TensorFlow Class

Reference

Represents an estimator for training in TensorFlow experiments.

DEPRECATED. Use the ScriptRunConfig object with your own defined environment or one of the Azure ML TensorFlow curated environments. For an introduction to configuring TensorFlow experiment runs with ScriptRunConfig, see Train TensorFlow models at scale with Azure Machine Learning.

Supported versions: 1.10, 1.12, 1.13, 2.0, 2.1, 2.2

Initialize a TensorFlow estimator.

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._framework_base_estimator._FrameworkBaseEstimator

TensorFlow

Constructor

TensorFlow(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, worker_count=1, parameter_server_count=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, 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 The relative path to the file containing the training script.
script_params Required	dict A dictionary of command-line arguments to pass to the training script specified in `entry_script`.
node_count Required	int The number of nodes in the compute target used for training. Only the AmlCompute target is supported for distributed training (`node_count` > 1).
process_count_per_node Required	int When using MPI, the number of processes per node.
worker_count Required	int When using Parameter Server for distributed training, the number of worker nodes. DEPRECATED. Specify as part of the `distributed_training` parameter.
parameter_server_count Required	int When using Parameter Server for distributed training, the number of parameter server nodes.
distributed_backend Required	str The communication backend for distributed training. DEPRECATED. Use the `distributed_training` parameter. Supported values: 'mpi' and 'ps'. 'mpi' represents MPI/Horovod and 'ps' represents Parameter Server. This parameter is required when any of `node_count`, `process_count_per_node`, `worker_count`, or `parameter_server_count` > 1. In case of 'ps', the sum of `worker_count` and `parameter_server_count` should be less than or equal to `node_count` * (number of CPUs or GPUs per node) 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 Required	ParameterServer or Mpi Parameters for running a distributed training job. For running a distributed job with Parameter Server backend, use the ParameterServer object to specify `worker_count` and `parameter_server_count`. The sum of the `worker_count` and the `parameter_server_count` parameters should be less than or equal to `node_count` * (the number of CPUs or GPUs per node). For running a distributed job with MPI backend, use the Mpi object to specify `process_count_per_node`.
use_gpu Required	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 Required	bool Specifies whether the environment in which to run the experiment should be Docker-based.
custom_docker_base_image Required	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 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. If false, Azure ML 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. 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.
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 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 these invalid combinations.
inputs Required	list A list of DataReference or DatasetConsumptionConfig objects to use as input.
source_directory_data_store Required	Datastore The backing datastore for project share.
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. For more information, see Docker run reference.
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 TensorFlow version to be used for executing training code. If no version is provided, the estimator will default to the latest version supported by Azure ML. Use `TensorFlow.get_supported_versions()` to return a list to get a list of all versions supported the current Azure ML 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 The relative path to the file containing the training script.
script_params Required	dict A dictionary of command-line arguments to pass to tne training script specified in `entry_script`.
node_count Required	int The number of nodes in the compute target used for training. Only the AmlCompute target is supported for distributed training (`node_count` > 1).
process_count_per_node Required	int When using MPI, the number of processes per node.
worker_count Required	int When using Parameter Server, the number of worker nodes. DEPRECATED. Specify as part of the `distributed_training` parameter.
parameter_server_count Required	int When using Parameter Server, the number of parameter server nodes.
distributed_backend Required	str The communication backend for distributed training. DEPRECATED. Use the `distributed_training` parameter. Supported values: 'mpi' and 'ps'. 'mpi' represents MPI/Horovod and 'ps' represents Parameter Server. This parameter is required when any of `node_count`, `process_count_per_node`, `worker_count`, or `parameter_server_count` > 1. In case of 'ps', the sum of `worker_count` and `parameter_server_count` should be less than or equal to `node_count` * (number of CPUs or GPUs per node) 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. is supported for distributed training.
distributed_training Required	ParameterServer or Mpi Parameters for running a distributed training job. For running a distributed job with the Parameter Server backend, use ParameterServer object to specify `worker_count` and `parameter_server_count`. The sum of the `worker_count` and the `parameter_server_count` parameters should be less than or equal to `node_count` * (the number of CPUs or GPUs per node). For running a distributed job with MPI backend, use Mpi object to specify `process_count_per_node`.
use_gpu Required	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 `custom_docker_image` parameter is not set. This setting is used only in Docker-enabled compute targets.
use_docker Required	bool Specifies whether the environment in which to run the experiment should be Docker-based.
custom_docker_base_image Required	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 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. If false, Azure ML 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 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` parameter.
pip_requirements_file_path Required	str 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.
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.
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.
pip_requirements_file Required	str The relative path to the pip requirements text file. This can be provided in combination with the `pip_packages` parameter.
environment_variables Required	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 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 these invalid combinations.
inputs Required	list A list of azureml.data.data_reference.DataReference objects to use as input.
source_directory_data_store Required	str The backing datastore for project share.
shm_size Required	The size of the Docker container's shared memory block. If not set, default is azureml.core.environment._DEFAULT_SHM_SIZE. For more information, see
framework_version Required	str The TensorFlow version to be used for executing training code. If no version is provided, the estimator will default to the latest version supported by Azure ML. Use TensorFlow.get_supported_versions() to return a list to get a list of all versions supported the current Azure ML 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. The TensorFlow containers have the following dependencies installed.

Dependencies | TensorFlow 1.10/1.12 | TensorFlow 1.13 | TF 2.0/2.1/2.2 | ———————————— | ——————– | ————— | —————— | Python | 3.6.2 | 3.6.2 | 3.6.2 | CUDA (GPU image only) | 9.0 | 10.0 | 10.0 | cuDNN (GPU image only) | 7.6.3 | 7.6.3 | 7.6.3 | NCCL (GPU image only) | 2.4.8 | 2.4.8 | 2.4.8 | azureml-defaults | Latest | Latest | Latest | azureml-dataset-runtime[fuse,pandas] | Latest | Latest | Latest | IntelMpi | 2018.3.222 | 2018.3.222 | —- | OpenMpi | —- | —- | 3.1.2 | horovod | 0.15.2 | 0.16.1 | 0.18.1/0.19.1/0.19.5 | miniconda | 4.5.11 | 4.5.11 | 4.5.11 | tensorflow | 1.10.0/1.12.0 | 1.13.1 | 2.0.0/2.1.0/2.2.0 | git | 2.7.4 | 2.7.4 | 2.7.4 |

The v1 Docker images extend Ubuntu 16.04. The v2 Docker images extend Ubuntu 18.04.

To install additional dependencies, you can either use the pip_packages or conda_packages parameter. Or, you can specify the pip_requirements_file or conda_dependencies_file parameter. Alternatively, you can build your own image, and pass the custom_docker_image parameter to the estimator constructor.

For more information about Docker containers used in TensorFlow training, see https://github.com/Azure/AzureML-Containers.

The TensorFlow class supports two methods of distributed training:

MPI-based distributed training using the

Horovod framework
Native distributed TensorFlow

For examples and more information about using TensorFlow in distributed training, see the tutorial Train and register TensorFlow models at scale with Azure Machine Learning.

Attributes

DEFAULT_VERSION

DEFAULT_VERSION = '1.13'

FRAMEWORK_NAME

FRAMEWORK_NAME = 'TensorFlow'

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