meta-pytorch/monarch
PyTorch Single Controller
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
1 release in the last 90 days, the latest 22 days ago, with 96 contributors on GitHub.
Gained 56% fewer stars in the last 90 days than in the 90 days before.
Use Cases & Benefits
- Provides a distributed programming framework for PyTorch using scalable actor messaging and remote actors grouped into meshes.
- Enables fault tolerance with supervision trees and efficient GPU/CPU memory transfers via RDMA for high-performance distributed tensor operations.
- Use for building scalable, fault-tolerant distributed machine learning training pipelines with PyTorch actors across multiple GPUs.
- Use for implementing fine-grained error recovery in distributed systems by leveraging supervision trees to manage actor failures.
- Use for optimizing data transfers in GPU-accelerated applications through one-sided RDMA memory operations to reduce communication overhead.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
BSD 3-Clause
Permissive: commercial use, modification and redistribution are allowed with attribution.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
Monarch is a distributed programming framework for PyTorch based on scalable actor messaging. It provides:
Monarch code imperatively describes how to create processes and actors using a simple python API:
The introduction to monarch concepts provides an introduction to using these features.
⚠️ Early Development Warning Monarch is currently in an experimental stage. You should expect bugs, incomplete features, and APIs that may change in future versions. The project welcomes bugfixes, but to make sure things are well coordinated you should discuss any significant change before starting the work. It's recommended that you signal your intention to contribute in the issue tracker, either by filing a new issue or by claiming an existing one.
View Monarch's hosted documentation at this link.
Note for running distributed tensors and RDMA, the local torch version must match the version that monarch was built with. Stable and nightly distributions require libmxl and libibverbs (runtime).
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
179+12
Open issues
311-176
Compared with peers in Backend
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| anthropics/skills | 187.8K | +2.9K | 15 | — |
| DuarteSantos8/openGym | 19.7K | +2.4K | 60 | 2 days ago |
| shiyu-coder/Kronos | 47.0K | +2.3K | 16 | — |
| NationalSecurityAgency/ghidra | 98.2K | +2.3K | 64 | 14 days ago |
| microsoft/VibeVoice | 56.0K | +1.1K | 29 | — |
| meta-pytorch/monarch | 1.1K | — | 64 | 22 days ago |
| Category median | 56.0K | +2.3K | 29 | — |
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