Start testing 1.13 nightly to be ready for the next PyTorch release.
Motivation
Soon there will be a release of PyTorch 1.13. To be ready for it, I propose to start testing 1.13 nightly against our test suite.
Pitch
Open a PR updating the requirements, CI configuration etc.
Run the test suite.
Start fixing issues and tests
Additional context
Related: #15010
If you enjoy Lightning, check out our other projects! âš¡
Metrics: Machine learning metrics for distributed, scalable PyTorch applications.
Lite: enables pure PyTorch users to scale their existing code on any kind of device while retaining full control over their own loops and optimization logic.
Flash: The fastest way to get a Lightning baseline! A collection of tasks for fast prototyping, baselining, fine-tuning, and solving problems with deep learning.
Bolts: Pretrained SOTA Deep Learning models, callbacks, and more for research and production with PyTorch Lightning and PyTorch.
Lightning Transformers: Flexible interface for high-performance research using SOTA Transformers leveraging PyTorch Lightning, Transformers, and Hydra.
🚀 Feature
Start testing 1.13 nightly to be ready for the next PyTorch release.
Motivation
Soon there will be a release of PyTorch 1.13. To be ready for it, I propose to start testing 1.13 nightly against our test suite.
Pitch
Additional context
Related: #15010
If you enjoy Lightning, check out our other projects! âš¡
Metrics: Machine learning metrics for distributed, scalable PyTorch applications.
Lite: enables pure PyTorch users to scale their existing code on any kind of device while retaining full control over their own loops and optimization logic.
Flash: The fastest way to get a Lightning baseline! A collection of tasks for fast prototyping, baselining, fine-tuning, and solving problems with deep learning.
Bolts: Pretrained SOTA Deep Learning models, callbacks, and more for research and production with PyTorch Lightning and PyTorch.
Lightning Transformers: Flexible interface for high-performance research using SOTA Transformers leveraging PyTorch Lightning, Transformers, and Hydra.