HuggingFace: HuggingFace estimator class

In DyfanJones/sagemaker-r-mlframework: sagemaker machine learning developed by amazon

HuggingFace estimator class

Description

Handle training of custom HuggingFace code.

Super classes

sagemaker.mlcore::EstimatorBase -> sagemaker.mlcore::Framework -> HuggingFace

Public fields

.module

mimic python module

Methods

Public methods

• HuggingFace$new()

• HuggingFace$hyperparameters()

• HuggingFace$create_model()

• HuggingFace$clone()

Inherited methods

• sagemaker.mlcore::EstimatorBase$compile_model()

• sagemaker.mlcore::EstimatorBase$delete_endpoint()

• sagemaker.mlcore::EstimatorBase$deploy()

• sagemaker.mlcore::EstimatorBase$describe()

• sagemaker.mlcore::EstimatorBase$disable_profiling()

• sagemaker.mlcore::EstimatorBase$enable_default_profiling()

• sagemaker.mlcore::EstimatorBase$enable_network_isolation()

• sagemaker.mlcore::EstimatorBase$fit()

• sagemaker.mlcore::EstimatorBase$format()

• sagemaker.mlcore::EstimatorBase$get_vpc_config()

• sagemaker.mlcore::EstimatorBase$help()

• sagemaker.mlcore::EstimatorBase$latest_job_debugger_artifacts_path()

• sagemaker.mlcore::EstimatorBase$latest_job_profiler_artifacts_path()

• sagemaker.mlcore::EstimatorBase$latest_job_tensorboard_artifacts_path()

• sagemaker.mlcore::EstimatorBase$logs()

• sagemaker.mlcore::EstimatorBase$prepare_workflow_for_training()

• sagemaker.mlcore::EstimatorBase$register()

• sagemaker.mlcore::EstimatorBase$rule_job_summary()

• sagemaker.mlcore::EstimatorBase$update_profiler()

• sagemaker.mlcore::EstimatorBase$wait()

• sagemaker.mlcore::Framework$.prepare_for_training()

• sagemaker.mlcore::Framework$attach()

• sagemaker.mlcore::Framework$training_image_uri()

• sagemaker.mlcore::Framework$transformer()

---

Method new()

This “Estimator“ executes a HuggingFace script in a managed execution environment. The managed HuggingFace environment is an Amazon-built Docker container that executes functions defined in the supplied “entry_point“ Python script within a SageMaker Training Job. Training is started by calling :meth:'~sagemaker.amazon.estimator.Framework.fit' on this Estimator.

Usage

HuggingFace$new(
  py_version,
  entry_point,
  transformers_version = NULL,
  tensorflow_version = NULL,
  pytorch_version = NULL,
  source_dir = NULL,
  hyperparameters = NULL,
  image_uri = NULL,
  distribution = NULL,
  compiler_config = NULL,
  ...
)

Arguments

py_version

(str): Python version you want to use for executing your model training code. Defaults to “None“. Required unless “image_uri“ is provided. List of supported versions: https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators

entry_point

(str): Path (absolute or relative) to the Python source file which should be executed as the entry point to training. If “source_dir“ is specified, then “entry_point“ must point to a file located at the root of “source_dir“.

transformers_version

(str): Transformers version you want to use for executing your model training code. Defaults to “None“. Required unless “image_uri“ is provided. The current supported version is “4.6.1“.

tensorflow_version

(str): TensorFlow version you want to use for executing your model training code. Defaults to “None“. Required unless “pytorch_version“ is provided. List of supported versions: https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators.

pytorch_version

(str): PyTorch version you want to use for executing your model training code. Defaults to “None“. Required unless “tensorflow_version“ is provided. List of supported versions: https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators.

source_dir

(str): Path (absolute, relative or an S3 URI) to a directory with any other training source code dependencies aside from the entry point file (default: None). If “source_dir“ is an S3 URI, it must point to a tar.gz file. Structure within this directory are preserved when training on Amazon SageMaker.

hyperparameters

(dict): Hyperparameters that will be used for training (default: None). The hyperparameters are made accessible as a dict[str, str] to the training code on SageMaker. For convenience, this accepts other types for keys and values, but “str()“ will be called to convert them before training.

image_uri

(str): If specified, the estimator will use this image for training and hosting, instead of selecting the appropriate SageMaker official image based on framework_version and py_version. It can be an ECR url or dockerhub image and tag. Examples: * “123412341234.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0“ * “custom-image:latest“ If “framework_version“ or “py_version“ are “None“, then “image_uri“ is required. If also “None“, then a “ValueError“ will be raised.

distribution

(dict): A dictionary with information on how to run distributed training (default: None). Currently, the following are supported: distributed training with parameter servers, SageMaker Distributed (SMD) Data and Model Parallelism, and MPI. SMD Model Parallelism can only be used with MPI. To enable parameter server use the following setup: .. code:: python "parameter_server": "enabled": True To enable MPI: .. code:: python "mpi": "enabled": True To enable SMDistributed Data Parallel or Model Parallel: .. code:: python "smdistributed": "dataparallel": "enabled": True , "modelparallel": "enabled": True, "parameters":

compiler_config

(:class:'sagemaker.mlcore::TrainingCompilerConfig'): Configures SageMaker Training Compiler to accelerate training.

...

: Additional kwargs passed to the :class:'~sagemaker.estimator.Framework' constructor.

---

Method hyperparameters()

Return hyperparameters used by your custom PyTorch code during model training.

Usage

HuggingFace$hyperparameters()

---

Method create_model()

Create a model to deploy. The serializer, deserializer, content_type, and accept arguments are only used to define a default Predictor. They are ignored if an explicit predictor class is passed in. Other arguments are passed through to the Model class. Creating model with HuggingFace training job is not supported.

Usage

HuggingFace$create_model(
  model_server_workers = NULL,
  role = NULL,
  vpc_config_override = "VPC_CONFIG_DEFAULT",
  entry_point = NULL,
  source_dir = NULL,
  dependencies = NULL,
  ...
)

Arguments

model_server_workers

(int): Optional. The number of worker processes used by the inference server. If None, server will use one worker per vCPU.

role

(str): The “ExecutionRoleArn“ IAM Role ARN for the “Model“, which is also used during transform jobs. If not specified, the role from the Estimator will be used.

vpc_config_override

(dict[str, list[str]]): Optional override for VpcConfig set on the model. Default: use subnets and security groups from this Estimator. * 'Subnets' (list[str]): List of subnet ids. * 'SecurityGroupIds' (list[str]): List of security group ids.

entry_point

(str): Path (absolute or relative) to the local Python source file which should be executed as the entry point to training. If “source_dir“ is specified, then “entry_point“ must point to a file located at the root of “source_dir“. If 'git_config' is provided, 'entry_point' should be a relative location to the Python source file in the Git repo.

source_dir

(str): Path (absolute, relative or an S3 URI) to a directory with any other training source code dependencies aside from the entry point file (default: None). If “source_dir“ is an S3 URI, it must point to a tar.gz file. Structure within this directory are preserved when training on Amazon SageMaker. If 'git_config' is provided, 'source_dir' should be a relative location to a directory in the Git repo.

dependencies

(list[str]): A list of paths to directories (absolute or relative) with any additional libraries that will be exported to the container (default: []). The library folders will be copied to SageMaker in the same folder where the entrypoint is copied. If 'git_config' is provided, 'dependencies' should be a list of relative locations to directories with any additional libraries needed in the Git repo.

...

: Additional parameters passed to :class:'~sagemaker.model.Model' .. tip:: You can find additional parameters for using this method at :class:'~sagemaker.model.Model'.

Returns

(sagemaker.model.Model) a Model ready for deployment.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

HuggingFace$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

DyfanJones/sagemaker-r-mlframework documentation built on March 18, 2022, 7:41 a.m.