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TensorFlow Serving

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TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. It deals with the inference aspect of machine learning, taking models after training and managing their lifetimes, providing clients with versioned access via a high-performance, reference-counted lookup table. TensorFlow Serving provides out-of-the-box integration with TensorFlow models, but can be easily extended to serve other types of models and data.

To note a few features:

  • Can serve multiple models, or multiple versions of the same model simultaneously
  • Exposes both gRPC as well as HTTP inference endpoints
  • Allows deployment of new model versions without changing any client code
  • Supports canarying new versions and A/B testing experimental models
  • Adds minimal latency to inference time due to efficient, low-overhead implementation
  • Features a scheduler that groups individual inference requests into batches for joint execution on GPU, with configurable latency controls
  • Supports many servables: Tensorflow models, embeddings, vocabularies, feature transformations and even non-Tensorflow-based machine learning models

Serve a Tensorflow model in 60 seconds

# Download the TensorFlow Serving Docker image and repo
docker pull tensorflow/serving

git clone
# Location of demo models

# Start TensorFlow Serving container and open the REST API port
docker run -t --rm -p 8501:8501 \
    -v "$TESTDATA/saved_model_half_plus_two_cpu:/models/half_plus_two" \
    -e MODEL_NAME=half_plus_two \
    tensorflow/serving &

# Query the model using the predict API
curl -d '{"instances": [1.0, 2.0, 5.0]}' \
    -X POST http://localhost:8501/v1/models/half_plus_two:predict

# Returns => { "predictions": [2.5, 3.0, 4.5] }

End-to-End Training & Serving Tutorial

Refer to the official Tensorflow documentations site for a complete tutorial to train and serve a Tensorflow Model.


Set up

The easiest and most straight-forward way of using TensorFlow Serving is with Docker images. We highly recommend this route unless you have specific needs that are not addressed by running in a container.


Export your Tensorflow model

In order to serve a Tensorflow model, simply export a SavedModel from your Tensorflow program. SavedModel is a language-neutral, recoverable, hermetic serialization format that enables higher-level systems and tools to produce, consume, and transform TensorFlow models.

Please refer to Tensorflow documentation for detailed instructions on how to export SavedModels.

Configure and Use Tensorflow Serving


Tensorflow Serving's architecture is highly modular. You can use some parts individually (e.g. batch scheduling) and/or extend it to serve new use cases.


If you'd like to contribute to TensorFlow Serving, be sure to review the contribution guidelines.

For more information

Please refer to the official TensorFlow website for more information.