Comments (1)
@mbway Thanks for flagging this -- we're currently actively working on a major redesign of checkpointing that will include migrating to a safer format. We've been hesitant to change how checkpointing works as we deeply care about backwards compatibility, so we have been buffering up a long list of features we will now include in the revamp. You should start seeing things roll out this month.
CC: @eracah it might be good to publicly share a version of the roadmap as a RFC when its ready
If you already have a PR, please feel free to post it -- it's probably helpful for us to view a design and gain some inspiration. Given the refactor though, we likely wouldn't accept it as this seems like a big change.
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Related Issues (20)
- 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
- Torch script export not working with hugging face model HOT 1
- Unable to script model HOT 5
- Documentation: Dead links to images stored in GCS buckets in "Analyzing Traces" Tutorial
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