Create a benchmark to track speed improvements / regressions
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help wanted
- Dominant language
- Python
- Stars
- 8.3k
- Forks
- 871
- Avg merge
- 1d 13h
- Merged PRs (30d)
- 5
Description
Considering a lot of time goes into optimizing the speed of UMAP it seems necessary to have some kind of bechmark/suite to evaluate performance.
**EDIT:**
A collection of useful links to get started:
Tracking System Capabilities: https://github.com/giampaolo/psutil
Datasets: https://www.tensorflow.org/datasets
Benchmarking: https://pypi.org/project/pyperf/
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
The issue names no repository files, tests, or entry points. Start by locating UMAP's performance-sensitive paths and reviewing the linked psutil, TensorFlow Datasets, and pyperf resources; done means a reproducible benchmark suite exists that can reveal speed improvements and regressions.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100