create_run_configuration

health_azure.create_run_configuration(workspace, compute_cluster_name, conda_environment_file=None, aml_environment_name='', environment_variables=None, pip_extra_index_url='', private_pip_wheel_path=None, docker_base_image='', docker_shm_size='', num_nodes=1, max_run_duration='', input_datasets=None, output_datasets=None)[source]

Creates an AzureML run configuration, that contains information about environment, multi node execution, and Docker.

Parameters

  • workspace (Workspace) – The AzureML Workspace to use.

  • aml_environment_name (str) – The name of an AzureML environment that should be used to submit the script. If not provided, an environment will be created from the arguments to this function (conda_environment_file, pip_extra_index_url, environment_variables, docker_base_image)

  • max_run_duration (str) – The maximum runtime that is allowed for this job in AzureML. This is given as a floating point number with a string suffix s, m, h, d for seconds, minutes, hours, day. Examples: ‘3.5h’, ‘2d’

  • compute_cluster_name (str) – The name of the AzureML cluster that should run the job. This can be a cluster with CPU or GPU machines.

  • conda_environment_file (Optional[Path]) – The conda configuration file that describes which packages are necessary for your script to run.

  • environment_variables (Optional[Dict[str, str]]) – The environment variables that should be set when running in AzureML.

  • docker_base_image (str) – The Docker base image that should be used when creating a new Docker image.

  • docker_shm_size (str) – The Docker shared memory size that should be used when creating a new Docker image.

  • pip_extra_index_url (str) – If provided, use this PIP package index to find additional packages when building the Docker image.

  • private_pip_wheel_path (Optional[Path]) – If provided, add this wheel as a private package to the AzureML workspace.

  • conda_environment_file – The file that contains the Conda environment definition.

  • input_datasets (Optional[List[DatasetConfig]]) – The script will consume all data in folder in blob storage as the input. The folder must exist in blob storage, in the location that you gave when creating the datastore. Once the script has run, it will also register the data in this folder as an AzureML dataset.

  • output_datasets (Optional[List[DatasetConfig]]) – The script will create a temporary folder when running in AzureML, and while the job writes data to that folder, upload it to blob storage, in the data store.

  • num_nodes (int) – The number of nodes to use in distributed training on AzureML.

Return type

RunConfiguration

Returns