from azureml.core import Environment from azureml.core.conda_dependencies import CondaDependencies from azureml.core.runconfig import RunConfiguration # Create a Python environment for the experiment diabetes_env = Environment("diabetes-pipeline-env") # Create a set of package dependencies diabetes_packages = CondaDependencies.create(conda_packages=['scikit-learn','ipykernel','matplotlib','pandas','pip'], pip_packages=['azureml-defaults','azureml-dataprep[pandas]','pyarrow']) # Add the dependencies to the environment diabetes_env.python.conda_dependencies = diabetes_packages # Register the environment diabetes_env.register(workspace=ws) registered_env = Environment.get(ws, 'diabetes-pipeline-env') # Create a new runconfig object for the pipeline pipeline_run_config = RunConfiguration() # Use the compute you created above. pipeline_run_config.target = pipeline_cluster # Assign the environment to the run configuration pipeline_run_config.environment = registered_env print ("Run configuration created.")