PyTorchProcessor: PyTorchProcessor class

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

PyTorchProcessor class

Description

Handles Amazon SageMaker processing tasks for jobs using PyTorch containers.

Super classes

sagemaker.common::Processor -> sagemaker.common::ScriptProcessor -> sagemaker.common::FrameworkProcessor -> PyTorchProcessor

Public fields

estimator_cls

Estimator object

Methods

Public methods

• PyTorchProcessor$new()

• PyTorchProcessor$clone()

Inherited methods

• sagemaker.common::Processor$format()

• sagemaker.common::FrameworkProcessor$get_run_args()

• sagemaker.common::FrameworkProcessor$run()

---

Method new()

This processor executes a Python script in a PyTorch execution environment. Unless “image_uri“ is specified, the PyTorch environment is an Amazon-built Docker container that executes functions defined in the supplied “code“ Python script.

Usage

PyTorchProcessor$new(
  framework_version,
  role,
  instance_count,
  instance_type,
  py_version = "py3",
  image_uri = NULL,
  command = NULL,
  volume_size_in_gb = 30,
  volume_kms_key = NULL,
  output_kms_key = NULL,
  code_location = NULL,
  max_runtime_in_seconds = NULL,
  base_job_name = NULL,
  sagemaker_session = NULL,
  env = NULL,
  tags = NULL,
  network_config = NULL
)

Arguments

framework_version

(str): The version of the framework. Value is ignored when “image_uri“ is provided.

role

(str): An AWS IAM role name or ARN. Amazon SageMaker Processing uses this role to access AWS resources, such as data stored in Amazon S3.

instance_count

(int): The number of instances to run a processing job with.

instance_type

(str): The type of EC2 instance to use for processing, for example, 'ml.c4.xlarge'.

py_version

(str): Python version you want to use for executing your model training code. One of 'py2' or 'py3'. Defaults to 'py3'. Value is ignored when “image_uri“ is provided.

image_uri

(str): The URI of the Docker image to use for the processing jobs (default: None).

command

([str]): The command to run, along with any command-line flags to *precede* the “'code script“'. Example: ["python3", "-v"]. If not provided, ["python"] will be chosen (default: None).

volume_size_in_gb

(int): Size in GB of the EBS volume to use for storing data during processing (default: 30).

volume_kms_key

(str): A KMS key for the processing volume (default: None).

output_kms_key

(str): The KMS key ID for processing job outputs (default: None).

code_location

(str): The S3 prefix URI where custom code will be uploaded (default: None). The code file uploaded to S3 is 'code_location/job-name/source/sourcedir.tar.gz'. If not specified, the default “code location“ is 's3://sagemaker-default-bucket'

max_runtime_in_seconds

(int): Timeout in seconds (default: None). After this amount of time, Amazon SageMaker terminates the job, regardless of its current status. If 'max_runtime_in_seconds' is not specified, the default value is 24 hours.

base_job_name

(str): Prefix for processing name. If not specified, the processor generates a default job name, based on the processing image name and current timestamp (default: None).

sagemaker_session

(:class:'~sagemaker.session.Session'): Session object which manages interactions with Amazon SageMaker and any other AWS services needed. If not specified, the processor creates one using the default AWS configuration chain (default: None).

env

(dict[str, str]): Environment variables to be passed to the processing jobs (default: None).

tags

(list[dict]): List of tags to be passed to the processing job (default: None). For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

network_config

(:class:'~sagemaker.network.NetworkConfig'): A :class:'~sagemaker.network.NetworkConfig' object that configures network isolation, encryption of inter-container traffic, security group IDs, and subnets (default: None).

---

Method clone()

The objects of this class are cloneable with this method.

Usage

PyTorchProcessor$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.