pytorch-lightning
https://github.com/pytorchlightning/pytorch-lightning
Python
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.
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- Issues
- can't fit with ddp_notebook on a Vertex AI Workbench instance (CUDA initialized)
- WIP: Integrate Collective into strategies
- Using the MLflow logger produces Inconsistent metric plots
- Possible bug in recognizing `mps` accelerator even though PyTorch seems to register the `mps` device?
- Error loading a saved model to run inference (using ddp_notebook strategy)
- Resume training, how to change learning scheduler?
- Added some more potentially robust ways to do learning rate tuning
- Support get optimizer and lr_schedulers from deepspeed config
- RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [68]] is at version 3; expected version 2 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).
- When doing tuner.scale_batch_size, check full dataset length first
- Docs
- Python not yet supported