microsoft / nnscaler
nnScaler: Compiling DNN models for Parallel Training
Why this job: works in the areas we cover, but does not say which job it does
What it does
- Ease of Use. Only a few lines of code need to be changed to enable automated parallelization.
- Pythonic. The parallelization output is in PyTorch code, making it easy for users to understand and convenient for further development or customization.
- Extensibility. nnScaler exposes an API to support new operators for emerging models.
- Reliability. Verified through various end-to-end training sessions, nnScaler is a dependable system.
- Performance. By exploring a large parallelization space, nnScaler can significantly enhance parallel training performance.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 17 days ago
- Stars
- 136
- On GitHub
- Licence
- MIT
- Allows business use
- Downloads for
- Linux
- From the latest release
- Latest version
- 0.8
- Released 14 months ago
- Built with
- Python
- 99% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 61% closed
- 9 still open
- Pull requests
- 15 merged
- 4 waiting
- Releases
- 3
- 0 in the last year
- No contributing guide
- Security policy (opens in new tab)
- Code of conduct (opens in new tab)
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More: download files, languages, topics and project record
Languages
- Python 99%
- C++ 1%
Topics
- compiler
- deep-learning
- distributed-training
- llm
- machine-learning
- parallel-computing
Project record
- JobOther AI tools
- StatusListed
- Commits35
- Forks27
- Watching8
- Stars in 28 days0
- CreatedApr 7, 2024
- First seen by usNov 7, 2026
- CheckedNov 8, 2026