Dependency Isolation Best Practices
The following solutions are recommended for Python environment management pain points:
- Forced Version Lock::
1. Utilizationconda create -n openr1 python=3.11Creating exclusive environments
2. Must be installed in the order documented: vLLM → PyTorch 2.5.1 → Project Dependencies (pip install -e .[dev])
3. Prohibit mixing pip/conda to install the same dependency - conflict detection::
(of a computer) runpip checkVerify dependency tree integrity in case of conflicts:- Logging Conflict Pack Versions
- manually operated
pip uninstallconflict version - expense or outlay
--force-reinstallSpecify the correct version
- Containerization Solutions::
Dfile is available for advanced users:FROM nvidia/cuda:12.1-base
RUN apt-get update && apt-get install -y git-lfs
COPY requirements.txt /tmp/
RUN pip install -r /tmp/requirements.txt
When encountering CUDA-related errors, it is recommended to reinstall the corresponding version of the NVCC compiler.
This answer comes from the articleOpen R1: Hugging Face Replicates the Training Process of DeepSeek-R1The
May not be reproduced without permission:AI productivity tools " How to avoid Open R1 dependency conflicts in a local development environment?
