Comments (1)
Been thinking about this one a lot lately - a few open questions I see:
-
In terms of implementation, does an auto-tuner have to be a wrapper around the composer library? In the event the call to
train_model
fails, I imagine we would have to pretty much just reinitialize everything in order to try again. -
What if the model's memory usage changes significantly over time? Some reasons this could happen include batch skips due to bad loss scaling, algorithms that increase the effective model size over time, and even checkpointing.
-
Auto-tuning will presumably take some time, and it would be ideal to not have to repeat this process every time a new run is launched. Is there a way we can persist discovered
grad_accum
settings across runs with our current infrastructure?
from composer.
Related Issues (20)
- 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
- CUDA OOM error not caught with auto microbatching HOT 3
- Computing train metrics at a given frequency HOT 1
- Support DDP with rank-dependent dataloader lengths HOT 2
- Training stops after first pass of Evaluation when pretraining MosaicBert HOT 8
- EVAL_STANDALONE_END documentation missing HOT 1
- Epoch length incorrectly calculated when using DDP HOT 5
- Resuming Training from Load Path HOT 1
- ONNX export with `dynamic_axes` does not work when applying `BlurPool` HOT 1
- Save interval not working HOT 9
- FSDP Wrapping Alters Optimizer's Parameter Tracking Behavior HOT 8
- How to use composer under python3.8 HOT 1
- Multi-TPU Training Support for Composer HOT 4
- Eval Loss HOT 1
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from composer.