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

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.

References in corpus (5)

• MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
• cuDNN: Efficient Primitives for Deep Learning
• DyNet: The Dynamic Neural Network Toolkit
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• LazyTensor: combining eager execution with domain-specific compilers
• From Domain-Specific Languages to Memory-Optimized Accelerators for Fluid Dynamics
• HyPar-Flow: Exploiting MPI and Keras for Scalable Hybrid-Parallel DNN Training using TensorFlow
• DKM: Differentiable K-Means Clustering Layer for Neural Network Compression
• Swift for TensorFlow: A portable, flexible platform for deep learning
• Diffeomorphic Transformations for Time Series Analysis: An Efficient Approach to Nonlinear Warping
• Automatic Creation of High-Bandwidth Memory Architectures from Domain-Specific Languages: The Case of Computational Fluid Dynamics