Use TensorFlow with the SageMaker Python SDK

With the SageMaker Python SDK, you can train and host TensorFlow models on Amazon SageMaker.

For information about supported versions of TensorFlow, see the AWS documentation. We recommend that you use the latest supported version because that’s where we focus our development efforts.

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

Warning

Support for TensorFlow versions 1.4-1.10 has been deprecated. For information on how to upgrade, see Upgrade from Legacy TensorFlow Support.

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 Training Script

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 can be 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_MODEL_DIR 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.

Use third-party libraries

If there are other packages you want to use with your script, you can use a requirements.txt to install other dependencies at runtime.

For training, support for installing packages using requirements.txt varies by TensorFlow version as follows:

  • For TensorFlow 1.15.2 with Python 3.7 or newer, and TensorFlow 2.2 or newer:
    • Include a requirements.txt file in the same directory as your training script.

    • You must specify this directory using the source_dir argument when creating a TensorFlow estimator.

  • For TensorFlow versions 1.11-1.15.2, 2.0-2.1 with Python 2.7 or 3.6:
    • Write a shell script for your entry point that first calls pip install -r requirements.txt, then runs your training script.

    • For an example of using shell scripts, see this example notebook.

  • For legacy versions of TensorFlow:

For serving, support for installing packages using requirements.txt varies by TensorFlow version as follows:

A requirements.txt file is a text file that contains a list of items that are installed by using pip install. You can also specify the version of an item to install. For information about the format of a requirements.txt file, see Requirements Files in the pip documentation.

Create an Estimator

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

To use Python 3.7, please specify both of the args:

  • py_version='py37'

  • framework_version='1.15.2'

from sagemaker.tensorflow import TensorFlow

tf_estimator = TensorFlow(
    entry_point="tf-train.py",
    role="SageMakerRole",
    instance_count=1,
    instance_type="ml.p2.xlarge",
    framework_version="2.2",
    py_version="py37",
)
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.

Specify a Docker image using an Estimator

There are use cases, such as extending an existing pre-built Amazon SageMaker images, that require specifing a Docker image when creating an Estimator by directly specifying the ECR URI instead of the Python and framework version. For a full list of available container URIs, see Available Deep Learning Containers Images For more information on using Docker containers, see Use Your Own Algorithms or Models with Amazon SageMaker.

When specifying the image, you must use the image_name='' arg to replace the following arg:

  • py_version=''

You should still specify the framework_version='' arg because the SageMaker Python SDK accomodates for differences in the images based on the version.

The following example uses the image_name='' arg to specify the container image, Python version, and framework version.

tf_estimator = TensorFlow(entry_point='tf-train.py',
                          role='SageMakerRole',
                          train_instance_count=1,
                          train_instance_type='ml.p2.xlarge',
                          image_name='763104351884.dkr.ecr.<region>.amazonaws.com/<framework>-<job type>:<framework version>-<cpu/gpu>-<python version>-ubuntu18.04',
                          script_mode=True)

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

Call the fit Method

You start your training script by calling the fit method on a TensorFlow estimator.

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

  • Starts instance_count EC2 instances of the type 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)

For more information about the options available for fit, see the API documentation.

Distributed Training

To run your training job with multiple instances in a distributed fashion, set instance_count to a number larger than 1. We support two different types of distributed training, parameter server and Horovod. The distribution 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 distribution 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",
    instance_count=2,
    instance_type="ml.p2.xlarge",
    framework_version="2.2",
    py_version="py37",
    distribution={"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.

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

  • 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 4 processes on one host:

from sagemaker.tensorflow import TensorFlow

tf_estimator = TensorFlow(
    entry_point="tf-train.py",
    role="SageMakerRole",
    instance_count=1,
    instance_type="ml.p3.8xlarge",
    framework_version="2.1.0",
    py_version="py3",
    distribution={
        "mpi": {
            "enabled": True,
            "processes_per_host": 4,
            "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,
    instance_count=1,
    instance_type="ml.p2.xlarge",
    framework_version="1.10.0",
    py_version="py3",
    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.inputs import TrainingInput

train_s3_input = TrainingInput('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",
    ...,
    instance_count=1,
    instance_type="ml.c4.xlarge",
    framework_version="2.2",
    py_version="py37",
)

estimator.fit(inputs)

predictor = estimator.deploy(initial_instance_count=1, instance_type="ml.c5.xlarge")

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 import TensorFlowModel

model = TensorFlowModel(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 import TensorFlowModel

model = TensorFlowModel(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 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.serializers.CSVSerializer())

# 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 CSVSerializer 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. This customized Python code must be named inference.py and is specified through the entry_point parameter:

from sagemaker.tensorflow import TensorFlowModel

model = TensorFlowModel(entry_point='inference.py',
                        model_data='s3://mybucket/model.tar.gz',
                        role='MySageMakerRole')

In the example above, inference.py is assumed to be a file inside model.tar.gz. If you want to use a local file instead, you must add the source_dir argument. See the documentation on TensorFlowModel.

How to implement the pre- and/or post-processing handler(s)

Your entry point file must be named inference.py and 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 your model archive contains code/requirements.txt, the container will install the Python dependencies at runtime using pip install -r.

from sagemaker.tensorflow import TensorFlowModel

model = TensorFlowModel(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 import TensorFlowModel

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

For more information, see: https://github.com/aws/sagemaker-tensorflow-serving-container#prepost-processing

SageMaker TensorFlow Classes

For information about the different TensorFlow-related classes in the SageMaker Python SDK, see https://sagemaker.readthedocs.io/en/stable/sagemaker.tensorflow.html.