DataParallel is used by auto_model with single GPU
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Description
🐛 Bug description
I am not sure whether it is a bug or a feature:
The DataParallel is being applied/patched by idist.auto_model in the context of a single gpu (backend=None, nproc_per_node=1). What is the reason behind this choice? Does it bring any speed improvements?
The only way to prevent it is to set os.environ["CUDA_VISIBLE_DEVICES"] = "0" for single-gpu contexts.
Environment
- PyTorch Version (e.g., 1.4): 1.7.1
- Ignite Version (e.g., 0.3.0): 0.4.8
- OS (e.g., Linux): Linux
- How you installed Ignite (
conda,pip, source): pip - Python version: 3.8
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 at idist.auto_model and trace how DataParallel is applied when backend=None and nproc_per_node=1. Compare that path with the CUDA_VISIBLE_DEVICES workaround and determine whether single-GPU wrapping is intentional; done means the behavior is clarified and the issue's expected outcome is recorded in an appropriate test or documentation update.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- distributed-systems, machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100