PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series

In this new version, we

1. enable PyPOTS to run on multiple CUDA devices to train models in parallel. NOW you can further speed up your model training in PyPOTS 🥳;
2. add imputation model M-RNN;
3. add optimizer Adadelta;

• Use relative imports replace absolute ones by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/117
• Add optimizer Adadelta, and fix issue #116 by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/118
• Docs auto param type hints by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/119
• Use sphinx_autodoc_typehints to auto gene type hints for params, and improve docs by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/120
• Enable parallel training on multi GPUs by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/122
• skip multi-gpu test if not multi-gpu host by @MaciejSkrabski in https://github.com/WenjieDu/PyPOTS/pull/124
• Enable models to run on multiple CUDA devices by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/125
• Add model MRNN by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/126
• Add model M-RNN, and update the docs by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/127
• Fix VaDER failed testing and MRNN failed training on multiple devices by @WenjieDu in https://github.com/WenjieDu/PyPOTS/pull/128

Full Changelog: https://github.com/WenjieDu/PyPOTS/compare/v0.1.0...v0.1.1