ml_service/pipelines/diabetes_regression_build_train_pipeline.py [13:38]:
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def main():
    e = Env()
    # Get Azure machine learning workspace
    aml_workspace = Workspace.get(
        name=e.workspace_name,
        subscription_id=e.subscription_id,
        resource_group=e.resource_group,
    )
    print("get_workspace:")
    print(aml_workspace)

    # Get Azure machine learning cluster
    aml_compute = get_compute(aml_workspace, e.compute_name, e.vm_size)
    if aml_compute is not None:
        print("aml_compute:")
        print(aml_compute)

    # Create a reusable Azure ML environment
    environment = get_environment(
        aml_workspace,
        e.aml_env_name,
        conda_dependencies_file=e.aml_env_train_conda_dep_file,
        create_new=e.rebuild_env,
    )  #
    run_config = RunConfiguration()
    run_config.environment = environment
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ml_service/pipelines/diabetes_regression_build_train_pipeline_with_r.py [10:37]:
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def main():
    e = Env()
    # Get Azure machine learning workspace
    aml_workspace = Workspace.get(
        name=e.workspace_name,
        subscription_id=e.subscription_id,
        resource_group=e.resource_group,
    )
    print("get_workspace:")
    print(aml_workspace)

    # Get Azure machine learning cluster
    aml_compute = get_compute(aml_workspace, e.compute_name, e.vm_size)
    if aml_compute is not None:
        print("aml_compute:")
        print(aml_compute)

    # Create a reusable Azure ML environment
    # Make sure to include `r-essentials'
    #   in diabetes_regression/conda_dependencies.yml
    environment = get_environment(
        aml_workspace,
        e.aml_env_name,
        conda_dependencies_file=e.aml_env_train_conda_dep_file,
        create_new=e.rebuild_env,
    )  # NOQA: E501
    run_config = RunConfiguration()
    run_config.environment = environment
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