documenting v1 known issue

Author: Larry Franks

articles/machine-learning/v1/how-to-debug-pipelines.md

@@ -267,6 +267,86 @@ For more information on using the OpenCensus Python library in this manner, see
 
 In some cases, you may need to interactively debug the Python code used in your ML pipeline. By using Visual Studio Code (VS Code) and debugpy, you can attach to the code as it runs in the training environment. For more information, visit the [interactive debugging in VS Code guide](../how-to-debug-visual-studio-code.md#debug-and-troubleshoot-machine-learning-pipelines).
 
+## HyperdriveStep and AutoMLStep fail with network isolation
+
+After using HyperdriveStep and AutoMLStep, when you attempt to register the model you may receive an error.
+
+* You are using Azure Machine Learning SDK v1.
+* Your Azure Machine Learning workspace is configured for network isolation (VNet).
+* Your pipeline attempts to register the model generated by the previous step. For example, in the following code the `inputs` parameter is the saved_model from a HyperdriveStep:
+
+    ```python
+    register_model_step = PythonScriptStep(script_name='register_model.py',
+                                       name="register_model_step01",
+                                       inputs=[saved_model],
+                                       compute_target=cpu_cluster,
+                                       arguments=["--saved-model", saved_model],
+                                       allow_reuse=True,
+                                       runconfig=rcfg)
+    ```
+
+### Workaround
+
+> [!IMPORTANT]
+> This behavior does not occur when using Azure Machine Learning SDK v2.
+
+To work around this error, use the [Run](/python/api/azureml-core/azureml.core.run.run) class to get the model created from the HyperdriveStep or AutoMLStep. The following is an example script that gets the output model from a HyperdriveStep:
+
+```python
+%%writefile $script_folder/model_download9.py
+import argparse
+from azureml.core import Run
+from azureml.pipeline.core import PipelineRun
+from azureml.core.experiment import Experiment
+from azureml.train.hyperdrive import HyperDriveRun
+from azureml.pipeline.steps import HyperDriveStepRun
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--hd_step_name', 
+        type=str, dest='hd_step_name', 
+        help='The name of the step that runs AutoML training within this pipeline')
+        
+        
+    
+    args = parser.parse_args()
+    
+    current_run = Run.get_context()
+
+    pipeline_run = PipelineRun(current_run.experiment, current_run.experiment.name)
+
+    hd_step_run = HyperDriveStepRun((pipeline_run.find_step_run(args.hd_step_name))[0])
+    hd_best_run = hd_step_run.get_best_run_by_primary_metric()
+
+    print(hd_best_run)
+    hd_best_run.download_file("outputs/model/saved_model.pb", "saved_model.pb")
+    
+    
+    print("Successfully downloaded model") 
+```
+
+The file can then be used from a PythonScriptStep:
+
+```python
+from azureml.pipeline.steps import PythonScriptStep
+conda_dep = CondaDependencies()
+conda_dep.add_pip_package("azureml-sdk")
+conda_dep.add_pip_package("azureml-pipeline")
+
+rcfg = RunConfiguration(conda_dependencies=conda_dep)
+
+model_download_step = PythonScriptStep(
+    name="Download Model 9",
+    script_name="model_download9.py", 
+    arguments=["--hd_step_name", hd_step_name],
+    compute_target=compute_target,
+    source_directory=script_folder,
+    allow_reuse=False,
+    runconfig=rcfg
+)
+```
+
 ## Next steps
 
 * For a complete tutorial using `ParallelRunStep`, see [Tutorial: Build an Azure Machine Learning pipeline for batch scoring](../tutorial-pipeline-batch-scoring-classification.md).