Comments (1)
#65 uses a hash store when training without using the launch script, so this issue should be resolved in Jupyter
For the launch script, it is possible to auto-select ports; however, there will be a race condition between when the launch script auto-selects a free port and when the training script calls torch.distributed.initialize_process_group
. However, we will add this in with a warning.
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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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