sillsdev / sillsdev/machine.py
Make running NMT jobs locally more straightforward
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- Dominant language
- Python
- Stars
- 12
- Forks
- 3
- Avg merge
- 2d 3h
- Merged PRs (30d)
- 5
Description
A simple way to do this would be to:
- Make a flag that can be passed through to the specify the device (so you can use the CPU).
- Add a flag and alternative translation pipeline that just echos back the source segments.
- Use the NLLB tiny random model.
- Specify a configuration using these flags/this model in the launch.json.
Contributor guide
No contributing guide indexed for this repository
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
Review launch.json and the existing local NMT configuration first. Trace how device selection, translation pipelines, and model configuration are currently exposed, then add the requested CPU option, source-echo pipeline, and NLLB tiny random model configuration; done means the launch configuration can run an NMT job locally without specialized hardware.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 42/100