DRAGON | DRAGON makes it easy to deploy distributed parameter server

by NTHU-LSALAB Go Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | DRAGON Summary

DRAGON is a Go library. DRAGON has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

DRAGON makes it easy to deploy distributed parameter server TensorFlow training jobs with automatic scheduling and scaling strategies. DRAGON bypass the kube-scheduler, directly assigns the location to every distributed training components. DRAGON is a custom scheduler for distributed TensorFlow training jobs.

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Quality

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Support

DRAGON has a low active ecosystem.

It has 3 star(s) with 0 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

DRAGON has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of DRAGON is current.

Quality

DRAGON has no bugs reported.

Security

DRAGON has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

DRAGON is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

DRAGON releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

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DRAGON Key Features

No Key Features are available at this moment for DRAGON.

DRAGON Examples and Code Snippets

No Code Snippets are available at this moment for DRAGON.

Community Discussions

No Community Discussions are available at this moment for DRAGON.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install DRAGON

Installation depends on various use cases:.
Training with CPU
Training with NVIDIA GPU (NVIDIA GPU)
Training with portion NVIDIA GPU allocation (KubeShare)

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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