Chainer(entry_point, use_mpi=None, num_processes=None, process_slots_per_host=None, additional_mpi_options=None, source_dir=None, hyperparameters=None, py_version='py3', framework_version=None, image_name=None, **kwargs)¶
Handle end-to-end training and deployment of custom Chainer code.
Estimatorexecutes an Chainer script in a managed Chainer execution environment, within a SageMaker Training Job. The managed Chainer environment is an Amazon-built Docker container that executes functions defined in the supplied
Training is started by calling
fit()on this Estimator. After training is complete, calling
deploy()creates a hosted SageMaker endpoint and returns an
ChainerPredictorinstance that can be used to perform inference against the hosted model.
Technical documentation on preparing Chainer scripts for SageMaker training and using the Chainer Estimator is available on the project home-page: https://github.com/aws/sagemaker-python-sdk
- entry_point (str) – Path (absolute or relative) to the Python source file which should be executed as the entry point to training. This should be compatible with either Python 2.7 or Python 3.5.
- use_mpi (bool) – If true, entry point is run as an MPI script. By default, the Chainer Framework runs the entry point with ‘mpirun’ if more than one instance is used.
- num_processes (int) – Total number of processes to run the entry point with. By default, the Chainer Framework runs one process per GPU (on GPU instances), or one process per host (on CPU instances).
- process_slots_per_host (int) – The number of processes that can run on each instance. By default, this is set to the number of GPUs on the instance (on GPU instances), or one (on CPU instances).
- additional_mpi_options (str) – String of options to the ‘mpirun’ command used to run the entry point. For example, ‘-X NCCL_DEBUG=WARN’ will pass that option string to the mpirun command.
- source_dir (str) – Path (absolute or relative) to a directory with any other training source code dependencies aside from tne entry point file (default: None). 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.
- py_version (str) – Python version you want to use for executing your model training code (default: ‘py2’). One of ‘py2’ or ‘py3’.
- framework_version (str) – Chainer version you want to use for executing your model training code. List of supported versions https://github.com/aws/sagemaker-python-sdk#chainer-sagemaker-estimators. If not specified, this will default to 4.1.
- image_name (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. .. admonition:: Examples123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0 custom-image:latest.
- **kwargs – Additional kwargs passed to the
The latest version of Chainer included in the SageMaker pre-built Docker images.
Return hyperparameters used by your custom Chainer code during training.
create_model(model_server_workers=None, role=None, vpc_config_override='VPC_CONFIG_DEFAULT', entry_point=None, source_dir=None, dependencies=None)¶
Create a SageMaker
ChainerModelobject that can be deployed to an
- 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
ExecutionRoleArnIAM 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 not specified, the training entry point is used.
- source_dir (str) – Path (absolute or relative) to a directory with any other serving source code dependencies aside from the entry point file. If not specified, the model source directory from training is used.
- dependencies (list[str]) – A list of paths to directories (absolute or relative) with any additional libraries that will be exported to the container. If not specified, the dependencies from training are used.
ChainerModel()for full details.
ChainerModel(model_data, role, entry_point, image=None, py_version='py3', framework_version='4.1.0', predictor_cls=<class 'sagemaker.chainer.model.ChainerPredictor'>, model_server_workers=None, **kwargs)¶
An Chainer SageMaker
Modelthat can be deployed to a SageMaker
Initialize an ChainerModel.
- model_data (str) – The S3 location of a SageMaker model data
- role (str) – An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. After the endpoint is created, the inference code might use the IAM role, if it needs to access an AWS resource.
- entry_point (str) – Path (absolute or relative) to the Python source file which should be executed as the entry point to model hosting. This should be compatible with either Python 2.7 or Python 3.5.
- image (str) – A Docker image URI (default: None). If not specified, a default image for Chainer will be used.
- py_version (str) – Python version you want to use for executing your model training code (default: ‘py2’).
- framework_version (str) – Chainer version you want to use for executing your model training code.
- predictor_cls (callable[str, sagemaker.session.Session]) – A function
to call to create a predictor with an endpoint name and
Session. If specified,
deploy()returns the result of invoking this function on the created endpoint name.
- **kwargs – Keyword arguments passed to the
Return a container definition with framework configuration set in model environment variables.
A container definition object usable with the CreateModel API.
- model_data (str) – The S3 location of a SageMaker model data
A RealTimePredictor for inference against Chainer Endpoints.
This is able to serialize Python lists, dictionaries, and numpy arrays to multidimensional tensors for Chainer inference.
- endpoint_name (str) – The name of the endpoint to perform inference on.
- sagemaker_session (sagemaker.session.Session) – Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, the estimator creates one using the default AWS configuration chain.