NVIDIA-BioNeMo / NVIDIA-BioNeMo/Proteina-Complexa
Fork adapted to Mac users, now available
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 432
- Forks
- 78
- Avg merge
- 10d 46m
- Merged PRs (30d)
- 1
Description
Hello,
Proteina-Complexa is really great, and it would be useful to adapt it to the Apple Silicon Platform for testing by people who do not have access to top NVIDIA hardware, such as students.
For information, I succeeded in creating a working fork for Apple Silicon (MPS/Metal). It is currently HERE.
It runs in a Python 3.13 conda environment with PyTorch 2.11, jax-mps, and RF3 and tmol forks that I also adapted to Apple Silicon (thanks to Codex and Claude code!).
I can make a PR, though I understand it is not of interest to NVIDIA.
Florian
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
Start by reviewing the linked Apple Silicon fork and its Python 3.13 conda environment, PyTorch 2.11, jax-mps, RF3, and tmol adaptations. Compare its changes with Proteina-Complexa, then validate the project on Apple Silicon; done means the supported workflow runs without requiring NVIDIA hardware.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning, operating-systems
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100