MMLBaseEstimatorRunConfig Class

Abstract base class for all Estimator run configs.

DEPRECATED. Use the RunConfiguration class.

Initialize the MMLBaseEstimatorRunConfig.

Inheritance

RunConfiguration

MMLBaseEstimatorRunConfig

Constructor

MMLBaseEstimatorRunConfig(compute_target, vm_size=None, vm_priority=None, entry_script=None, script_params=None, node_count=None, process_count_per_node=None, distributed_backend=None, use_gpu=None, use_docker=None, custom_docker_base_image=None, custom_docker_image=None, image_registry_details=None, user_managed=False, conda_packages=None, pip_packages=None, environment_definition=None, inputs=None, source_directory_data_store=None, shm_size=None)

Parameters

Name	Description
compute_target Required	AbstractComputeTarget or str The compute target where training will happen. This can either be an object or the string "local".
vm_size	str The VM size of the compute target that will be created for the training. Supported values: Any Azure VM size. default value: None
vm_priority	str The VM priority of the compute target that will be created for the training. If not specified, 'dedicated' is used. Supported values: 'dedicated' and 'lowpriority'. This takes effect only when the vm_size parameter is specified in the input. default value: None
entry_script	str The relative path to the file used to start training. default value: None
script_params	dict A dictionary containing parameters that will be passed as arguments to the entry_script. default value: None
node_count	int The number of nodes in the compute target used for training. Only the the AmlCompute target is supported for distributed training (node_count > 1). default value: None
process_count_per_node	int When using MPI as an execution backend, the number of processes per node. default value: None
distributed_backend	str The communication backend for distributed training. Supported values: 'mpi' and 'ps'. 'mpi': MPI/Horovod 'ps': parameter server This parameter is required when any of node_count, process_count_per_node, worker_count, or parameter_server_count > 1. When node_count == 1 and process_count_per_node == 1, no backend will be used unless a backend is explicitly set. Only the azureml.core.compute.AmlCompute target is supported for distributed training. default value: None
use_gpu	bool Specifies whether the environment to run the experiment should support GPUs. If true, a GPU-based default Docker image will be used in the environment. If false, a CPU-based image will be used. Default Docker images (CPU or GPU) will be used only if the custom_docker_image parameter is not set. This setting is used only in Docker-enabled compute targets. default value: None
use_docker	bool Specifies whether the environment to run the experiment should be Docker-based. default value: None
custom_docker_base_image	str The name of the Docker image from which the image to use for training will be built. DEPRECATED. Use the custom_docker_image parameter. If not set, a default CPU-based image will be used as the base image. default value: None
custom_docker_image	str The name of the Docker image from which the image to use for training will be built. If not set, a default CPU-based image will be used as the base image. default value: None
image_registry_details	ContainerRegistry The details of the Docker image registry. default value: None
user_managed	bool Specifies whether Azure ML reuses an existing Python environment. If false, a Python environment is created based on the conda dependencies specification. default value: False
conda_packages	list List of strings representing conda packages to be added to the Python environment for the experiment. default value: None
pip_packages	list A list of strings representing pip packages to be added to the Python environment for the experiment. default value: None
environment_definition	Environment The environment definition for the experiment. It includes PythonSection, DockerSection, and environment variables. Any environment option not directly exposed through other parameters to the Estimator construction can be set using this parameter. If this parameter is specified, it will take precedence over other environment related parameters like use_gpu, custom_docker_image, conda_packages, or pip_packages and errors will be reported on these invalid combinations. default value: None
inputs	list A list of DataReference or DatasetConsumptionConfig objects to use as input. default value: None
source_directory_data_store	str The backing data store for the project share. default value: None
shm_size	str The size of the Docker container's shared memory block. For more information, see Docker run reference. If not set, the default azureml.core.environment._DEFAULT_SHM_SIZE is used. default value: None