*Issue #, if available:*
*Description of changes:*
- Refactor the notebook to cover real-time inference (CPU & GPU),
serverless inference and batch prediction options for Chronos-2 on
SageMaker
- Update README
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*Description of changes:*
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---------
Co-authored-by: Oleksandr Shchur <oleks.shchur@gmail.com>
Removed logo image from the README.
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*Description of changes:*
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*Description of changes:*
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*Description of changes:* Update README for Chronos-2
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Co-authored-by: Oleksandr Shchur <oleks.shchur@gmail.com>
*Issue #, if available:*
*Description of changes:* This PR adds the Chronos-2 model.
* Chronos-2 modeling and pipeline code, including tests.
* Updated `pyproject.toml`. Merge `training` and `evaluation` extras
into a single `dev` extra. This stuff is only relevant for the Chronos
models.
* Added `predict_fev` to `BaseChronosPipeline`.
* Changes to `InstanceNorm` for Chronos-Bolt to make it general and
compatible with Chronos-2.
* Minor renaming and polishing in the inference code for Chronos and
Chronos-Bolt.
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Co-authored-by: Oleksandr Shchur <oleks.shchur@gmail.com>
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*Description of changes:*
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*Description of changes:*
- Add pointers to fev and JumpStart notebook in the README
- Add notebook describing how to deploy Chronos with SageMaker JumpStart
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*Issue #, if available:*
*Description of changes:* This PR uses absolute link to the images so
they show up correct on other places such as PyPi.
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*Issue #, if available:*
*Description of changes:* `predict` returns different things based on
model type. This fixes the example to use `predict_quantiles` which will
give correct quantiles.
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Co-authored-by: Abdul Fatir Ansari <ansarnd@amazon.de>
*Issue #, if available:*
*Description of changes:* MPS mostly causes issues for users, so let's
remove the reference to MPS from the README. Plus, Chronos-Bolt models
currently fail on MPS.
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Co-authored-by: Abdul Fatir Ansari <ansarnd@amazon.de>
*Description of changes:* This PR updates project information and
workflows to allow for PyPi release.
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---------
Co-authored-by: Abdul Fatir Ansari <ansarnd@amazon.de>
*Issue #, if available:* N/A
*Description of changes:* This PR adds support for Chronos-Bolt models.
TODOs:
- [x] Update evaluation script
- [x] Fix and add tests for Bolt
- [x] Update docstrings
- [x] Update README example and mention Chronos-Bolt
- [x] Update results bar plot in README
- [x] Add versions for libraries in `pyproject.toml`
- [x] Check that the training and eval scripts work
- [x] Change `autogluon` -> `amazon` in model names
Post Merge:
- [ ] Update Citation style in README, both Github and HuggingFace repos
- [ ] Remove note about AutoGluon
- [ ] Update READMEs of original Chronos models to refer to Chronos-Bolt
NOTE: To be merged after Chronos-Bolt models are available under the
`amazon` namespace on HF.
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Co-authored-by: Abdul Fatir Ansari <ansarnd@amazon.de>
Co-authored-by: Caner Turkmen <turkmen.ac@gmail.com>
Co-authored-by: Lorenzo Stella <stellalo@amazon.com>
*Issue #, if available:*
*Description of changes:* Update README.
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*Description of changes:* Title.
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*Description of changes:* This PR updates README.md with dataset and
evaluation details
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*Description of changes:* Updates the citation.
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*Description of changes:* Simplifies content in the "Usage" section, fix
a link.
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*Description of changes:* Adds usage examples for `scripts/`.
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Co-authored-by: Abdul Fatir Ansari <ansarnd@amazon.com>
*Description of changes:* This PR adds the script to generate synthetic
data from KernelSynth.
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Co-authored-by: Abdul Fatir Ansari <ansarnd@amazon.com>
*Description of changes:* Add training script and config files. Can be
used for pre-training, or adapted for fine-tuning chronos models.
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Co-authored-by: Abdul Fatir <Abdulfatirs@gmail.com>
*Issue #, if available:*
*Description of changes:*
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Co-authored-by: Abdul Fatir <Abdulfatirs@gmail.com>
*Description of changes:* This PR revamps the README.
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Co-authored-by: Abdul Fatir Ansari <ansarnd@amazon.com>
*Issue #, if available:* #28 (also, PR #41)
*Description of changes:* This PR updates the README with information on
MLX support.
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*Description of changes:* This PR adds `pipeline.embed` which extracts
encoder embeddings from the model. These embeddings may be useful for
some downstream tasks such as classification, so this is useful to have.
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Co-authored-by: Abdul Fatir Ansari <ansarnd@amazon.de>
*Description of changes:* This PR adds optional inference params such as
`num_samples`, `top_k`, etc. to the example in the README for clarity.
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