def create_run_config(cpu_cluster, docker_proc_type, conda_env_file):
    """
    AzureML requires the run environment to be setup prior to submission.
    This configures a docker persistent compute.  Even though
    it is called Persistent compute, AzureML handles startup/shutdown
    of the compute environment.

    Args:
        cpu_cluster      (str) : Names the cluster for the test
                                 In the case of unit tests, any of
                                 the following:
                                 - Reco_cpu_test
                                 - Reco_gpu_test
        docker_proc_type (str) : processor type, cpu or gpu
        conda_env_file   (str) : filename which contains info to
                                 set up conda env
    Return:
          run_amlcompute : AzureML run config
    """

    # runconfig with max_run_duration_seconds did not work, check why:
    # run_amlcompute = RunConfiguration(max_run_duration_seconds=60*30)
    run_amlcompute = RunConfiguration()
    run_amlcompute.target = cpu_cluster
    run_amlcompute.environment.docker.enabled = True
    run_amlcompute.environment.docker.base_image = docker_proc_type

    # Use conda_dependencies.yml to create a conda environment in
    # the Docker image for execution
    # False means the user will provide a conda file for setup
    # True means the user will manually configure the environment
    run_amlcompute.environment.python.user_managed_dependencies = False
    run_amlcompute.environment.python.conda_dependencies = CondaDependencies(
            conda_dependencies_file_path=conda_env_file)
    return run_amlcompute