energy-transformer-jax | The Energy Transformer block , in JAX

by bhoov Jupyter Notebook Version: Current License: Apache-2.0

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

kandi X-RAY | energy-transformer-jax Summary

energy-transformer-jax is a Jupyter Notebook library. energy-transformer-jax has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

The Energy Transformer block, in JAX

Support

Quality

Security

License

Reuse

Support

energy-transformer-jax has a low active ecosystem.

It has 5 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

energy-transformer-jax has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of energy-transformer-jax is current.

Quality

energy-transformer-jax has no bugs reported.

Security

energy-transformer-jax has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

energy-transformer-jax 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

energy-transformer-jax 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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energy-transformer-jax Key Features

No Key Features are available at this moment for energy-transformer-jax.

energy-transformer-jax Examples and Code Snippets

No Code Snippets are available at this moment for energy-transformer-jax.

Community Discussions

No Community Discussions are available at this moment for energy-transformer-jax.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install energy-transformer-jax

We use conda to choose the nvcc and cudnn versions that work with our system (which uses A100 gpus) and JAX.
The training code in this work assumes the ImageNet-1k Dataset in standard PyTorch ImageFolder structure.

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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