Comments (3)
@mughetto @benedekrozemberczki A simple option for flexible GPU support is to use Accelerate.
I played around with a quick implementation for chemicalx and it's very easy, only changed a few lines of code. I tested on CPU and single GPU and it works well so far. GPU speeds up training and gets high utilisation, e.g the deepddi example is so much faster with GPU enabled 🏎.
If you're interested in this approach I can make an initial PR?
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@GavEdwards Sounds great! Right know, we really struggle with speed for the experiments. Can you make sure that the molecule and fingerprint based models both work on GPU?
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draft PR: #76
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Related Issues (20)
- KeyError: 'node_feature' from running deepdds_example.py HOT 7
- How are the methods implemented outside of the domain they are designed for? HOT 1
- Incorporate various dataset splits HOT 1
- Inconsistent labels in DrugCombDB
- Is this repo dead? Improve communication of its status HOT 2
- Performance bug of deepsynergy, deepdds and matchmaker on DrugBankDDI HOT 1
- How to retrieve the drug name?
- Data resolver tests HOT 2
- Add the AUDNNSynergy model HOT 1
- Add the TWOSIDES dataset with molecular features HOT 1
- Add the DrugComb and DrugCombDB data cleaning HOT 2
- Make dataset loaders on-the-fly
- DeepDDS Softmax bug
- BatchGenerator move back to name space HOT 2
- ChemicalX Tutorials are not working HOT 4
- 'LabeledTriples' object is not iterable HOT 2
- Pipeline - Unexpected Labels
- Tensor's device mismatch HOT 3
- Typo within documentation
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