Search
Abstract
TensorHive is a tool for organizing work of research and engineering teams that use servers with GPUs for machine learning workloads. In a comprehensive web interface, it supports reservation of GPUs for exclusive usage, hardware monitoring, as well as configuring, executing and queuing distributed computational jobs. Focusing on easy installation and simple configuration, the tool automatically detects the available computing resources and monitors their utilization. Reservations granted on the basis of flexible access control settings are protected by pluggable violation hooks. The job execution module includes auto-configuration templates for distributed neural network training jobs in frameworks such as TensorFlow and PyTorch. Documentation, source code, usage examples and issue tracking are available at the project page: https://github.com/roscisz/TensorHive/
Authors (3)
Cite as
Full text
- Publication version
- Accepted or Published Version
- License
-
open in new tab
Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
JOURNAL OF MACHINE LEARNING RESEARCH
no. 22,
pages 1 - 5,
ISSN: 1532-4435 - Language:
- English
- Publication year:
- 2021
- Bibliographic description:
- Rościszewski P., Martyniak M., Schodowski F.: TensorHive: Management of Exclusive GPU Access for Distributed Machine Learning Workloads// JOURNAL OF MACHINE LEARNING RESEARCH -,iss. 22 (2021), s.1-5
- Verified by:
- Gdańsk University of Technology
seen 138 times