Estimator Class
Represents a generic estimator to train data using any supplied framework.
DEPRECATED. Use the ScriptRunConfig object with your own defined environment or an Azure ML curated environment. For an introduction to configuring experiment runs with ScriptRunConfig, see Configure and submit training runs.
This class is designed for use with machine learning frameworks that do not already have an Azure Machine Learning pre-configured estimator. Pre-configured estimators exist for Chainer, PyTorch, TensorFlow, and SKLearn. To create an Estimator that is not preconfigured, see Train models with Azure Machine Learning using estimator.
The Estimator class wraps run configuration information to help simplify the tasks of specifying how a script is executed. It supports single-node as well as multi-node execution. Running the estimator produces a model in the output directory specified in your training script.
Initialize the estimator.
azureml.core.environment._DEFAULT_SHM_SIZE is used. For more information, see Docker run reference. :type shm_size: str :param resume_from: The data path containing the checkpoint or model files from which to resume the experiment. :type resume_from: azureml.data.datapath.DataPath :param max_run_duration_seconds: The maximum allowed time for the run. Azure ML will attempt to automatically
cancel the run if it takes longer than this value.
- Inheritance
-
azureml.train.estimator._mml_base_estimator.MMLBaseEstimatorEstimator
Constructor
Estimator(source_directory, *, compute_target=None, vm_size=None, vm_priority=None, entry_script=None, script_params=None, node_count=1, process_count_per_node=1, distributed_backend=None, distributed_training=None, use_gpu=False, use_docker=True, custom_docker_base_image=None, custom_docker_image=None, image_registry_details=None, user_managed=False, conda_packages=None, pip_packages=None, conda_dependencies_file_path=None, pip_requirements_file_path=None, conda_dependencies_file=None, pip_requirements_file=None, environment_variables=None, environment_definition=None, inputs=None, source_directory_data_store=None, shm_size=None, resume_from=None, max_run_duration_seconds=None, _disable_validation=True, _show_lint_warnings=False, _show_package_warnings=False)Parameters
- source_directory
- str
A local directory containing experiment configuration and code files needed for a training job.
- compute_target
- 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.
- 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.
- script_params
- dict
A dictionary of command-line arguments to pass to the training script specified in
entry_script.
- node_count
- int
The number of nodes in the compute target used for training. If greater than 1, an MPI distributed job will be run.
- process_count_per_node
- int
The number of processes (or "workers") to run on each node. If greater than 1, an MPI distributed job will be run. Only the AmlCompute target is supported for distributed jobs.
- distributed_backend
- str
The communication backend for distributed training.
DEPRECATED. Use the distributed_training parameter.
Supported values: 'mpi'. 'mpi' represents MPI/Horovod.
This parameter is required when node_count or process_count_per_node > 1.
When node_count == 1 and process_count_per_node == 1, no backend will be used
unless the backend is explicitly set. Only the AmlCompute target is supported for
distributed training.
- distributed_training
- Mpi
Parameters for running a distributed training job.
For running a distributed job with MPI backend, use Mpi
object to specify process_count_per_node.
- use_gpu
- bool
Indicates 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.
- use_docker
- bool
Specifies whether the environment to run the experiment should be Docker-based.
- 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.
- 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. Only specify
images available in public docker repositories (Docker Hub). To use an image from a private docker
repository, use the constructor's environment_definition parameter instead.
- 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.
- conda_packages
- list
A list of strings representing conda packages to be added to the Python environment for the experiment.
- pip_packages
- list
A list of strings representing pip packages to be added to the Python environment for the experiment.
- conda_dependencies_file_path
- str
The relative path to the conda dependencies yaml file. If specified, Azure ML will not install any framework related packages.
DEPRECATED. Use the conda_dependencies_file paramenter.
Specify either conda_dependencies_file_path or conda_dependencies_file. If both are specified,
conda_dependencies_file is used.
- pip_requirements_file_path
- str
The relative path to the pip requirements text file.
DEPRECATED. Use the pip_requirements_file parameter.
This parameter can be specified in combination with the pip_packages parameter. Specify either
pip_requirements_file_path or pip_requirements_file. If both are specified,
pip_requirements_file is used.
- conda_dependencies_file
- str
The relative path to the conda dependencies yaml file. If specified, Azure ML will not install any framework related packages.
- pip_requirements_file
- str
The relative path to the pip requirements text file.
This parameter can be specified in combination with the pip_packages parameter.
- environment_variables
- dict
A dictionary of environment variables names and values. These environment variables are set on the process where user script is being executed.
- 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.
Errors will be reported on invalid combinations.
- shm_size
- str
The size of the Docker container's shared memory block. If not set, the default azureml.core.environment._DEFAULT_SHM_SIZE is used. For more information, see Docker run reference.
- resume_from
- DataPath
The data path containing the checkpoint or model files from which to resume the experiment.
- max_run_duration_seconds
- int
The maximum allowed time for the run. Azure ML will attempt to automatically cancel the run if it take longer than this value.
- source_directory
- str
A local directory containing experiment configuration and code files needed for a training job.
- compute_target
- 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.
- 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.
- script_params
- dict
A dictionary of command-line arguments to pass to the training script specified in
entry_script.
- node_count
- int
The number of nodes in the compute target used for training. If greater than 1, a MPI distributed job will be run. Only the AmlCompute target is supported for distributed jobs.
- process_count_per_node
- int
The number of processes per node. If greater than 1, a MPI distributed job will be run. Only the AmlCompute target is supported for distributed jobs.
- distributed_backend
- str
The communication backend for distributed training.
DEPRECATED. Use the distributed_training parameter.
Supported values: 'mpi'. 'mpi' represents MPI/Horovod.
This parameter is required when node_count or process_count_per_node > 1.
When node_count == 1 and process_count_per_node == 1, no backend will be used
unless the backend is explicitly set. Only the AmlCompute target is
supported for distributed training.
- distributed_training
- Mpi
Parameters for running a distributed training job.
For running a distributed job with MPI backend, use Mpi
object to specify process_count_per_node.
- 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.
- use_docker
- bool
Specifies whether the environment to run the experiment should be Docker-based.
- 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.
- 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. Only specify
images available in public docker repositories (Docker Hub). To use an image from a private docker
repository, use the constructor's environment_definition parameter instead.
- 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.
- conda_packages
- list
A list of strings representing conda packages to be added to the Python environment for the experiment.
- pip_packages
- list
A list of strings representing pip packages to be added to the Python environment for the experiment.
- conda_dependencies_file_path
The relative path to the conda dependencies yaml file. If specified, Azure ML will not install any framework related packages.
DEPRECATED. Use the conda_dependencies_file paramenter.
Specify either conda_dependencies_file_path or conda_dependencies_file. If both are specified,
conda_dependencies_file is used.
- pip_requirements_file_path
The relative path to the pip requirements text file.
DEPRECATED. Use the pip_requirements_file parameter.
This can be provided in combination with the pip_packages parameter. Specify either
pip_requirements_file_path or pip_requirements_file. If both are specified,
pip_requirements_file is used.
- pip_requirements_file
- str
The relative path to the pip requirements text file.
This can be provided in combination with the pip_packages parameter.
- environment_variables
- dict
A dictionary of environment variables names and values. These environment variables are set on the process where user script is being executed.
- 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.
Errors will be reported on invalid combinations.
- shm_size
The size of the Docker container's shared memory block. If not set, the default
- _disable_validation
- bool
Disable script validation before run submission. The default is True.
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