Comments (1)
never mind for now as long as we don't have major issues
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Related Issues (20)
- Missing documentation for the `log_weight_decay` argument in `lightning.pytorch.callbacks.LearningRateMonitor`
- parsing issue with `save_last` parameter of `ModelCheckpoint`
- Construct objects from yaml by classmethod
- FSDP Strategy checkpoint loading
- Current FSDPPrecision does not support custom scaler for 16-mixed precision
- Differentiate testing multiple sets/models when logging
- Issue in Manual optimisation, during self.manual_backward call HOT 1
- Existing metric keys not moved to device after LearningRateFinder
- Checkpoint every_n_steps reruns epoch on restore HOT 3
- Metrics logged by self.log and metric.compute() are different HOT 1
- Multi-node Training with DDP stuck at "Initialize distributed..." on SLURM cluster HOT 3
- Full validation after first microbatch when training after LearningRateFinder
- Add a warning when some of the modules are in eval mode before the training stage
- why pytorch-lightning doc say "Model-parallel training (FSDP and DeepSpeed)". I think there is something wrong. HOT 1
- AWS Trainium fails number of device validation when using more than 1 accelerator on the instances
- OnExceptionCheckpoint: training resumes if ckpt found, even if no ckpt_path provided
- TensorBoardLogger has the wrong epoch numbers much more than the fact
- How to incorporate vLLM in Lightning for LLM inference?
- WandbLogger `save_dir` and `dir` parameters do not work as expected.
- Loading large models with fabric, FSDP and empty_init=True does not work
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