  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.
