TensorFlow Serving
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 https://github.com/tensorflow/serving
# Location of demo models
TESTDATA="$(pwd)/serving/tensorflow_serving/servables/tensorflow/testdata"
# Start TensorFlow Serving container and open the REST API port
docker run -t --rm -p 8501:8501 \
-v "$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.
Documentation
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.
- Install Tensorflow Serving using Docker
(Recommended) - Install Tensorflow Serving without Docker
(Not Recommended) - Build Tensorflow Serving from Source with Docker
- Deploy Tensorflow Serving on Kubernetes
Use
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
- Follow a tutorial on Serving Tensorflow models
- Configure Tensorflow Serving to make it fit your serving use case
- Read the Performance Guide
and learn how to use TensorBoard to profile and optimize inference requests - Read the REST API Guide
or gRPC API definition - Use SavedModel Warmup if initial inference requests are slow due to lazy initialization of graph
- If encountering issues regarding model signatures, please read the SignatureDef documentation
- If using a model with custom ops, learn how to serve models with custom ops
Extend
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.
- Ensure you are familiar with building Tensorflow Serving
- Learn about Tensorflow Serving's architecture
- Explore the Tensorflow Serving C++ API reference
- Create a new type of Servable
- Create a custom Source of Servable versions
Contribute
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.