Related Issues (20)
- False positive iterable dataset warning for LitData StreamingDataset
- Inconsistent input io type between `to_onnx` and `torch.onnx.export`.
- loss spikes in validation step when the model has multiple losses applied HOT 1
- Trainer does not switch to train mode after validation step HOT 2
- trainer.validate() get different result from trainer.fit
- trainer test and validate have issues with autograd
- `Error while merging hparams` when using LightningCLI and YAML HOT 3
- MLFlowLogger does not save config.yaml for each run
- Checkpoint callback run before validation step - stale or none monitor values considered for validation metrics HOT 2
- OnExceptionCheckpoint callback suppresses exceptions and results in NCCL timeout
- Seeding and multi-GPU training HOT 1
- Support IO Type Checkpoints for trainer.fit() in ckpt_path Parameter
- shortcuts for logging weights and biases norms
- Unable to load Checkpoint
- Add param_group name for BaseFinetuningCallback
- LightningCLI: --help argument given after the subcommand fails
- ModelCheckpoint Callback not working/saving unless `save_on_train_epoch_end` is enabled True which considerably slows down training
- 7x slower training speed when switching from lightning 1.0 to 2.0
- Loading a model changes pytorch random state
- Allow passing custom reader/writer in _distributed_checkpoint_save and _distributed_checkpoint_load.
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