b/articles/machine-learning/v1/how-to-use-automlstep-in-pipelines.md
@@ -65,7 +65,7 @@ if not 'titanic_ds' in ws.datasets.keys() :
 titanic_ds = Dataset.get_by_name(ws, 'titanic_ds')
 ```
 
-The code first logs in to the Azure Machine Learning workspace defined in **config.json** (for an explanation, see [Create a workspace configuration file](how-to-configure-environment-v1.md#workspace). If there isn't already a dataset named `'titanic_ds'` registered, then it creates one. The code downloads CSV data from the Web, uses them to instantiate a `TabularDataset` and then registers the dataset with the workspace. Finally, the function `Dataset.get_by_name()` assigns the `Dataset` to `titanic_ds`. 
+The code first logs in to the Azure Machine Learning workspace defined in **config.json** (for an explanation, see [Create a workspace configuration file](how-to-configure-environment-v1.md). If there isn't already a dataset named `'titanic_ds'` registered, then it creates one. The code downloads CSV data from the Web, uses them to instantiate a `TabularDataset` and then registers the dataset with the workspace. Finally, the function `Dataset.get_by_name()` assigns the `Dataset` to `titanic_ds`. 
 
 ### Configure your storage and compute target
 
diff --git a/articles/machine-learning/v1/how-to-use-reinforcement-learning.md b/articles/machine-learning/v1/how-to-use-reinforcement-learning.md
@@ -46,7 +46,7 @@ Run this code in either of these environments. We recommend you try Azure Machin
 
     - Install the [Azure Machine Learning SDK](/python/api/overview/azure/ml/install).
     - Install the [Azure Machine Learning RL SDK](/python/api/azureml-contrib-reinforcementlearning/): `pip install --upgrade azureml-contrib-reinforcementlearning`
-    - Create a [workspace configuration file](how-to-configure-environment-v1.md#workspace).
+    - Create a [workspace configuration file](how-to-configure-environment-v1.md).
     - Run the virtual network to open network ports used for distributed reinforcement learning.
 
 
diff --git a/articles/machine-learning/v1/how-to-use-synapsesparkstep.md b/articles/machine-learning/v1/how-to-use-synapsesparkstep.md
@@ -70,7 +70,7 @@ for service in LinkedService.list(ws) :
 linked_service = LinkedService.get(ws, 'synapselink1')
 ```
 
-First, `Workspace.from_config()` accesses your Azure Machine Learning workspace using the configuration in `config.json` (see [Create a workspace configuration file](how-to-configure-environment-v1.md#workspace)). Then, the code prints all of the linked services available in the Workspace. Finally, `LinkedService.get()` retrieves a linked service named `'synapselink1'`. 
+First, `Workspace.from_config()` accesses your Azure Machine Learning workspace using the configuration in `config.json` (see [Create a workspace configuration file](how-to-configure-environment-v1.md)). Then, the code prints all of the linked services available in the Workspace. Finally, `LinkedService.get()` retrieves a linked service named `'synapselink1'`. 
 
 ## Attach your Apache spark pool as a compute target for Azure Machine Learning
 
diff --git a/articles/machine-learning/v1/tutorial-pipeline-python-sdk.md b/articles/machine-learning/v1/tutorial-pipeline-python-sdk.md
@@ -92,7 +92,7 @@ workspace = Workspace.from_config()
 ```
 
 > [!IMPORTANT]
-> This code snippet expects the workspace configuration to be saved in the current directory or its parent. For more information on creating a workspace, see [Create workspace resources](../quickstart-create-resources.md). For more information on saving the configuration to file, see [Create a workspace configuration file](how-to-configure-environment-v1.md#workspace).
+> This code snippet expects the workspace configuration to be saved in the current directory or its parent. For more information on creating a workspace, see [Create workspace resources](../quickstart-create-resources.md). For more information on saving the configuration to file, see [Create a workspace configuration file](how-to-configure-environment-v1.md).
 
 ## Create the infrastructure for your pipeline 
 
