Comments (4)
jdb78
commented on August 30, 2024
Are you running with the latest version of pytorch and pytorch-forecasting?
BTW: There is a consequence of the new metric system/bug (which is being fixed now in #112 ) that forces you to specify the loss you want to optimize (i.e. you have to use .from_dataset(loss=MASE()) when you create your network.
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andrewcztrack
commented on August 30, 2024
Hi @jdb78 !! i hope your well! thank you for your reply.
i was just running your tutorials from the notebook.
i tried to reinstall the library but the errors still comes up.
Best,
Andrew
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jdb78
commented on August 30, 2024
Have you tried with pytorch 1.6? Conda forge doesn’t not allow specifying higher versions than 1.4.
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andrewcztrack
commented on August 30, 2024
Done! and it is working!
Thank you :).
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Related Issues (20)
- Truncated Backpropagation Through Time
- LSTM model in docs is fake
- How to extrapolate historic time series data into future with DeepAR model?
- Unexpected results for time-varying target variables.
- Question on using TFT without past target values
- how can this lib support the large data?
- Feature name unseen at fit time
- Issue with using optimise_hyperparameter with PyTorch DDP -> please help! :) HOT 1
- [ENH] un-stale the package and give simple instructions for working install HOT 4
- Understanding model.predict, decoder, test_dataset in TFT
- [MNT] maintenance & handover items for integration with sktime org HOT 8
- [MNT] `numpy 2` compatibility HOT 1
- [MNT] `optuna>=3.3.0` compatibility
- [MNT] MPS backend test failures on MacOS HOT 1
- [BUG] `test_none_reduction` fails on main HOT 2
- [MNT] manage core dependencies prior to 1.1.0 release
- [ENH] defining boundary between `sktime` and `pytorch-forecasting`, support for foundation models HOT 1
- [MNT] make squash commits default
- [MNT] windows compatibility HOT 4
- [MNT] revert workaround in notebooks after 1614 is fixed
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