Release v0.10🦙LLMs coming · WenjieDu/PyPOTS

We make the following main updates in this new release:

added Time-LLM and GPT4TS;
enabled users to customize their training loss and evaluation metric for models;
fixed an argument-order error in CRPS loss calculation;
fixed a bug that data and model not on the same device when applying a list of CUDA devices;

Kudos to our new contributors @c-lyu and @giacomoguiduzzi 👍!

What's Changed

Fix CRPS loss calculation by @c-lyu in #565
Fix wrong order of arguments when calling calc_quantile_loss by @WenjieDu in #566
Enable to customized loss and val funcs by @WenjieDu in #526
Implement TimeLLM as an imputation model by @WenjieDu in #567
Make pytest ignore LLM-based testing cases by @WenjieDu in #569
Enable customizing training loss and val metric, add Time-LLM by @WenjieDu in #570
Refactor deprecated torch.cuda.amp.autocast by @WenjieDu in #521
Expose more models for tuning, bump dependency PyGrinder version num, and overwrite torch.autocast by @WenjieDu in #572
Refactor CSAI imputation & classification by @LinglongQian in #552
Update docs by @WenjieDu in #573
Update CSAI, refactor code and update docs by @WenjieDu in #574
Fix a bug that data and model are not on the same device when CUDA device list is applied by @giacomoguiduzzi in #563
Update the docs by @WenjieDu in #576
Fix potential bug that data and model not on the same cuda device, update docs by @WenjieDu in #577
Implement GPT4TS for time series imputation by @WenjieDu in #579
Update docs by @WenjieDu in #580
Including GPT4TS and update docs by @WenjieDu in #581
Update Docs by @WenjieDu in #582
Update docs and release v0.10 by @WenjieDu in #583

Full Changelog: v0.9...v0.10