TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning

TL;DR

TensorFlow Eager introduces a multi-stage, Python-embedded DSL that combines immediate execution with graph compilation for flexible, hardware-accelerated machine learning development.

Contribution

It provides an imperative front-end with a JIT tracer, enabling seamless switching between dynamic execution and optimized graph compilation.

Findings

Enables rapid prototyping with immediate execution.

Supports efficient deployment via dataflow graphs.

Balances usability and performance in machine learning workflows.

Abstract

TensorFlow Eager is a multi-stage, Python-embedded domain-specific language for hardware-accelerated machine learning, suitable for both interactive research and production. TensorFlow, which TensorFlow Eager extends, requires users to represent computations as dataflow graphs; this permits compiler optimizations and simplifies deployment but hinders rapid prototyping and run-time dynamism. TensorFlow Eager eliminates these usability costs without sacrificing the benefits furnished by graphs: It provides an imperative front-end to TensorFlow that executes operations immediately and a JIT tracer that translates Python functions composed of TensorFlow operations into executable dataflow graphs. TensorFlow Eager thus offers a multi-stage programming model that makes it easy to interpolate between imperative and staged execution in a single package.