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Since initially open-sourcing TensorFlow Serving in February 2016, we’ve made some major enhancements. Let’s take a look back at where we started, review our progress, and share where we are headed next.
Before TensorFlow Serving, users of TensorFlow inside Google had to create their own serving system from scratch. Although serving might appear easy at first, one-off serving solutions quickly grow in complexity. Machine Learning (ML) serving systems need to support model versioning (for model updates with a rollback option) and multiple models (for experimentation via A/B testing), while ensuring that concurrent models achieve high throughput on hardware accelerators (GPUs and TPUs) with low latency. So we set out to create a single, general TensorFlow Serving software stack.
We decided to make it open-sourceable from the get-go, and development started in September 2015. Within a few months, we created the initial end-to-end working system and our open-source release in February 2016.
Over the past year and half, with the help of our users and partners inside and outside our company, TensorFlow Serving has advanced performance, best practices, and standards:
All of our work has been informed by close collaborations with: (a) Google’s ML SRE team, which helps ensure we are robust and meet internal SLAs; (b) other Google machine learning infrastructure teams including ads serving and TFX; (c) application teams such as Google Play; (d) our partners at the UC Berkeley RISE Lab, who explore complementary research problems with the Clipper serving system; (e) our open-source user base and contributors.
TensorFlow Serving is currently handling tens of millions of inferences per second for 1100+ of our own projects including Google’s Cloud ML Prediction. Our core serving code is available to all via our open-source releases.
Looking forward, our work is far from done and we are exploring several avenues of innovation. Today we are excited to share early progress in two experimental areas:
Thanks again to all of our users and partners who have contributed feedback, code and ideas. Join the project at: github.com/tensorflow/serving.