arXiv (Cornell University)· 2016· Preprint
TensorFlow: A system for large-scale machine learning
- 8,829citations
- 2016year
Short summary
TensorFlow is a new machine learning system designed for large-scale, heterogeneous environments, utilizing dataflow graphs to represent computation and operations across multiple machines and devices like CPUs, GPUs, and TPUs.
AI-generated from the title and abstract; the full text is not read.
Key points
- Uses dataflow graphs to represent computation and state mutation.
- Maps computation across multiple machines and devices (CPUs, GPUs, TPUs).
- Offers developers flexibility to experiment with new optimizations and training algorithms.
- Supports training and inference for deep neural networks.
AI-generated from the title and abstract; the full text is not read.
Abstract
TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous "parameter server" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with particularly strong support for training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model in contrast to existing systems, and demonstrate the compelling performance that TensorFlow achieves for several real-world applications.
The authors' abstract, as published at the source. arXiv (Cornell University), 2016 · DOI ↗
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Field: Hardware and Architecture
Hardware and ArchitectureComputer Science