GPU Selection & Device Specification
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- Dominant language
- Python
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Description
I would like to suggest a feature that would allow specifing which GPU or GPUs to run on directly within the Ollama Python library.
This feature is crucial in shared server environments across multiple GPUs and multiple users, as it allows each Jupyter notebook to run on the corresponding GPU without conflicts. Currently, specifying GPU usage in Ollama is somewhat complex. A streamlined method to assign tasks to specific GPUs directly inside the Python program would prevent conflicts and optimize workflow. Implementing this feature would significantly improve usability and align Ollama with other machine-learning frameworks.
Thank you for considering this suggestion. I would be happy to discuss further details if needed.
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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 Ollama Python library API and how GPU usage is currently specified in Ollama. Define the Python-facing behavior needed to select one or more GPUs from a Jupyter notebook, with completion demonstrated by independent notebook tasks using their assigned GPUs without conflicts.
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Assessment
- Tech stack
- ollama, python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 38/100