Comments (2)
For me, I was able to make it work using pip install disent --user
to install disent globally and then install PyTorch in a distinct virtual environment, which appears to allow disent to use all of its dependencies while allowing me to customize the version of PyTorch I'm using for my project. I realize this is probably not strictly kosher, but it does the trick.
I was able to run all of the import statements from https://disent.dontpanic.sh/en/latest/#metrics, except for from disent.frameworks.vae import BetaVae
, which results in some module import errors.
That's okay for my purposes, since I'm going to just use the metrics functions for my project.
from disent.
Thank you for reporting this, glad you found a solution.
I unfortunately wont re-open this issue as this should have been fixed in the development versions. I will however be working on trimming down the dependencies, and removing unnecessary parts of the project.
from disent.
Related Issues (20)
- Windows, Macos & GPU Tests
- Standardise Loss Reduction Modes HOT 1
- Adopt a Lightning-Flash Style API for Frameworks HOT 1
- [Q]: What's the native way to split datasets into train, validation and test? HOT 4
- [Q]: Command to run with defaults? HOT 4
- [FEATURE]: Experiment Config Override Support & Custom Code Registry HOT 3
- [FEATURE]: Allow Metrics To Directly Accept Frameworks & Datasets HOT 1
- [DOCS]: Add Experiment Examples HOT 1
- [BUG]: Investigate Schedule Interaction With Validation & Test Data
- [FEATURE]: Model Saving and Checkpointing HOT 11
- [FEATURE]: Add to Registry from Configs HOT 1
- [FEATURE]: Simplifying your configs using hydra-zen HOT 2
- [BUG]: Automatic downloads not working for MPI3D and dSprites HOT 3
- Averaging in AdaGVAE HOT 2
- [FEATURE]: Add DMS metric HOT 5
- [FEATURE]: Add IRS Metric HOT 2
- [FEATURE]: wandb model checkpoint support
- visualisation with trained model HOT 3
- [BUG]: Frameworks not loading as LightningModule when using trainer.fit
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