*Issue #, if available:* N/A
*Description of changes:* This PR ensures that predictions are returned
in FP32 and on the CPU device. This choice is now better because we have
two types of models which have different types of forecasts (samples vs.
quantiles). Furthermore, `int64` input_type (our README example is one
such case) ran into issues with `predict_quantiles` before. This choice
also fixes that.
By submitting this pull request, I confirm that you can use, modify,
copy, and redistribute this contribution, under the terms of your
choice.
---------
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.
By submitting this pull request, I confirm that you can use, modify,
copy, and redistribute this contribution, under the terms of your
choice.
---------
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>
