Multi GPU support for vision model training in C++ with libtorch
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- Python
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Description
🚀 Feature
Multi GPU support for vision model training in C++ with libtorch.
Motivation
More and more systems rely only on C++ for deep learning, including ours. But at the moment the C++ version of vision models are quite unpractical to use, and it is not possible to train them on multiple GPU when using large amount of data.
Pitch
Make C++ models support multi GPU. This can be done by deriving the pytorch ICloneable class, but this might not be sufficient for optimal GPU training, as pointed out in this issue. The best option might be to implement the clone method so that data_parallel (here) can handle the models.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the C++ vision model interfaces and the referenced torch/csrc/api/include/torch/nn/parallel/data_parallel.h entry point, along with the linked PyTorch issue about ICloneable. Determine how clone and data_parallel are expected to interact for these models. Done means C++ vision models can train across multiple GPUs with large datasets.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp, pytorch
- Domain
- computer-vision, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100