munich-quantum-toolkit / munich-quantum-toolkit/predictor
✨ Parallelization
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 87
- Forks
- 23
- Avg merge
- 6h 23m
- Merged PRs (30d)
- 35
Description
What's the problem this feature will solve?
Currently, the model training is only executed on a single CPU core. However, in general, stable_baselines3 and gymnasium support parallelization of the learning process. Apparently, masking makes it a bit more complex (see, eg.g., https://github.com/Stable-Baselines-Team/stable-baselines3-contrib/issues/49). Nevertheless, this would could lower the training time significantly and should be explored more.
Describe the solution you'd like
Implement a parallel training procedure.
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
No files or tests are named. Start by locating the current single-core model-training entry point, then read the referenced Stable-Baselines3 masking discussion and inspect how gymnasium environments are used. Done means a parallel training procedure that handles masking correctly and reduces training time.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100