TensorFlow¶
TensorFlow Estimator¶
-
class
sagemaker.tensorflow.estimator.
TensorFlow
(py_version=None, framework_version=None, model_dir=None, image_uri=None, distribution=None, **kwargs)¶ Bases:
sagemaker.estimator.Framework
Handle end-to-end training and deployment of user-provided TensorFlow code.
Initialize a
TensorFlow
estimator.- Parameters
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) – TensorFlow 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#tensorflow-sagemaker-estimators.model_dir (str) –
S3 location where the checkpoint data and models can be exported to during training (default: None). It will be passed in the training script as one of the command line arguments. If not specified, one is provided based on your training configuration:
distributed training with SMDistributed or MPI with Horovod -
/opt/ml/model
single-machine training or distributed training without MPI -
s3://{output_path}/model
Local Mode with local sources (file:// instead of s3://) -
/opt/ml/shared/model
To disable having
model_dir
passed to your training script, setmodel_dir=False
.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
123.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.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:
{ "parameter_server": { "enabled": True } }
To enable MPI:
{ "mpi": { "enabled": True } }
To enable SMDistributed Data Parallel or Model Parallel:
{ "smdistributed": { "dataparallel": { "enabled": True }, "modelparallel": { "enabled": True, "parameters": {} } } }
**kwargs – Additional kwargs passed to the Framework constructor.
Tip
You can find additional parameters for initializing this class at
Framework
andEstimatorBase
.-
create_model
(role=None, vpc_config_override='VPC_CONFIG_DEFAULT', entry_point=None, source_dir=None, dependencies=None, **kwargs)¶ Creates
TensorFlowModel
object to be used for creating SageMaker model entities.This can be done by deploying it to a SageMaker endpoint, or starting SageMaker Batch Transform jobs.
- Parameters
role (str) – The
TensorFlowModel
, which is also used during transform jobs. If not specified, the role from the Estimator is 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 andendpoint_type
is ‘tensorflow-serving’, no entry point is used. Ifendpoint_type
is alsoNone
, then the training entry point is used.source_dir (str) – Path (absolute or relative or an S3 URI) to a directory with any other serving source code dependencies aside from the entry point file (default: None).
dependencies (list[str]) – A list of paths to directories (absolute or relative) with any additional libraries that will be exported to the container (default: None).
**kwargs – Additional kwargs passed to
TensorFlowModel
.
- Returns
- A
TensorFlowModel
object. See
TensorFlowModel
for full details.
- A
- Return type
-
hyperparameters
()¶ Return hyperparameters used by your custom TensorFlow code during model training.
-
transformer
(instance_count, instance_type, strategy=None, assemble_with=None, output_path=None, output_kms_key=None, accept=None, env=None, max_concurrent_transforms=None, max_payload=None, tags=None, role=None, volume_kms_key=None, entry_point=None, vpc_config_override='VPC_CONFIG_DEFAULT', enable_network_isolation=None, model_name=None)¶ Return a
Transformer
that uses a SageMaker Model based on the training job.It reuses the SageMaker Session and base job name used by the Estimator.
- Parameters
instance_count (int) – Number of EC2 instances to use.
instance_type (str) – Type of EC2 instance to use, for example, ‘ml.c4.xlarge’.
strategy (str) – The strategy used to decide how to batch records in a single request (default: None). Valid values: ‘MultiRecord’ and ‘SingleRecord’.
assemble_with (str) – How the output is assembled (default: None). Valid values: ‘Line’ or ‘None’.
output_path (str) – S3 location for saving the transform result. If not specified, results are stored to a default bucket.
output_kms_key (str) – Optional. KMS key ID for encrypting the transform output (default: None).
accept (str) – The accept header passed by the client to the inference endpoint. If it is supported by the endpoint, it will be the format of the batch transform output.
env (dict) – Environment variables to be set for use during the transform job (default: None).
max_concurrent_transforms (int) – The maximum number of HTTP requests to be made to each individual transform container at one time.
max_payload (int) – Maximum size of the payload in a single HTTP request to the container in MB.
tags (list[dict]) – List of tags for labeling a transform job. If none specified, then the tags used for the training job are used for the transform job.
role (str) – The IAM Role ARN for the
TensorFlowModel
, which is also used during transform jobs. If not specified, the role from the Estimator is used.volume_kms_key (str) – Optional. KMS key ID for encrypting the volume attached to the ML compute instance (default: None).
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 andendpoint_type
is ‘tensorflow-serving’, no entry point is used. Ifendpoint_type
is alsoNone
, then the training entry point is used.vpc_config_override (dict[str, list[str]]) –
Optional override for the 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.
enable_network_isolation (bool) – Specifies whether container will run in network isolation mode. Network isolation mode restricts the container access to outside networks (such as the internet). The container does not make any inbound or outbound network calls. If True, a channel named “code” will be created for any user entry script for inference. Also known as Internet-free mode. If not specified, this setting is taken from the estimator’s current configuration.
model_name (str) – Name to use for creating an Amazon SageMaker model. If not specified, the estimator generates a default job name based on the training image name and current timestamp.
TensorFlow Serving Model¶
-
class
sagemaker.tensorflow.model.
TensorFlowModel
(model_data, role, entry_point=None, image_uri=None, framework_version=None, container_log_level=None, predictor_cls=<class 'sagemaker.tensorflow.model.TensorFlowPredictor'>, **kwargs)¶ Bases:
sagemaker.model.FrameworkModel
A
FrameworkModel
implementation for inference with TensorFlow Serving.Initialize a Model.
- Parameters
model_data (str) – 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) – A Docker image URI (default: None). If not specified, a default image for TensorFlow Serving will be used. If
framework_version
isNone
, thenimage_uri
is required. If alsoNone
, then aValueError
will be raised.framework_version (str) – Optional. TensorFlow Serving version you want to use. Defaults to
None
. Required unlessimage_uri
is provided.container_log_level (int) – Log level to use within the container (default: logging.ERROR). Valid values are defined in the Python logging module.
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.**kwargs – Keyword arguments passed to the superclass
FrameworkModel
and, subsequently, its superclassModel
.
Tip
You can find additional parameters for initializing this class at
FrameworkModel
andModel
.-
LOG_LEVEL_PARAM_NAME
= 'SAGEMAKER_TFS_NGINX_LOGLEVEL'¶
-
LOG_LEVEL_MAP
= {10: 'debug', 20: 'info', 30: 'warn', 40: 'error', 50: 'crit'}¶
-
LATEST_EIA_VERSION
= [2, 3]¶
-
register
(content_types, response_types, inference_instances, transform_instances, 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)¶ Creates a model package for creating SageMaker models or listing on Marketplace.
- Parameters
content_types (list) – The supported MIME types for the input data.
response_types (list) – The supported MIME types for the output data.
inference_instances (list) – A list of the instance types that are used to generate inferences in real-time.
transform_instances (list) – 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) – 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) – Model Package Group name, exclusive to model_package_name, using model_package_group_name makes the Model Package versioned (default: None).
image_uri (str) – 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 object (default: None).
marketplace_cert (bool) – A boolean value indicating if the Model Package is certified for AWS Marketplace (default: False).
approval_status (str) – Model Approval Status, values can be “Approved”, “Rejected”, or “PendingManualApproval” (default: “PendingManualApproval”).
description (str) – Model Package description (default: None).
- Returns
A sagemaker.model.ModelPackage instance.
-
deploy
(initial_instance_count, instance_type, serializer=None, deserializer=None, accelerator_type=None, endpoint_name=None, tags=None, kms_key=None, wait=True, data_capture_config=None, update_endpoint=None)¶ Deploy a Tensorflow
Model
to a SageMakerEndpoint
.
-
prepare_container_def
(instance_type=None, accelerator_type=None)¶ Prepare the container definition.
- Parameters
instance_type – Instance type of the container.
accelerator_type – Accelerator type, if applicable.
- Returns
A container definition for deploying a
Model
to anEndpoint
.
-
serving_image_uri
(region_name, instance_type, accelerator_type=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).
accelerator_type (str) – The Elastic Inference accelerator type to deploy to the instance for loading and making inferences to the model (default: None). For example, ‘ml.eia1.medium’.
- Returns
The appropriate image URI based on the given parameters.
- Return type
TensorFlow Serving Predictor¶
-
class
sagemaker.tensorflow.model.
TensorFlowPredictor
(endpoint_name, sagemaker_session=None, serializer=<sagemaker.serializers.JSONSerializer object>, deserializer=<sagemaker.deserializers.JSONDeserializer object>, model_name=None, model_version=None, **kwargs)¶ Bases:
sagemaker.predictor.Predictor
A
Predictor
implementation for inference against TensorFlow Serving endpoints.Initialize a
TensorFlowPredictor
.See
Predictor
for more info about parameters.- 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 (callable) – Optional. Default serializes input data to json. Handles dicts, lists, and numpy arrays.
deserializer (callable) – Optional. Default parses the response using
json.load(...)
.model_name (str) – Optional. The name of the SavedModel model that should handle the request. If not specified, the endpoint’s default model will handle the request.
model_version (str) – Optional. The version of the SavedModel model that should handle the request. If not specified, the latest version of the model will be used.
-
classify
(data)¶ Placeholder docstring.
-
regress
(data)¶ Placeholder docstring.
-
predict
(data, initial_args=None)¶ Placeholder docstring.