Chainer¶
Chainer Estimator¶
-
class
sagemaker.chainer.estimator.
Chainer
(entry_point, use_mpi=None, num_processes=None, process_slots_per_host=None, additional_mpi_options=None, source_dir=None, hyperparameters=None, framework_version=None, py_version=None, image_uri=None, **kwargs)¶ Bases:
sagemaker.estimator.Framework
Handle end-to-end training and deployment of custom Chainer code.
This
Estimator
executes an Chainer script in a managed execution environment.The managed Chainer 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
fit()
on this Estimator. After training is complete, callingdeploy()
creates a hosted SageMaker endpoint and returns anChainerPredictor
instance 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
- Parameters
entry_point (str or PipelineVariable) – Path (absolute or relative) to the Python source file which should be executed as the entry point to training. If
source_dir
is specified, thenentry_point
must point to a file located at the root ofsource_dir
.use_mpi (bool or PipelineVariable) – 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 or PipelineVariable) – 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 or PipelineVariable) – 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 or PipelineVariable) – 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 or PipelineVariable) – Path (absolute or relative) to a directory with any other training source code dependencies aside from the entry point file (default: None). Structure within this directory are preserved when training on Amazon SageMaker.
hyperparameters (dict[str, str] or dict[str, PipelineVariable]) – 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. Defaults to
None
. Required unlessimage_uri
is provided.framework_version (str) – Chainer version you want to use for executing your model training code. Defaults to
None
. Required unlessimage_uri
is provided. List of supported versions: https://github.com/aws/sagemaker-python-sdk#chainer-sagemaker-estimators.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
orpy_version
areNone
, thenimage_uri
is required. If alsoNone
, then aValueError
will be raised.**kwargs – Additional kwargs passed to the
Framework
constructor.
Tip
You can find additional parameters for initializing this class at
Framework
andEstimatorBase
.-
hyperparameters
()¶ 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, **kwargs)¶ Create a SageMaker
ChainerModel
object that can be deployed to anEndpoint
.- Parameters
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 theModel
, 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, thenentry_point
must point to a file located at the root ofsource_dir
. 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. This is not supported with “local code” in Local Mode.
**kwargs – Additional kwargs passed to the ChainerModel constructor.
- Returns
A SageMaker
ChainerModel
object. SeeChainerModel()
for full details.- Return type
Chainer Model¶
-
class
sagemaker.chainer.model.
ChainerModel
(model_data, role=None, entry_point=None, image_uri=None, framework_version=None, py_version=None, predictor_cls=<class 'sagemaker.chainer.model.ChainerPredictor'>, model_server_workers=None, **kwargs)¶ Bases:
sagemaker.model.FrameworkModel
An Chainer SageMaker
Model
that can be deployed to a SageMakerEndpoint
.Initialize an ChainerModel.
- Parameters
model_data (str or PipelineVariable) – The S3 location of a SageMaker model data
.tar.gz
file.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. If
source_dir
is specified, thenentry_point
must point to a file located at the root ofsource_dir
.image_uri (str or PipelineVariable) – A Docker image URI (default: None). If not specified, a default image for Chainer will be used. If
framework_version
orpy_version
areNone
, thenimage_uri
is required. Ifimage_uri
is alsoNone
, then aValueError
will be raised.framework_version (str) – Chainer version you want to use for executing your model training code. Defaults to
None
. Required unlessimage_uri
is provided.py_version (str) – Python version you want to use for executing your model training code. Defaults to
None
. Required unlessimage_uri
is provided.predictor_cls (callable[str, sagemaker.session.Session]) – A function to call to create a predictor with an endpoint name and SageMaker
Session
. If specified,deploy()
returns the result of invoking this function on the created endpoint name.model_server_workers (int or PipelineVariable) – Optional. The number of worker processes used by the inference server. If None, server will use one worker per vCPU.
**kwargs – Keyword arguments passed to the
FrameworkModel
initializer.
Tip
You can find additional parameters for initializing this class at
FrameworkModel
andModel
.-
register
(content_types, response_types, inference_instances=None, transform_instances=None, model_package_name=None, model_package_group_name=None, image_uri=None, model_metrics=None, metadata_properties=None, marketplace_cert=False, approval_status=None, description=None, drift_check_baselines=None, customer_metadata_properties=None, domain=None, sample_payload_url=None, task=None, framework=None, framework_version=None, nearest_model_name=None, data_input_configuration=None, skip_model_validation=None)¶ Creates a model package for creating SageMaker models or listing on Marketplace.
- Parameters
content_types (list[str] or list[PipelineVariable]) – The supported MIME types for the input data.
response_types (list[str] or list[PipelineVariable]) – The supported MIME types for the output data.
inference_instances (list[str] or list[PipelineVariable]) – A list of the instance types that are used to generate inferences in real-time.
transform_instances (list[str] or list[PipelineVariable]) – A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
model_package_name (str or PipelineVariable) – Model Package name, exclusive to model_package_group_name, using model_package_name makes the Model Package un-versioned (default: None).
model_package_group_name (str or PipelineVariable) – Model Package Group name, exclusive to model_package_name, using model_package_group_name makes the Model Package versioned (default: None).
image_uri (str or PipelineVariable) – Inference image uri for the container. Model class’ self.image will be used if it is None (default: None).
model_metrics (ModelMetrics) – ModelMetrics object (default: None).
metadata_properties (MetadataProperties) – MetadataProperties (default: None).
marketplace_cert (bool) – A boolean value indicating if the Model Package is certified for AWS Marketplace (default: False).
approval_status (str or PipelineVariable) – Model Approval Status, values can be “Approved”, “Rejected”, or “PendingManualApproval” (default: “PendingManualApproval”).
description (str) – Model Package description (default: None).
drift_check_baselines (DriftCheckBaselines) – DriftCheckBaselines object (default: None).
customer_metadata_properties (dict[str, str] or dict[str, PipelineVariable]) – A dictionary of key-value paired metadata properties (default: None).
domain (str or PipelineVariable) – Domain values can be “COMPUTER_VISION”, “NATURAL_LANGUAGE_PROCESSING”, “MACHINE_LEARNING” (default: None).
sample_payload_url (str or PipelineVariable) – The S3 path where the sample payload is stored (default: None).
task (str or PipelineVariable) – Task values which are supported by Inference Recommender are “FILL_MASK”, “IMAGE_CLASSIFICATION”, “OBJECT_DETECTION”, “TEXT_GENERATION”, “IMAGE_SEGMENTATION”, “CLASSIFICATION”, “REGRESSION”, “OTHER” (default: None).
framework (str or PipelineVariable) – Machine learning framework of the model package container image (default: None).
framework_version (str or PipelineVariable) – Framework version of the Model Package Container Image (default: None).
nearest_model_name (str or PipelineVariable) – Name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender (default: None).
data_input_configuration (str or PipelineVariable) – Input object for the model (default: None).
skip_model_validation (str or PipelineVariable) – Indicates if you want to skip model validation. Values can be “All” or “None” (default: None).
- Returns
A string of SageMaker Model Package ARN.
- Return type
-
prepare_container_def
(instance_type=None, accelerator_type=None, serverless_inference_config=None)¶ Return a container definition with framework configuration set in model environment.
- Parameters
instance_type (str) – The EC2 instance type to deploy this Model to. For example, ‘ml.p2.xlarge’.
accelerator_type (str) – The Elastic Inference accelerator type to deploy to the instance for loading and making inferences to the model. For example, ‘ml.eia1.medium’.
serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig) – Specifies configuration related to serverless endpoint. Instance type is not provided in serverless inference. So this is used to find image URIs.
- Returns
A container definition object usable with the CreateModel API.
- Return type
-
serving_image_uri
(region_name, instance_type, accelerator_type=None, serverless_inference_config=None)¶ Create a URI for the serving image.
- Parameters
region_name (str) – AWS region where the image is uploaded.
instance_type (str) – SageMaker instance type. Used to determine device type (cpu/gpu/family-specific optimized).
serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig) – Specifies configuration related to serverless endpoint. Instance type is not provided in serverless inference. So this is used to determine device type.
- Returns
The appropriate image URI based on the given parameters.
- Return type
Chainer Predictor¶
-
class
sagemaker.chainer.model.
ChainerPredictor
(endpoint_name, sagemaker_session=None, serializer=<sagemaker.base_serializers.NumpySerializer object>, deserializer=<sagemaker.base_deserializers.NumpyDeserializer object>)¶ Bases:
sagemaker.base_predictor.Predictor
A Predictor for inference against Chainer Endpoints.
This is able to serialize Python lists, dictionaries, and numpy arrays to multidimensional tensors for Chainer inference.
Initialize an
ChainerPredictor
.- Parameters
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.
serializer (sagemaker.serializers.BaseSerializer) – Optional. Default serializes input data to .npy format. Handles lists and numpy arrays.
deserializer (sagemaker.deserializers.BaseDeserializer) – Optional. Default parses the response from .npy format to numpy array.