[Ecosystem] FairScale #63
Description
Contact emails
TBA
Project summary
PyTorch extension library for high performance and large scale training. This library extends basic PyTorch capabilities while adding new SOTA scaling techniques.
Project description
FairScale is a PyTorch extension library for high performance and large scale training. This library extends basic PyTorch capabilities while adding new SOTA scaling techniques. FairScale makes available the latest distributed training techniques in the form of composable modules and easy to use APIs. These APIs are a fundamental part of a researcher's toolbox as they attempt to scale models with limited resources.
FairScale was designed with the following values in mind:
Usability - Users should be able to understand and use FairScale APIs with minimum cognitive overload.
Modularity - Users should be able to combine multiple FairScale APIs as part of their training loop seamlessly.
Performance - FairScale APIs provide the best performance in terms of scaling and efficiency.
Are there any other projects in the PyTorch Ecosystem similar to yours? If, yes, what are they?
TBA
Project repo URL
https://github.com/facebookresearch/fairscale
Additional repos in scope of the application
No response
Project license
BSD-3-Clause
GitHub handles of the project maintainer(s)
TBA
Is there a corporate or academic entity backing this project? If so, please provide the name and URL of the entity.
Meta
Website URL
https://github.com/facebookresearch/fairscale
Documentation
https://github.com/facebookresearch/fairscale/tree/main/docs
How do you build and test the project today (continuous integration)? Please describe.
TBA
Version of PyTorch
Latest stable release
Components of PyTorch
TBA
How long do you expect to maintain the project?
TBA
Additional information
No response