Comments (3)
lukas-schwab
commented on June 2, 2024
1
I believe this is a bug for other models as well. I'm running a TextRepresentation + DistMult interaction model and despite having 80G of VRAM PyKEEN still tries to allocate 14.90G more than I have. Conincidentally that's OOM by exactly the same margin as in your example.
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mberr
commented on June 2, 2024
Hi @bolak92 ,
could you try whether #1261 has solved your issue? It's not yet in a release but you can use it by installing from source
pip install git+https://github.com/pykeen/pykeen.gitfrom pykeen.
ddofer
commented on June 2, 2024
I can confirm that I get this same issue on the latest version, when using apple silicon/"mps" device.
i.e consistently crashes on evaluation due to OOM when using "mps" (Macbook Pro M3).
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Related Issues (20)
- Possible issue with model evaluation when using datasets with inverse triples HOT 1
- RGCN RuntimeError: trying to backward through graph a second time. (has parameters but no reset_parameters) HOT 2
- QuatE: GPU memory is not released per epoch HOT 3
- Training loop does not update relation representations when continuing training HOT 2
- from pykeen.pipeline import pipeline, pipeline issue HOT 3
- Evaluating metrics on many subsets with multiple models HOT 2
- Shape Mismatch upon initializing pretrained ComplEx embeddings HOT 2
- Importing model_resolver HOT 2
- Getting Embeddings of the Entity and Relations HOT 13
- RGCN Hyper parameter optimization error HOT 1
- MatKG HOT 1
- HPO_Pipeline fails on AutoSF models HOT 1
- Unable to reproduce TransE experiment
- EarlyStopper: show progress bar
- Cosine Annealing with Warm Restart LR Scheduler recieving an unexpected kwarg `T_i` HOT 1
- OOM Crash on MPS/Apple silicon HOT 2
- Reason for omitting validation inference triples from filtering when doing test evaluation in inductive lp example HOT 2
- tqdm progressbar is still shown although setting `use_tqdm=False`
- create_inverse_triples=True fails for the ILPC datasets
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