Using TensorFlow with the SageMaker Python SDK

TensorFlow SageMaker Estimators allow you to run your own TensorFlow training algorithms on SageMaker Learner, and to host your own TensorFlow models on SageMaker Hosting.

For general information about using the SageMaker Python SDK, see Using the SageMaker Python SDK.

Warning

We have added a new format of your TensorFlow training script with TensorFlow version 1.11. This new way gives the user script more flexibility. This new format is called Script Mode, as opposed to Legacy Mode, which is what we support with TensorFlow 1.11 and older versions. In addition we are adding Python 3 support with Script Mode. The last supported version of Legacy Mode will be TensorFlow 1.12. Script Mode is available with TensorFlow version 1.11 and newer. Make sure you refer to the correct version of this README when you prepare your script. You can find the Legacy Mode README here.

Supported versions of TensorFlow for Elastic Inference: 1.11.0, 1.12.0.

Train a Model with TensorFlow

To train a TensorFlow model by using the SageMaker Python SDK:

  1. Prepare a training script
  2. Create a sagemaker.tensorflow.TensorFlow estimator
  3. Call the estimator’s fit method

Prepare a Script Mode Training Script

Your TensorFlow training script must be a Python 2.7- or 3.6-compatible source file.

The training script is very similar to a training script you might run outside of SageMaker, but you can access useful properties about the training environment through various environment variables, including the following:

  • SM_MODEL_DIR: A string that represents the local path where the training job writes the model artifacts to. After training, artifacts in this directory are uploaded to S3 for model hosting. This is different than the model_dir argument passed in your training script, which is an S3 location. SM_MODEL_DIR is always set to /opt/ml/model.
  • SM_NUM_GPUS: An integer representing the number of GPUs available to the host.
  • SM_OUTPUT_DATA_DIR: A string that represents the path to the directory to write output artifacts to. Output artifacts might include checkpoints, graphs, and other files to save, but do not include model artifacts. These artifacts are compressed and uploaded to S3 to an S3 bucket with the same prefix as the model artifacts.
  • SM_CHANNEL_XXXX: A string that represents the path to the directory that contains the input data for the specified channel. For example, if you specify two input channels in the TensorFlow estimator’s fit call, named ‘train’ and ‘test’, the environment variables SM_CHANNEL_TRAIN and SM_CHANNEL_TEST are set.

For the exhaustive list of available environment variables, see the SageMaker Containers documentation.

A typical training script loads data from the input channels, configures training with hyperparameters, trains a model, and saves a model to SM_CHANNEL_TRAIN so that it can be deployed for inference later. Hyperparameters are passed to your script as arguments and can be retrieved with an argparse.ArgumentParser instance. For example, a training script might start with the following:

import argparse
import os

if __name__ =='__main__':

    parser = argparse.ArgumentParser()

    # hyperparameters sent by the client are passed as command-line arguments to the script.
    parser.add_argument('--epochs', type=int, default=10)
    parser.add_argument('--batch_size', type=int, default=100)
    parser.add_argument('--learning_rate', type=float, default=0.1)

    # input data and model directories
    parser.add_argument('--model_dir', type=str)
    parser.add_argument('--train', type=str, default=os.environ.get('SM_CHANNEL_TRAIN'))
    parser.add_argument('--test', type=str, default=os.environ.get('SM_CHANNEL_TEST'))

    args, _ = parser.parse_known_args()

    # ... load from args.train and args.test, train a model, write model to args.model_dir.

Because the SageMaker imports your training script, putting your training launching code in a main guard (if __name__=='__main__':) is good practice.

Note that SageMaker doesn’t support argparse actions. For example, if you want to use a boolean hyperparameter, specify type as bool in your script and provide an explicit True or False value for this hyperparameter when you create the TensorFlow estimator.

For a complete example of a TensorFlow training script, see mnist.py.

Adapting your local TensorFlow script

If you have a TensorFlow training script that runs outside of SageMaker, do the following to adapt the script to run in SageMaker:

1. Make sure your script can handle --model_dir as an additional command line argument. If you did not specify a location when you created the TensorFlow estimator, an S3 location under the default training job bucket is used. Distributed training with parameter servers requires you to use the tf.estimator.train_and_evaluate API and to provide an S3 location as the model directory during training. Here is an example:

estimator = tf.estimator.Estimator(model_fn=my_model_fn, model_dir=args.model_dir)
...
train_spec = tf.estimator.TrainSpec(train_input_fn, max_steps=1000)
eval_spec = tf.estimator.EvalSpec(eval_input_fn)
tf.estimator.train_and_evaluate(mnist_classifier, train_spec, eval_spec)
  1. Load input data from the input channels. The input channels are defined when fit is called. For example:
estimator.fit({'train':'s3://my-bucket/my-training-data',
              'eval':'s3://my-bucket/my-evaluation-data'})

In your training script the channels will be stored in environment variables SM_CHANNEL_TRAIN and SM_CHANNEL_EVAL. You can add them to your argument parsing logic like this:

parser = argparse.ArgumentParser()
parser.add_argument('--train', type=str, default=os.environ.get('SM_CHANNEL_TRAIN'))
parser.add_argument('--eval', type=str, default=os.environ.get('SM_CHANNEL_EVAL'))
  1. Export your final model to path stored in environment variable SM_MODEL_DIR which should always be /opt/ml/model. At end of training SageMaker will upload the model file under /opt/ml/model to output_path.

Create an Estimator

After you create your training script, create an instance of the sagemaker.tensorflow.TensorFlow estimator.

To use Script Mode, set at least one of these args

  • py_version='py3'
  • script_mode=True

When using Script Mode, your training script needs to accept the following args:

  • model_dir

The following args are not permitted when using Script Mode:

  • checkpoint_path
  • training_steps
  • evaluation_steps
  • requirements_file
from sagemaker.tensorflow import TensorFlow

tf_estimator = TensorFlow(entry_point='tf-train.py', role='SageMakerRole',
                          train_instance_count=1, train_instance_type='ml.p2.xlarge',
                          framework_version='1.12', py_version='py3')
tf_estimator.fit('s3://bucket/path/to/training/data')

Where the S3 url is a path to your training data within Amazon S3. The constructor keyword arguments define how SageMaker runs your training script.

For more information about the sagemaker.tensorflow.TensorFlow estimator, see sagemaker.tensorflow.TensorFlow Class.

Call the fit Method

You start your training script by calling the fit method on a TensorFlow estimator. fit takes both required and optional arguments.

Required arguments

  • inputs: The S3 location(s) of datasets to be used for training. This can take one of two forms:
    • str: An S3 URI, for example s3://my-bucket/my-training-data, which indicates the dataset’s location.
    • dict[str, str]: A dictionary mapping channel names to S3 locations, for example {'train': 's3://my-bucket/my-training-data/train', 'test': 's3://my-bucket/my-training-data/test'}
    • sagemaker.session.s3_input: channel configuration for S3 data sources that can provide additional information as well as the path to the training dataset. See the API docs for full details.

Optional arguments

  • wait (bool): Defaults to True, whether to block and wait for the training script to complete before returning. If set to False, it will return immediately, and can later be attached to.
  • logs (bool): Defaults to True, whether to show logs produced by training job in the Python session. Only meaningful when wait is True.
  • run_tensorboard_locally (bool): Defaults to False. If set to True a Tensorboard command will be printed out.
  • job_name (str): Training job name. If not specified, the estimator generates a default job name, based on the training image name and current timestamp.

What happens when fit is called

Calling fit starts a SageMaker training job. The training job will execute the following.

  • Starts train_instance_count EC2 instances of the type train_instance_type.
  • On each instance, it will do the following steps:
    • starts a Docker container optimized for TensorFlow.
    • downloads the dataset.
    • setup up training related environment varialbes
    • setup up distributed training environment if configured to use parameter server
    • starts asynchronous training

If the wait=False flag is passed to fit, then it returns immediately. The training job continues running asynchronously. Later, a Tensorflow estimator can be obtained by attaching to the existing training job. If the training job is not finished, it starts showing the standard output of training and wait until it completes. After attaching, the estimator can be deployed as usual.

tf_estimator.fit(your_input_data, wait=False)
training_job_name = tf_estimator.latest_training_job.name

# after some time, or in a separate Python notebook, we can attach to it again.

tf_estimator = TensorFlow.attach(training_job_name=training_job_name)

Distributed Training

To run your training job with multiple instances in a distributed fashion, set train_instance_count to a number larger than 1. We support two different types of distributed training, parameter server and Horovod. The distributions parameter is used to configure which distributed training strategy to use.

Training with parameter servers

If you specify parameter_server as the value of the distributions parameter, the container launches a parameter server thread on each instance in the training cluster, and then executes your training code. You can find more information on TensorFlow distributed training at TensorFlow docs. To enable parameter server training:

from sagemaker.tensorflow import TensorFlow

tf_estimator = TensorFlow(entry_point='tf-train.py', role='SageMakerRole',
                          train_instance_count=2, train_instance_type='ml.p2.xlarge',
                          framework_version='1.11', py_version='py3',
                          distributions={'parameter_server': {'enabled': True}})
tf_estimator.fit('s3://bucket/path/to/training/data')

Training with Horovod

Horovod is a distributed training framework based on MPI. Horovod is only available with TensorFlow version 1.12 or newer. You can find more details at Horovod README.

The container sets up the MPI environment and executes the mpirun command enabling you to run any Horovod training script with Script Mode.

Training with MPI is configured by specifying following fields in distributions:

  • enabled (bool): If set to True, the MPI setup is performed and mpirun command is executed.
  • processes_per_host (int): Number of processes MPI should launch on each host. Note, this should not be greater than the available slots on the selected instance type. This flag should be set for the multi-cpu/gpu training.
  • custom_mpi_options (str): Any mpirun flag(s) can be passed in this field that will be added to the mpirun command executed by SageMaker to launch distributed horovod training.

In the below example we create an estimator to launch Horovod distributed training with 2 processes on one host:

from sagemaker.tensorflow import TensorFlow

tf_estimator = TensorFlow(entry_point='tf-train.py', role='SageMakerRole',
                          train_instance_count=1, train_instance_type='ml.p2.xlarge',
                          framework_version='1.12', py_version='py3',
                          distributions={
                              'mpi': {
                                  'enabled': True,
                                  'processes_per_host': 2,
                                  'custom_mpi_options': '--NCCL_DEBUG INFO'
                              }
                          })
tf_estimator.fit('s3://bucket/path/to/training/data')

Training with Pipe Mode using PipeModeDataset

Amazon SageMaker allows users to create training jobs using Pipe input mode. With Pipe input mode, your dataset is streamed directly to your training instances instead of being downloaded first. This means that your training jobs start sooner, finish quicker, and need less disk space.

SageMaker TensorFlow provides an implementation of tf.data.Dataset that makes it easy to take advantage of Pipe input mode in SageMaker. You can replace your tf.data.Dataset with a sagemaker_tensorflow.PipeModeDataset to read TFRecords as they are streamed to your training instances.

In your entry_point script, you can use PipeModeDataset like a Dataset. In this example, we create a PipeModeDataset to read TFRecords from the ‘training’ channel:

from sagemaker_tensorflow import PipeModeDataset

features = {
    'data': tf.FixedLenFeature([], tf.string),
    'labels': tf.FixedLenFeature([], tf.int64),
}

def parse(record):
    parsed = tf.parse_single_example(record, features)
    return ({
        'data': tf.decode_raw(parsed['data'], tf.float64)
    }, parsed['labels'])

def train_input_fn(training_dir, hyperparameters):
    ds = PipeModeDataset(channel='training', record_format='TFRecord')
    ds = ds.repeat(20)
    ds = ds.prefetch(10)
    ds = ds.map(parse, num_parallel_calls=10)
    ds = ds.batch(64)
    return ds

To run training job with Pipe input mode, pass in input_mode='Pipe' to your TensorFlow Estimator:

from sagemaker.tensorflow import TensorFlow

tf_estimator = TensorFlow(entry_point='tf-train-with-pipemodedataset.py', role='SageMakerRole',
                          training_steps=10000, evaluation_steps=100,
                          train_instance_count=1, train_instance_type='ml.p2.xlarge',
                          framework_version='1.10.0', input_mode='Pipe')

tf_estimator.fit('s3://bucket/path/to/training/data')

If your TFRecords are compressed, you can train on Gzipped TF Records by passing in compression='Gzip' to the call to fit(), and SageMaker will automatically unzip the records as data is streamed to your training instances:

from sagemaker.session import s3_input

train_s3_input = s3_input('s3://bucket/path/to/training/data', compression='Gzip')
tf_estimator.fit(train_s3_input)

You can learn more about PipeModeDataset in the sagemaker-tensorflow-extensions repository: https://github.com/aws/sagemaker-tensorflow-extensions

Training with MKL-DNN disabled

SageMaker TensorFlow CPU images use TensorFlow built with Intel® MKL-DNN optimization.

In certain cases you might be able to get a better performance by disabling this optimization (for example when using small models)

You can disable MKL-DNN optimization for TensorFlow 1.8.0 and above by setting two following environment variables:

import os

os.environ['TF_DISABLE_MKL'] = '1'
os.environ['TF_DISABLE_POOL_ALLOCATOR'] = '1'

Deploy TensorFlow Serving models

After a TensorFlow estimator has been fit, it saves a TensorFlow SavedModel in the S3 location defined by output_path. You can call deploy on a TensorFlow estimator to create a SageMaker Endpoint, or you can call transformer to create a Transformer that you can use to run a batch transform job.

Your model will be deployed to a TensorFlow Serving-based server. The server provides a super-set of the TensorFlow Serving REST API.

Deploy to a SageMaker Endpoint

Deploying from an Estimator

After a TensorFlow estimator has been fit, it saves a TensorFlow SavedModel bundle in the S3 location defined by output_path. You can call deploy on a TensorFlow estimator object to create a SageMaker Endpoint:

from sagemaker.tensorflow import TensorFlow

estimator = TensorFlow(entry_point='tf-train.py', ..., train_instance_count=1,
                       train_instance_type='ml.c4.xlarge', framework_version='1.11')

estimator.fit(inputs)

predictor = estimator.deploy(initial_instance_count=1,
                             instance_type='ml.c5.xlarge',
                             endpoint_type='tensorflow-serving')

The code block above deploys a SageMaker Endpoint with one instance of the type ‘ml.c5.xlarge’.

What happens when deploy is called

Calling deploy starts the process of creating a SageMaker Endpoint. This process includes the following steps.

  • Starts initial_instance_count EC2 instances of the type instance_type.
  • On each instance, it will do the following steps:
    • start a Docker container optimized for TensorFlow Serving, see SageMaker TensorFlow Serving containers.
    • start a TensorFlow Serving process configured to run your model.
    • start an HTTP server that provides access to TensorFlow Server through the SageMaker InvokeEndpoint API.

When the deploy call finishes, the created SageMaker Endpoint is ready for prediction requests. The Making predictions against a SageMaker Endpoint section will explain how to make prediction requests against the Endpoint.

Deploying directly from model artifacts

If you already have existing model artifacts in S3, you can skip training and deploy them directly to an endpoint:

from sagemaker.tensorflow.serving import Model

model = Model(model_data='s3://mybucket/model.tar.gz', role='MySageMakerRole')

predictor = model.deploy(initial_instance_count=1, instance_type='ml.c5.xlarge')

Python-based TensorFlow serving on SageMaker has support for Elastic Inference, which allows for inference acceleration to a hosted endpoint for a fraction of the cost of using a full GPU instance. In order to attach an Elastic Inference accelerator to your endpoint provide the accelerator type to accelerator_type to your deploy call.

from sagemaker.tensorflow.serving import Model

model = Model(model_data='s3://mybucket/model.tar.gz', role='MySageMakerRole')

predictor = model.deploy(initial_instance_count=1, instance_type='ml.c5.xlarge', accelerator_type='ml.eia1.medium')

Making predictions against a SageMaker Endpoint

Once you have the Predictor instance returned by model.deploy(...) or estimator.deploy(...), you can send prediction requests to your Endpoint.

The following code shows how to make a prediction request:

input = {
  'instances': [1.0, 2.0, 5.0]
}
result = predictor.predict(input)

The result object will contain a Python dict like this:

{
  'predictions': [3.5, 4.0, 5.5]
}

The formats of the input and the output data correspond directly to the request and response formats of the Predict method in the TensorFlow Serving REST API.

If your SavedModel includes the right signature_def, you can also make Classify or Regress requests:

# input matches the Classify and Regress API
input = {
  'signature_name': 'tensorflow/serving/regress',
  'examples': [{'x': 1.0}, {'x': 2.0}]
}

result = predictor.regress(input)  # or predictor.classify(...)

# result contains:
{
  'results': [3.5, 4.0]
}

You can include multiple instances in your predict request (or multiple examples in classify/regress requests) to get multiple prediction results in one request to your Endpoint:

input = {
  'instances': [
    [1.0, 2.0, 5.0],
    [1.0, 2.0, 5.0],
    [1.0, 2.0, 5.0]
  ]
}
result = predictor.predict(input)

# result contains:
{
  'predictions': [
    [3.5, 4.0, 5.5],
    [3.5, 4.0, 5.5],
    [3.5, 4.0, 5.5]
  ]
}

If your application allows request grouping like this, it is much more efficient than making separate requests.

See Deploying to TensorFlow Serving Endpoints <https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/tensorflow/deploying_tensorflow_serving.rst> to learn how to deploy your model and make inference requests.

Run a Batch Transform Job

Batch transform allows you to get inferences for an entire dataset that is stored in an S3 bucket.

For general information about using batch transform with the SageMaker Python SDK, see SageMaker Batch Transform. For information about SageMaker batch transform, see Get Inferences for an Entire Dataset with Batch Transform <https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-batch.html> in the AWS documentation.

To run a batch transform job, you first create a Transformer object, and then call that object’s transform method.

Create a Transformer Object

If you used an estimator to train your model, you can call the transformer method of the estimator to create a Transformer object.

For example:

bucket = myBucket # The name of the S3 bucket where the results are stored
prefix = 'batch-results' # The folder in the S3 bucket where the results are stored

batch_output = 's3://{}/{}/results'.format(bucket, prefix) # The location to store the results

tf_transformer = tf_estimator.transformer(instance_count=1, instance_type='ml.m4.xlarge, output_path=batch_output)

To use a model trained outside of SageMaker, you can package the model as a SageMaker model, and call the transformer method of the SageMaker model.

For example:

bucket = myBucket # The name of the S3 bucket where the results are stored
prefix = 'batch-results' # The folder in the S3 bucket where the results are stored

batch_output = 's3://{}/{}/results'.format(bucket, prefix) # The location to store the results

tf_transformer = tensorflow_serving_model.transformer(instance_count=1, instance_type='ml.m4.xlarge, output_path=batch_output)

For information about how to package a model as a SageMaker model, see BYO Model. When you call the tranformer method, you specify the type and number of instances to use for the batch transform job, and the location where the results are stored in S3.

Call transform

After you create a Transformer object, you call that object’s transform method to start a batch transform job. For example:

batch_input = 's3://{}/{}/test/examples'.format(bucket, prefix) # The location of the input dataset

tf_transformer.transform(data=batch_input, data_type='S3Prefix', content_type='text/csv', split_type='Line')

In the example, the content type is CSV, and each line in the dataset is treated as a record to get a predition for.

Batch Transform Supported Data Formats

When you call the tranform method to start a batch transform job, you specify the data format by providing a MIME type as the value for the content_type parameter.

The following content formats are supported without custom intput and output handling:

  • CSV - specify text/csv as the value of the content_type parameter.
  • JSON - specify application/json as the value of the content_type parameter.
  • JSON lines - specify application/jsonlines as the value of the content_type parameter.

For detailed information about how TensorFlow Serving formats these data types for input and output, see TensorFlow Serving Input and Output.

You can also accept any custom data format by writing input and output functions, and include them in the inference.py file in your model. For information, see Create Python Scripts for Custom Input and Output Formats.

TensorFlow Serving Input and Output

The following sections describe the data formats that TensorFlow Serving endpoints and batch transform jobs accept, and how to write input and output functions to input and output custom data formats.

Supported Formats

SageMaker’s TensforFlow Serving endpoints can also accept some additional input formats that are not part of the TensorFlow REST API, including a simplified json format, line-delimited json objects (“jsons” or “jsonlines”), and CSV data.

Simplified JSON Input

The Endpoint will accept simplified JSON input that doesn’t match the TensorFlow REST API’s Predict request format. When the Endpoint receives data like this, it will attempt to transform it into a valid Predict request, using a few simple rules:

  • python value, dict, or one-dimensional arrays are treated as the input value in a single ‘instance’ Predict request.
  • multidimensional arrays are treated as a multiple values in a multi-instance Predict request.

Combined with the client-side Predictor object’s JSON serialization, this allows you to make simple requests like this:

input = [
  [1.0, 2.0, 5.0],
  [1.0, 2.0, 5.0]
]
result = predictor.predict(input)

# result contains:
{
  'predictions': [
    [3.5, 4.0, 5.5],
    [3.5, 4.0, 5.5]
  ]
}

Or this:

# 'x' must match name of input tensor in your SavedModel graph
# for models with multiple named inputs, just include all the keys in the input dict
input = {
  'x': [1.0, 2.0, 5.0]
}

# result contains:
{
  'predictions': [
    [3.5, 4.0, 5.5]
  ]
}
Line-delimited JSON

The Endpoint will accept line-delimited JSON objects (also known as “jsons” or “jsonlines” data). The Endpoint treats each line as a separate instance in a multi-instance Predict request. To use this feature from your python code, you need to create a Predictor instance that does not try to serialize your input to JSON:

# create a Predictor without JSON serialization

predictor = Predictor('endpoint-name', serializer=None, content_type='application/jsonlines')

input = '''{'x': [1.0, 2.0, 5.0]}
{'x': [1.0, 2.0, 5.0]}
{'x': [1.0, 2.0, 5.0]}'''

result = predictor.predict(input)

# result contains:
{
  'predictions': [
    [3.5, 4.0, 5.5],
    [3.5, 4.0, 5.5],
    [3.5, 4.0, 5.5]
  ]
}

This feature is especially useful if you are reading data from a file containing jsonlines data.

CSV (comma-separated values)

The Endpoint will accept CSV data. Each line is treated as a separate instance. This is a compact format for representing multiple instances of 1-d array data. To use this feature from your python code, you need to create a Predictor instance that can serialize your input data to CSV format:

# create a Predictor with JSON serialization

predictor = Predictor('endpoint-name', serializer=sagemaker.predictor.csv_serializer)

# CSV-formatted string input
input = '1.0,2.0,5.0\n1.0,2.0,5.0\n1.0,2.0,5.0'

result = predictor.predict(input)

# result contains:
{
  'predictions': [
    [3.5, 4.0, 5.5],
    [3.5, 4.0, 5.5],
    [3.5, 4.0, 5.5]
  ]
}

You can also use python arrays or numpy arrays as input and let the csv_serializer object convert them to CSV, but the client-size CSV conversion is more sophisticated than the CSV parsing on the Endpoint, so if you encounter conversion problems, try using one of the JSON options instead.

Create Python Scripts for Custom Input and Output Formats

You can add your customized Python code to process your input and output data:

from sagemaker.tensorflow.serving import Model

model = Model(entry_point='inference.py',
              model_data='s3://mybucket/model.tar.gz',
              role='MySageMakerRole')
How to implement the pre- and/or post-processing handler(s)
Your entry point file should implement either a pair of input_handler
and output_handler functions or a single handler function. Note that if handler function is implemented, input_handler and output_handler are ignored.

To implement pre- and/or post-processing handler(s), use the Context object that the Python service creates. The Context object is a namedtuple with the following attributes:

  • model_name (string): the name of the model to use for inference. For example, ‘half-plus-three’
  • model_version (string): version of the model. For example, ‘5’
  • method (string): inference method. For example, ‘predict’, ‘classify’ or ‘regress’, for more information on methods, please see Classify and Regress API and Predict API
  • rest_uri (string): the TFS REST uri generated by the Python service. For example, ‘http://localhost:8501/v1/models/half_plus_three:predict
  • grpc_uri (string): the GRPC port number generated by the Python service. For example, ‘9000’
  • custom_attributes (string): content of ‘X-Amzn-SageMaker-Custom-Attributes’ header from the original request. For example, ‘tfs-model-name=half*plus*three,tfs-method=predict’
  • request_content_type (string): the original request content type, defaulted to ‘application/json’ if not provided
  • accept_header (string): the original request accept type, defaulted to ‘application/json’ if not provided
  • content_length (int): content length of the original request

The following code example implements input_handler and output_handler. By providing these, the Python service posts the request to the TFS REST URI with the data pre-processed by input_handler and passes the response to output_handler for post-processing.

import json

def input_handler(data, context):
    """ Pre-process request input before it is sent to TensorFlow Serving REST API
    Args:
        data (obj): the request data, in format of dict or string
        context (Context): an object containing request and configuration details
    Returns:
        (dict): a JSON-serializable dict that contains request body and headers
    """
    if context.request_content_type == 'application/json':
        # pass through json (assumes it's correctly formed)
        d = data.read().decode('utf-8')
        return d if len(d) else ''

    if context.request_content_type == 'text/csv':
        # very simple csv handler
        return json.dumps({
            'instances': [float(x) for x in data.read().decode('utf-8').split(',')]
        })

    raise ValueError('{{"error": "unsupported content type {}"}}'.format(
        context.request_content_type or "unknown"))


def output_handler(data, context):
    """Post-process TensorFlow Serving output before it is returned to the client.
    Args:
        data (obj): the TensorFlow serving response
        context (Context): an object containing request and configuration details
    Returns:
        (bytes, string): data to return to client, response content type
    """
    if data.status_code != 200:
        raise ValueError(data.content.decode('utf-8'))

    response_content_type = context.accept_header
    prediction = data.content
    return prediction, response_content_type

You might want to have complete control over the request. For example, you might want to make a TFS request (REST or GRPC) to the first model, inspect the results, and then make a request to a second model. In this case, implement the handler method instead of the input_handler and output_handler methods, as demonstrated in the following code:

import json
import requests


def handler(data, context):
    """Handle request.
    Args:
        data (obj): the request data
        context (Context): an object containing request and configuration details
    Returns:
        (bytes, string): data to return to client, (optional) response content type
    """
    processed_input = _process_input(data, context)
    response = requests.post(context.rest_uri, data=processed_input)
    return _process_output(response, context)


def _process_input(data, context):
    if context.request_content_type == 'application/json':
        # pass through json (assumes it's correctly formed)
        d = data.read().decode('utf-8')
        return d if len(d) else ''

    if context.request_content_type == 'text/csv':
        # very simple csv handler
        return json.dumps({
            'instances': [float(x) for x in data.read().decode('utf-8').split(',')]
        })

    raise ValueError('{{"error": "unsupported content type {}"}}'.format(
        context.request_content_type or "unknown"))


def _process_output(data, context):
    if data.status_code != 200:
        raise ValueError(data.content.decode('utf-8'))

    response_content_type = context.accept_header
    prediction = data.content
    return prediction, response_content_type

You can also bring in external dependencies to help with your data processing. There are 2 ways to do this:

  1. If you included requirements.txt in your source_dir or in your dependencies, the container installs the Python dependencies at runtime using pip install -r:
from sagemaker.tensorflow.serving import Model

model = Model(entry_point='inference.py',
              dependencies=['requirements.txt'],
              model_data='s3://mybucket/model.tar.gz',
              role='MySageMakerRole')
  1. If you are working in a network-isolation situation or if you don’t want to install dependencies at runtime every time your endpoint starts or a batch transform job runs, you might want to put pre-downloaded dependencies under a lib directory and this directory as dependency. The container adds the modules to the Python path. Note that if both lib and requirements.txt are present in the model archive, the requirements.txt is ignored:
from sagemaker.tensorflow.serving import Model

model = Model(entry_point='inference.py',
              dependencies=['/path/to/folder/named/lib'],
              model_data='s3://mybucket/model.tar.gz',
              role='MySageMakerRole')

sagemaker.tensorflow.TensorFlow Class

The following are the most commonly used TensorFlow constructor arguments.

Required:

  • entry_point (str) Path (absolute or relative) to the Python file which should be executed as the entry point to training.
  • 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 accessing AWS resource.
  • train_instance_count (int) Number of Amazon EC2 instances to use for training.
  • train_instance_type (str) Type of EC2 instance to use for training, for example, ‘ml.c4.xlarge’.

Optional:

  • source_dir (str) Path (absolute or relative) to a directory with any other training source code dependencies including the entry point file. Structure within this directory will be preserved when training on SageMaker.

  • dependencies (list[str]) A list of paths to directories (absolute or relative) with any additional libraries that will be exported to the container (default: []). The library folders will be copied to SageMaker in the same folder where the entrypoint is copied. If the source_dir points to S3, code will be uploaded and the S3 location will be used instead. Example:

    The following call

    >>> TensorFlow(entry_point='train.py', dependencies=['my/libs/common', 'virtual-env'])
    

    results in the following inside the container:

    >>> opt/ml/code
    >>>     ├── train.py
    >>>     ├── common
    >>>     └── virtual-env
    
  • hyperparameters (dict[str, ANY]) Hyperparameters that will be used for training. Will be made accessible as command line arguments.

  • train_volume_size (int) Size in GB of the EBS volume to use for storing input data during training. Must be large enough to the store training data.

  • train_max_run (int) Timeout in seconds for training, after which Amazon SageMaker terminates the job regardless of its current status.

  • output_path (str) S3 location where you want the training result (model artifacts and optional output files) saved. If not specified, results are stored to a default bucket. If the bucket with the specific name does not exist, the estimator creates the bucket during the fit method execution.

  • output_kms_key Optional KMS key ID to optionally encrypt training output with.

  • base_job_name Name to assign for the training job that the fit method launches. If not specified, the estimator generates a default job name, based on the training image name and current timestamp.

  • image_name An alternative docker image to use for training and serving. 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. Refer to: SageMaker TensorFlow Docker containers for details on what the official images support and where to find the source code to build your custom image.

  • script_mode (bool) Whether to use Script Mode or not. Script mode is the only available training mode in Python 3, setting py_version to py3 automatically sets script_mode to True.

  • model_dir (str) Location where model data, checkpoint data, and TensorBoard checkpoints should be saved during training. If not specified a S3 location will be generated under the training job’s default bucket. And model_dir will be passed in your training script as one of the command line arguments.

  • distributions (dict) Configure your distribution strategy with this argument.

SageMaker TensorFlow Docker containers

For information about SageMaker TensorFlow Docker containers and their dependencies, see SageMaker TensorFlow Docker containers.