Environmental compatibility solutions
For non-Ubuntu systems (e.g. CentOS/Arch), the following special configuration is required:
- Dependency alternatives::
- Use the conda virtual environment instead of system Python:
conda create -n flashmla python=3.8 - pass (a bill or inspection etc)
conda install cuda -c nvidiaGetting a compatible CUDA version
- Use the conda virtual environment instead of system Python:
- Kernel module compilation::
- modifications
setup.pyhit the nail on the headextra_compile_argsAdd-D_LINUX_COMPATIBILITYmacro (computing) - Explicitly specify the computational power:
export TORCH_CUDA_ARCH_LIST=9.0
- modifications
Validation Methods
- Check the glibc version:
ldd --versionNeeds to be ≥ 2.31 - Testing basic functions: running
python -c "import flash_mla; print(flash_mla.test_basic())"
Options
If compatibility issues still occur, consider:
- Use Docker containers:
docker pull nvidia/cuda:12.6-base - Deploying Ubuntu Subsystems in a Windows Environment via WSL2
This answer comes from the articleFlashMLA: Optimizing the MLA Decoding Kernel for Hopper GPUs (DeepSeek Open Source Week Day 1)The
May not be reproduced without permission:AI productivity tools " How to install FlashMLA on non-Ubuntu systems and avoid compatibility issues?
