Fix: avoid automatic device detection via serialized tensors when deserializing PyTorchPredictor.
#2576
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lostella
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lostella
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* Fix: avoid automatic device detection via serialized tensors when deserializing. (#2576) * Make itertools Map/Filter dataclasses. (#2579) * serde: Fix encoding of dtypes. (#2586) * Add assertion to split function ensuring valid windows (#2587) * Ensure dtype on feat_time in torch DeepAR. (#2596) * Expose aggregation method in ensemble NBEATS, fix forecast shape (#2598) * Fix version in requirements to comply with stricter setuptools. (#2604) Co-authored-by: Lorenzo Stella <[email protected]> * Add `gluonts.util.safe_extract` (#2606) Co-authored-by: Jasper <[email protected]> Co-authored-by: Lorenzo Stella <[email protected]> * fix requirements further * fix style * remove undesired change --------- Co-authored-by: Shubham Kapoor <[email protected]> Co-authored-by: Jasper <[email protected]> Co-authored-by: MarcelK1102 <[email protected]> Co-authored-by: Lorenzo Stella <[email protected]> Co-authored-by: Jasper <[email protected]>
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Issue #, if available:
Description of changes:
When
map_location=Noneis passed totorch.load, it automatically loads the tensors to the device they were serialized on. It results in runtime errors when the model is trained using a GPU and serialized, but inference is done on CPU when predictor is constructed bydeserializemethod of PytorchPredictor withdevice=None.By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.
Please tag this pr with at least one of these labels to make our release process faster: BREAKING, new feature, bug fix, other change, dev setup