TensorFlow-Serving: Flexible, High-Performance ML Serving
Abstract
We describe TensorFlow-Serving, a system to serve machine learning models inside
Google which is also available in the cloud and via open-source. It is extremely
flexible in terms of the types of ML platforms it supports, and ways to
integrate with systems that convey new models and updated versions from training
to serving. At the same time, the core code paths around model lookup and
inference have been carefully optimized to avoid performance pitfalls observed
in naive implementations.
The paper covers the architecture of the extensible serving library, as well as
the distributed system for multi-tenant model hosting. Along the way it points
out which extensibility points and performance optimizations turned out to be
especially important based on production experience.
Google which is also available in the cloud and via open-source. It is extremely
flexible in terms of the types of ML platforms it supports, and ways to
integrate with systems that convey new models and updated versions from training
to serving. At the same time, the core code paths around model lookup and
inference have been carefully optimized to avoid performance pitfalls observed
in naive implementations.
The paper covers the architecture of the extensible serving library, as well as
the distributed system for multi-tenant model hosting. Along the way it points
out which extensibility points and performance optimizations turned out to be
especially important based on production experience.
Research Areas
