fixed scheduler checkpoint loading, typo#63
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AnnihilatorChess wants to merge 51 commits intoPolymathicAI:masterfrom
Closed
fixed scheduler checkpoint loading, typo#63AnnihilatorChess wants to merge 51 commits intoPolymathicAI:masterfrom
AnnihilatorChess wants to merge 51 commits intoPolymathicAI:masterfrom
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- Add doc - Center badges
Add badges to README
Updated `shear_flow` results
* Add arXiv badge * Update link to arXiv paper
Califronia -> California
…eadme Fix shear_flow README.md
* List all the Well dataset in utils.py * Use the dataset list in download script * Order MHD datasets by dimension
Data: - Add Rayleigh Benard uniform dataset - Edit information about Shear Flow data Statistics and Metrics: - Add RMS statistics - Add Pearson correlation metrics Code Refactoring: - Refine video generation control - Refactor sample load from HDF5 - Add transformation and augmentation based on resizing and roation - Allow specifying for dataset split - Format with ruff
* Update citation after NeurIPS release * Update citation in docs too
* Add the Well dataset collection mention to HF card * Ignore streamlit local runs * Make uploaded dataset public by default * Add option to skip repacking HDF5 file * Increase CPU resources in the uploading script
* Factorize models with a BaseModel * Improve AFNO typing * Add tests for the different models * Do not pass dataset metadata to model * Remove unecessary arguments in super Co-authored-by: François Rozet <francois.rozet@outlook.com> --------- Co-authored-by: François Rozet <francois.rozet@outlook.com>
* Make models inherit from PytorchModelHubMixin * Rename upload -> upload_dataset * Add script draft for uploading models * Add config template to upload model * Add ReadMes for the 4 models to upload to HF * Specify data n_inputs in model upload config * Update FNO README * Complete model uploading script * Fix path issues in model uploading script * Improve model path and name retrieval * Change model path retrieval strategy * Change dataset -> model in upload folder method * Update README.md * Factorize models with a BaseModel * Add tests for the different models * Do not pass dataset metadata to model * Improve AFNO typing * Edit model path retrieval * Update links in FNO readme * Update FNO Readme * Add header to model READMEs * Add tables to model READMEs * Add code sample to load models to READMEs * Fix model instantiation * Simplify uploading script * Simplify uploading logic * Fix typo in spatial * Convert Omegaconf containers to be jsonable * Improve type checking enforcement Co-authored-by: Miles Cranmer <miles.cranmer@gmail.com> * Simplify model path Co-authored-by: Miles Cranmer <miles.cranmer@gmail.com> * Update datasetname variable in README code snippet * Apply suggested pathlib edits * Factorize model card generation * Remove duplicated header from model READMEs * Fix model card template name * Factorize further model README files * Fix dataset name in model card * Make model name variable in model card * Fix missing model name update * Fix typo in spatial resolution of UNetConvNext * Edit links in README with appropriate model names * Edit links in model README files --------- Co-authored-by: Ruben Ohana <50375255+rubenohana@users.noreply.github.com> Co-authored-by: Miles Cranmer <miles.cranmer@gmail.com>
* Change HF link to point to the Well collection * Document retrieval of checkpoints through HF
- Refactor DeltaWellDataset for time step differences - Refactor normalization - Fix AFNO and AViT models Co-authored-by: Payel Mukhopadhyay <payelmukhopadhyay180@gmail.com> Co-authored-by: Mike McCabe <mike.mccabe210@gmail.com>
* Increment version from 1.0.1 to 1.1.0 * Add list of maintainers * Add 3.13 to supported Python versions * Test max and min supported Python versions
* Add missing statistics * Remove try-except block causing silent failure * Add DeltaWellDataset to the list of data imports * Add dataset tests to check delta statistics * Round statistics to 4 decimal places * Fix argument in round function * Make compute statistics script parallel * Write stats with 4 decimal scientific notation * Edit yaml dumping for scientific notation * Factorize dataset download tests with fixtures * Reorganize dataset tests * Add comments to pytest fixtures * Simplify step selection * Raise error when stride and normalization are set
Co-authored-by: Payel Mukhopadhyay <payelmukhopadhyay180@gmail.com>
* Rewrite normalization tests Now only test the normalization class instead of the actual dataset stats. --------- Co-authored-by: Lucas Meyer <lucas.thibaut.meyer@gmail.com>
* added max rollout steps to dataset docstring * Update the_well/data/datasets.py Co-authored-by: Lucas Meyer <LTMeyer@users.noreply.github.com> --------- Co-authored-by: Lucas Meyer <LTMeyer@users.noreply.github.com>
* Add template for bug reports * Update already existing issue message * Add version and environment to issue template * Add code snippet to obtain version and environment * Fix typo in code snippet
…g_page Add missing symbolic link to rayleigh_benard_uniform
fix: stop overwriting `best.pt` every validation
fix: denominator calculation for short validation
Contributor
Author
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This is what the learning rate currently looks like when continuing a run with a lr_scheduler (continued at step 76) with the same settings. |
Contributor
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Thanks for the contribution @AnnihilatorChess . The change looks good at a quick glance, but I think it'll be a few days before someone can do a more detailed check. For now I'll trigger the workflow and make sure it doesn't break any of the tests. |
Collaborator
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@AnnihilatorChess This was accidentally closed during a restructuring of the repo. We would love to have your contribution, so once we are done with the restructuring and release, we will ping you for submitting a PR again. |
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Hello,
I found a bug where resuming a run from a checkpoint incorrectly restarts the LR scheduler's warmup and cosine decay. This is because the Trainer in training.py saves and loads the optimizer state but not the lr_scheduler state.
This PR fixes the save_model and load_checkpoint methods to include the lr_scheduler.state_dict() in the checkpoint, ensuring that training resumes with the correct learning rate.
(I also fixed a small typo: optimizer_state_dit -> optimizer_state_dict.)"