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Environment
PyQUDA requires python>=3.7
. You can check the version of your python3
by
python3 --version
We recommend using the virtual environment to install your Python packages. You can use venv
, virtualenv
or conda
to manage your virtual environments. We recommend using the Python standard library venv
instead of conda
because of some OpenMPI issues.
Using venv
python3 -m venv .venv
source .venv/bin/activate
Using conda
conda create .venv
conda activate .venv
.venv
can be any other name you like.
pip install cython numpy mpi4py
Install the prebuilt cupy
package with your CUDA Toolkit version. You can check the version by
nvcc --version
And then install one of the packages below depending on the version
pip install cupy-cuda110
pip install cupy-cuda111
pip install cupy-cuda11x
pip install cupy-cuda12x
Check https://docs.cupy.dev/en/stable/install.html#installing-cupy for more information.
Install cupy
from the source is possible but not recommended
pip install cupy
If you want to install cupy
on DCU clusters, modify the cupy
source code and then install it with DTK just like ROCm. Refer to https://docs.cupy.dev/en/stable/install.html#using-cupy-on-amd-gpu-experimental for detailed information.
Download the source code of CuPy version 12.3.0
wget https://github.com/cupy/cupy/releases/download/v12.3.0/cupy-12.3.0.tar.gz
tar -xzvf cupy-12.3.0.tar.gz
cd cupy-12.3.0
Apply the patch
diff --git a/install/cupy_builder/_features.py b/install/cupy_builder/_features.py
index d12de78c3..8c9ac830a 100644
--- a/install/cupy_builder/_features.py
+++ b/install/cupy_builder/_features.py
@@ -173,7 +173,7 @@ def get_features(ctx: Context) -> Dict[str, Feature]:
'hiprand',
'hipsparse',
'rocfft',
- 'roctx64',
+ #'roctx64',
'rocblas',
'rocsolver',
'rocsparse',
Build and install CuPy from source
export CUPY_INSTALL_USE_HIP=1
export ROCM_HOME=/path/to/dtk-23.10
pip install .
PyTorch (Optional)
torch
is an alternative to cupy
.
python3 -m pip install torch