Comments (2)
setting the seed inside lightning seems like it hides too much according to ppl I’ve discussed with. i don’t want to have people wondering if lightning is doing anything with seeds.
setting seeds and such is deferred to the user.
from lightning.
How to set seed when I use 'ddp' for backend?
from lightning.
Related Issues (20)
- logged images are not showing up in tensorboard images.
- Enable batch size finder for distributed strategies HOT 1
- Support wandb_logger.watch() when using LightningCLI
- The packages such as libraries and models are not loading from files
- Please make it simple!
- LOG issue HOT 1
- Multi-gpu training is much lower than single gpu (due to additional processes?)
- 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
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from lightning.