SKLearn Class

Reference

Creates an estimator for training in Scikit-learn experiments.

DEPRECATED. Use the ScriptRunConfig object with your own defined environment or the AzureML-Tutorial curated environment. For an introduction to configuring SKLearn experiment runs with ScriptRunConfig, see Train scikit-learn models at scale with Azure Machine Learning.

This estimator only supports single-node CPU training.

Supported versions: 0.20.3

Initialize a Scikit-learn estimator.

Inheritance: azureml.train.estimator._framework_base_estimator._FrameworkBaseEstimator

SKLearn

Constructor

SKLearn(source_directory, *, compute_target=None, vm_size=None, vm_priority=None, entry_script=None, script_params=None, use_docker=True, 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, shm_size=None, resume_from=None, max_run_duration_seconds=None, framework_version=None, _enable_optimized_mode=False, _disable_validation=True, _show_lint_warnings=False, _show_package_warnings=False)

Parameters

Name	Description
source_directory Required	str A local directory containing experiment configuration files.
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 Required	str The VM size of the compute target that will be created for the training. Supported values: Any Azure VM size.
vm_priority Required	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 param` is specified in the input.
entry_script Required	str A string representing the relative path to the file used to start training.
script_params Required	dict A dictionary of command-line arguments to pass to your training script specified in `entry_script`.
custom_docker_image Required	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.
image_registry_details Required	ContainerRegistry The details of the Docker image registry.
user_managed Required	bool Specifies whether Azure ML reuses an existing Python environment. False means that AzureML will create a Python environment based on the conda dependencies specification.
conda_packages Required	list A list of strings representing conda packages to be added to the Python environment for the experiment.
pip_packages Required	list A list of strings representing pip packages to be added to the Python environment for the experiment.
conda_dependencies_file_path Required	str A string representing the relative path to the conda dependencies yaml file. If specified, Azure ML will not install any framework related packages. This can be provided in combination with the `conda_packages` parameter. DEPRECATED. Use the `conda_dependencies_file` parameter.
pip_requirements_file_path Required	str A string representing the relative path to the pip requirements text file. This can be provided in combination with the `pip_packages` parameter. DEPRECATED. Use the `pip_requirements_file` parameter.
conda_dependencies_file Required	str A string representing the relative path to the conda dependencies yaml file. If specified, Azure ML will not install any framework related packages. This can be provided in combination with the `conda_packages` parameter.
pip_requirements_file Required	str A string representing the relative path to the pip requirements text file. This can be provided in combination with the `pip_packages` parameter.
environment_variables Required	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 Required	Environment The environment definition for an experiment includes PythonSection, DockerSection, and environment variables. Any environment option not directly exposed through other parameters to the Estimator construction can be set using `environment_definition` 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 invalid combinations.
inputs Required	list A list of DataReference or DatasetConsumptionConfig objects to use as input.
shm_size Required	str The size of the Docker container's shared memory block. If not set, the default azureml.core.environment._DEFAULT_SHM_SIZE is used.
resume_from Required	DataPath The data path containing the checkpoint or model files from which to resume the experiment.
max_run_duration_seconds Required	int The maximum allowed time for the run. Azure ML will attempt to automatically cancel the run if it takes longer than this value.
framework_version Required	str The Scikit-learn version to be used for executing training code. `SKLearn.get_supported_versions()` returns a list of the versions supported by the current SDK.
source_directory Required	str A local directory containing experiment configuration files.
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 Required	str The VM size of the compute target that will be created for the training. Supported values: Any Azure VM size.
vm_priority Required	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 param` is specified in the input.
entry_script Required	str A string representing the relative path to the file used to start training.
script_params Required	dict A dictionary of command-line arguments to pass to your training script specified in `entry_script`.
use_docker Required	bool A bool value indicating if the environment to run the experiment should be Docker-based.
custom_docker_image Required	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.
image_registry_details Required	ContainerRegistry The details of the Docker image registry.
user_managed Required	bool Specifies whether Azure ML reuses an existing Python environment. False means that AzureML will create a Python environment based on the conda dependencies specification.
conda_packages Required	list A list of strings representing conda packages to be added to the Python environment for the experiment.
pip_packages Required	list A list of strings representing pip packages to be added to the Python environment for the experiment.
conda_dependencies_file_path Required	str A string representing the relative path to the conda dependencies yaml file. If specified, Azure ML will not install any framework related packages. This can be provided in combination with the `conda_packages` parameter. DEPRECATED. Use the `conda_dependencies_file` parameter.
pip_requirements_file_path Required	str A string representing the relative path to the pip requirements text file. This can be provided in combination with the `pip_packages` parameter. DEPRECATED. Use the `pip_requirements_file` parameter.
conda_dependencies_file Required	str A string representing the relative path to the conda dependencies yaml file. If specified, Azure ML will not install any framework related packages. This can be provided in combination with the `conda_packages` parameter.
pip_requirements_file Required	str A string representing the relative path to the pip requirements text file. This can be provided in combination with the `pip_packages` parameter.
environment_variables Required	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 Required	Environment The environment definition for an experiment includes PythonSection, DockerSection, and environment variables. Any environment option not directly exposed through other parameters to the Estimator construction can be set using `environment_definition` 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 invalid combinations.
inputs Required	list A list of azureml.data.data_reference.DataReference objects to use as input.
shm_size Required	str The size of the Docker container's shared memory block. If not set, the default azureml.core.environment._DEFAULT_SHM_SIZE is used.
resume_from Required	DataPath The data path containing the checkpoint or model files from which to resume the experiment.
max_run_duration_seconds Required	int The maximum allowed time for the run. Azure ML will attempt to automatically cancel the run if it takes longer than this value.
framework_version Required	str The Scikit-learn version to be used for executing training code. `SKLearn.get_supported_versions()` returns a list of the versions supported by the current SDK.
_enable_optimized_mode Required	bool Enable incremental environment build with pre-built framework images for faster environment preparation. A pre-built framework image is built on top of Azure ML default CPU/GPU base images with framework dependencies pre-installed.
_disable_validation Required	bool Disable script validation before run submission. The default is True.
_show_lint_warnings Required	bool Show script linting warnings. The default is False.
_show_package_warnings Required	bool Show package validation warnings. The default is False.

Remarks

When submitting a training job, Azure ML runs your script in a conda environment within a Docker container. SKLearn containers have the following dependencies installed.

Dependencies | Scikit-learn 0.20.3 | ———————- | —————– | Python | 3.6.2 | azureml-defaults | Latest | IntelMpi | 2018.3.222 | scikit-learn | 0.20.3 | numpy | 1.16.2 | miniconda | 4.5.11 | scipy | 1.2.1 | joblib | 0.13.2 | git | 2.7.4 |

The Docker images extend Ubuntu 16.04.

If you need to install additional dependencies, you can either use the pip_packages or conda_packages parameters, or you can provide your pip_requirements_file or conda_dependencies_file file. Alternatively, you can build your own image and pass the custom_docker_image parameter to the estimator constructor.

Attributes

DEFAULT_VERSION

DEFAULT_VERSION = '0.20.3'

FRAMEWORK_NAME

FRAMEWORK_NAME = 'SKLearn'

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