updating image info

Author: Larry Franks

articles/machine-learning/how-to-access-resources-from-endpoints-managed-identities.md

@@ -341,9 +341,7 @@ The following Python endpoint object:
 * Assigns the name by which you want to refer to the endpoint to the variable `endpoint_name. 
 * Specifies the type of authorization to use to access the endpoint `auth-mode="key"`.
 
-```python
-endpoint = ManagedOnlineEndpoint(name=endpoint_name, auth_mode="key")
-``` 
+[!notebook-python[] (~/azureml-examples-main/sdk/python/endpoints/online/managed/managed-identities/online-endpoints-managed-identity-sai.ipynb?name=2-define-endpoint-configuration)]
 
 This deployment object: 
 
@@ -354,27 +352,7 @@ This deployment object:
 * Includes environment variables needed for the system-assigned managed identity to access storage.  
 
 
-```python
-deployment = ManagedOnlineDeployment(
-    name="blue",
-    endpoint_name=endpoint_name,
-    model=Model(path="../../model-1/model/"),
-    code_configuration=CodeConfiguration(
-        code="../../model-1/onlinescoring/", scoring_script="score_managedidentity.py"
-    ),
-    environment=Environment(
-        conda_file="../../model-1/environment/conda.yml",
-        image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:20210727.v1",
-    ),
-    instance_type="Standard_DS2_v2",
-    instance_count=1,
-    environment_variables={
-        "STORAGE_ACCOUNT_NAME": storage_account_name,
-        "STORAGE_CONTAINER_NAME": storage_container_name,
-        "FILE_NAME": file_name,
-    },
-)
-``` 
+[!notebook-python[] (~/azureml-examples-main/sdk/python/endpoints/online/managed/managed-identities/online-endpoints-managed-identity-sai.ipynb?name=2-define-deployment-configuration)]
 
 # [User-assigned (Python)](#tab/user-identity-python)
 
@@ -392,29 +370,7 @@ This deployment object:
 * Adds a placeholder environment variable for `UAI_CLIENT_ID`, which will be added after creating one and before actually deploying this configuration. 
 
 
-```python
-deployment = ManagedOnlineDeployment(
-    name="blue",
-    endpoint_name=endpoint_name,
-    model=Model(path="../../model-1/model/"),
-    code_configuration=CodeConfiguration(
-        code="../../model-1/onlinescoring/", scoring_script="score_managedidentity.py"
-    ),
-    environment=Environment(
-        conda_file="../../model-1/environment/conda.yml",
-        image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:20210727.v1",
-    ),
-    instance_type="Standard_DS2_v2",
-    instance_count=1,
-    environment_variables={
-        "STORAGE_ACCOUNT_NAME": storage_account_name,
-        "STORAGE_CONTAINER_NAME": storage_container_name,
-        "FILE_NAME": file_name,
-        # We will update this after creating an identity
-        "UAI_CLIENT_ID": "uai_client_id_place_holder",
-    },
-)
-``` 
+[!notebook-python[] (~/azureml-examples-main/sdk/python/endpoints/online/managed/managed-identities/online-endpoints-managed-identity-uai.ipynb?name=2-define-deployment-configuration)]
 
 ---
 

articles/machine-learning/how-to-debug-managed-online-endpoints-visual-studio-code.md

@@ -175,26 +175,7 @@ ml_client = MLClient(
 
 To debug online endpoints locally in VS Code, set the `vscode-debug` and `local` flags when creating or updating an Azure Machine Learning online deployment. The following code mirrors a deployment example from the examples repo:
 
-```python
-deployment = ManagedOnlineDeployment(
-    name="blue",
-    endpoint_name=endpoint_name,
-    model=Model(path="../model-1/model/sklearn_regression_model.pkl"),
-    code_configuration=CodeConfiguration(
-        code="../model-1/onlinescoring", scoring_script="score.py"
-    ),
-    environment=Environment(
-        conda_file="../model-1/environment/conda.yml",
-        image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:20210727.v1",
-    ),
-    instance_type="Standard_DS2_v2",
-    instance_count=1,
-)
-
-deployment = ml_client.online_deployments.begin_create_or_update(
-    deployment, local=True, vscode_debug=True
-)
-```
+[!notebook-python[] (~/azureml-examples-main/sdk/python/endpoints/online/managed/debug-online-endpoints-locally-in-visual-studio-code.ipynb?name=launch-container-4)]
 
 > [!IMPORTANT]
 > On Windows Subsystem for Linux (WSL), you'll need to update your PATH environment variable to include the path to the VS Code executable or use WSL interop. For more information, see [Windows interoperability with Linux](/windows/wsl/interop).
@@ -391,26 +372,7 @@ To apply changes to your code:
 
 For more extensive changes involving updates to your environment and endpoint configuration, use your `MLClient`'s `online_deployments.update` module/method. Doing so will trigger a full image rebuild with your changes.
 
-```python
-new_deployment = ManagedOnlineDeployment(
-    name="green",
-    endpoint_name=endpoint_name,
-    model=Model(path="../model-2/model/sklearn_regression_model.pkl"),
-    code_configuration=CodeConfiguration(
-        code="../model-2/onlinescoring", scoring_script="score.py"
-    ),
-    environment=Environment(
-        conda_file="../model-2/environment/conda.yml",
-        image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:20210727.v1",
-    ),
-    instance_type="Standard_DS2_v2",
-    instance_count=2,
-)
-
-deployment = ml_client.online_deployments.begin_create_or_update(
-    new_deployment, local=True, vscode_debug=True
-)
-```
+[!notebook-python[] (~/azureml-examples-main/sdk/python/endpoints/online/managed/debug-online-endpoints-locally-in-visual-studio.ipynb?name=edit-endpoint-1)]
 
 Once the updated image is built and your development container launches, use the VS Code debugger to test and troubleshoot your updated endpoint.
 

articles/machine-learning/how-to-deploy-automl-endpoint.md

@@ -251,7 +251,7 @@ Next, we'll create the managed online endpoints and deployments.
     model = Model(path="./src/model.pkl")
     env = Environment(
         conda_file="./src/conda_env_v_1_0_0.yml",
-        image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:20210727.v1",
+        image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:latest",
     )
 
     blue_deployment = ManagedOnlineDeployment(

articles/machine-learning/how-to-deploy-managed-online-endpoints.md

@@ -238,7 +238,7 @@ In this article, we first define names of online endpoint and deployment for deb
     model = Model(path="../model-1/model/sklearn_regression_model.pkl")
     env = Environment(
         conda_file="../model-1/environment/conda.yml",
-        image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:20210727.v1",
+        image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:latest",
     )
 
     blue_deployment = ManagedOnlineDeployment(
@@ -520,7 +520,7 @@ This deployment might take up to 15 minutes, depending on whether the underlying
     model = Model(path="../model-1/model/sklearn_regression_model.pkl")
     env = Environment(
         conda_file="../model-1/environment/conda.yml",
-        image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:20210727.v1",
+        image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:latest",
     )
 
     blue_deployment = ManagedOnlineDeployment(

articles/machine-learning/v1/how-to-train-tensorflow.md

@@ -137,10 +137,10 @@ To define the Azure ML [Environment](../concept-environments.md) that encapsulat
 
 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 TensorFlow corresponding to different versions of TensorFlow. For more info, see [Azure ML Curated Environments](../resource-curated-environments.md).
 
-If you want to use a curated environment, you can run the following command instead:
+If you want to use a curated environment, the code will be similar to the following example:
 
 ```python
-curated_env_name = 'AzureML-TensorFlow-2.2-GPU'
+curated_env_name = 'AzureML-tensorflow-2.7-ubuntu20.04-py38-cuda11-gpu'
 tf_env = Environment.get(workspace=ws, name=curated_env_name)
 ```
 
@@ -155,14 +155,14 @@ Make sure the curated environment includes all the dependencies required by your
 
 ```python
 
-tf_env = Environment.from_conda_specification(name='tensorflow-2.2-gpu', file_path='./conda_dependencies.yml')
+tf_env = Environment.from_conda_specification(name='AzureML-tensorflow-2.7-ubuntu20.04-py38-cuda11-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
 
-tf_env = tf_env.clone(new_name='tensorflow-2.2-gpu')
+tf_env = tf_env.clone(new_name='my-AzureML-tensorflow-2.7-ubuntu20.04-py38-cuda11-gpu')
 ```
 
 #### Create a custom environment
@@ -186,7 +186,7 @@ Create an Azure ML environment from this conda environment specification. The en
 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 GitHub repo](https://github.com/Azure/AzureML-Containers) for more information.
 
 ```python
-tf_env = Environment.from_conda_specification(name='tensorflow-2.2-gpu', file_path='./conda_dependencies.yml')
+tf_env = Environment.from_conda_specification(name='AzureML-tensorflow-2.7-ubuntu20.04-py38-cuda11-gpu', file_path='./conda_dependencies.yml')
 
 # Specify a GPU base image
 tf_env.docker.enabled = True