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
- I think it's deadly necessary to add docs or tutorials for handling the case when We return multiple loaders in test_dataloaders() method? I think it
- Error when fast_dev_run=True or num_sanity_val_steps=0 and using torchmetrics MetricTracker
- Fabric: Incorrect `num_replicas` (ddp/fsdp) when number of GPUs on each node is different
- MLFlowLogger fails when logging hyperparameters as Trainer already does automatically
- Is "Prepare a config file for the CLI" out of date?
- MisconfigurationException: Do not set `gradient_accumulation_steps` in the DeepSpeed config
- Dataloader on multi-gpu jobs only surpport to manipulate on local_rank=0, is there a way tom manipulate every device?
- Lightning stalls with 2 GPUs on 1 node with SLURM (and apptainer)
- can't fit with ddp_notebook on a Vertex AI Workbench instance (CUDA initialized)
- WIP: Integrate Collective into strategies
- Docs
- Python not yet supported