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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. 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. 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.
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
Project Samples
