Comments (3)
Is this in an interactive shell or jupyter?
We can add in usability checks to determine which parameters need to be adjusted for interactive environemnts (e.g. dataloaders should have num_workers=0, world size must be 1, ...)
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Closing as we haven't been seeing this...please re-open if it comes back.
from composer.
Closing as we haven't been seeing this...please re-open if it comes back.
from composer.
Related Issues (20)
- Set `torch.utils.data.DataLoader.batch_sampler` epoch if defined HOT 2
- Allow smaller `t_max` in schedulers HOT 8
- Crash during import when signal handler cannot be set
- Memory leak due to the copy of Metric objects in Composer's trainer HOT 2
- override serialized fields when resuming from checkpoint HOT 1
- Allow default credentials for accessing GCS HOT 4
- Remote file name in `MemorySnapshot` not being formatted HOT 1
- Augment training batches with "on-the-fly" features
- Safer Checkpoint File Format HOT 1
- Streaming Dataloader with Multiple Workers causes out-of-memory error
- LookSAM Optimizer HOT 3
- Remove logic that pops labels from batch in composer/models/huggingface.py
- Composer default lr scheduler create spurious warnings when max training duration is in epochs HOT 2
- Error when using `Trainer.compile_config={}` in DDP mode HOT 4
- Enable control over MLFlowLogger run_name str to match a pre-existing tag run_name in MLFlow and resume model training HOT 1
- TORCH_NCCL_ASYNC_ERROR_HANDLING vs NCCL_ASYNC_ERROR_HANDLING at launcher.py HOT 3
- NUMA affinity control HOT 2
- Optional `CheckpointSaver` instantiation inside the `Trainer` HOT 9
- TypeError: Subscripted generics cannot be used with class and instance checks HOT 2
- Autoresume and duration mismatch on reload HOT 12
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