mlcube using singularity run --nv
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- Dominant language
- Python
- Stars
- 160
- Forks
- 31
- PR merge metrics
- No merged PRs in 30d
Description
Hi. I'm trying to run the MNIST example on an Nvidia V100, so the singularity run command needs the --nv argument to pull in CUDA and other Nvidia stuff. How do I do that with mlcube? Currently when I run with the singularity runner, it complains and just uses the CPU (although the execution is correct). Thanks.
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 with the Singularity runner used by the MNIST example and trace how its run command is assembled. Confirm that GPU execution passes Singularity's --nv option, then verify the MNIST example uses the V100/CUDA path instead of the CPU path.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100