Comments (2)
Ah, this is already possible, I missed that! I will simply add the full option as well
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We should consider switching to conda for venv configuration (using the YAML-based environment specification: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#sharing-an-environment).
Installing libraries like tensorflow via conda has considerable advantages. In particular, the conda-based installation will install CUDA and CuDNN in versions that match the tf installation, whereas the pip-based installation requires that the matching installations be provided externally, which is extremely painful.
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Related Issues (20)
- Notebook Test Failure: PyTorch-Lightning ImportError HOT 2
- Rule-Based Models' fit interface
- Notebook test failure due to TypeError in nbconvert HOT 1
- Simplify/improve naming of evaluator/evaluation utility classes HOT 1
- Add tests for evaluation util classes
- Add tests for regression models HOT 1
- Add backwards compatibility tests from v0 to v1 HOT 1
- Update notebooks to v1 and re-enable them for the docs build
- Parallelize ModelEvaluation.compare_models HOT 1
- Expose default eval metrics to user
- More explicit log and progress bar in ModelEvaluation.compare_models HOT 1
- Add to_feature_generator to ColumnGenerator HOT 1
- "Vectorize" ColumnGeneratorCachedByIndex HOT 2
- Add InMemoryPersistentKeyValueCache HOT 2
- Support pandas 2 HOT 1
- support test_io_data_dict in MultiDataModelEvaluation
- FeatureGenerators: Support normalisation alongside categorical features that are not encoded
- cloudpickle dependency
- "There are 14 competing standards"
- Add support for MPS backend HOT 3
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from sensai.