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 unless image_uri is provided.
framework_version (str) – TensorFlow 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#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 MPI - /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, set model_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 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 we support distributed training with parameter servers and MPI. To enable parameter server use the following setup:
```
{
    'parameter_server':
    {
        'enabled': True
    }
}
```
To enable MPI:
```
{
    'mpi':
    {
        'enabled': True
    }
}
```
**kwargs – Additional kwargs passed to the Framework constructor.

Tip

You can find additional parameters for initializing this class at Framework and EstimatorBase.

create_model(role=None, vpc_config_override='VPC_CONFIG_DEFAULT', entry_point=None, source_dir=None, dependencies=None, **kwargs)¶

Create a TensorFlowModel object that can be used for creating SageMaker model entities, deploying 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, then entry_point must point to a file located at the root of source_dir. If not specified and endpoint_type is ‘tensorflow-serving’, no entry point is used. If endpoint_type is also None, 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.

Return type

sagemaker.tensorflow.model.TensorFlowModel

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, then entry_point must point to a file located at the root of source_dir. If not specified and endpoint_type is ‘tensorflow-serving’, no entry point is used. If endpoint_type is also None, 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, then entry_point must point to a file located at the root of source_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 is None, then image_uri is required. If also None, then a ValueError will be raised.
framework_version (str) – Optional. TensorFlow Serving version you want to use. Defaults to None. Required unless image_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 Model initializer.

Tip

You can find additional parameters for initializing this class at FrameworkModel and Model.

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, 0]¶

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)¶

Deploy this Model to an Endpoint and optionally return a Predictor.

Create a SageMaker Model and EndpointConfig, and deploy an Endpoint from this Model. If self.predictor_cls is not None, this method returns a the result of invoking self.predictor_cls on the created endpoint name.

The name of the created model is accessible in the name field of this Model after deploy returns

The name of the created endpoint is accessible in the endpoint_name field of this Model after deploy returns.

Parameters

initial_instance_count (int) – The initial number of instances to run in the Endpoint created from this Model.
instance_type (str) – The EC2 instance type to deploy this Model to. For example, ‘ml.p2.xlarge’, or ‘local’ for local mode.
serializer (BaseSerializer) – A serializer object, used to encode data for an inference endpoint (default: None). If serializer is not None, then serializer will override the default serializer. The default serializer is set by the predictor_cls.
deserializer (BaseDeserializer) – A deserializer object, used to decode data from an inference endpoint (default: None). If deserializer is not None, then deserializer will override the default deserializer. The default deserializer is set by the predictor_cls.
accelerator_type (str) – Type of Elastic Inference accelerator to deploy this model for model loading and inference, for example, ‘ml.eia1.medium’. If not specified, no Elastic Inference accelerator will be attached to the endpoint. For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html
endpoint_name (str) – The name of the endpoint to create (default: None). If not specified, a unique endpoint name will be created.
tags (List[dict[str, str]]) – The list of tags to attach to this specific endpoint.
kms_key (str) – The ARN of the KMS key that is used to encrypt the data on the storage volume attached to the instance hosting the endpoint.
wait (bool) – Whether the call should wait until the deployment of this model completes (default: True).
data_capture_config (sagemaker.model_monitor.DataCaptureConfig) – Specifies configuration related to Endpoint data capture for use with Amazon SageMaker Model Monitoring. Default: None.

Returns

Invocation of: self.predictor_cls on the created endpoint name, if self.predictor_cls is not None. Otherwise, return None.

Return type

callable[string, sagemaker.session.Session] or None

prepare_container_def(instance_type=None, accelerator_type=None)¶

Parameters

instance_type –
accelerator_type –

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

str

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)¶

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)¶

Parameters: data –

regress(data)¶

Parameters: data –

predict(data, initial_args=None)¶

Parameters

data –
initial_args –