Lightning-Tensor installation¶
Standard installation¶
For the majority of cases, Lightning-Tensor can be installed by following our installation instructions at pennylane.ai/install.
Install Lightning-Tensor from source¶
Lightning-Tensor requires CUDA 12 and the cuQuantum SDK (only the cutensornet library is required).
The SDK may be installed within the Python environment site-packages
directory using pip
or conda
or the SDK library path appended to the LD_LIBRARY_PATH
environment variable.
Please see the cuQuantum SDK install guide for more information.
Note
The section below contains instructions for installing Lightning-Tensor from source. For most cases, this is not required and one can simply use the installation instructions at pennylane.ai/install. If those instructions do not work for you, or you have a more complex build environment that requires building from source, then consider reading on.
Lightning-Qubit needs to be ‘installed’ by pip
before Lightning-Tensor (compilation is not necessary):
git clone https://github.com/PennyLaneAI/pennylane-lightning.git
cd pennylane-lightning
pip install -r requirements.txt
pip install cutensornet-cu12
pip install git+https://github.com/PennyLaneAI/pennylane.git@master
PL_BACKEND="lightning_qubit" python scripts/configure_pyproject_toml.py
SKIP_COMPILATION=True pip install -e . --config-settings editable_mode=compat
Note that cutensornet-cu12 is a requirement for Lightning-Tensor, and is installed by pip
separately. After cutensornet-cu12 is installed, the CUQUANTUM_SDK
environment variable should be set to enable discovery during installation:
export CUQUANTUM_SDK=$(python -c "import site; print( f'{site.getsitepackages()[0]}/cuquantum')")
The Lightning-Tensor can then be installed with pip
:
PL_BACKEND="lightning_tensor" python scripts/configure_pyproject_toml.py
CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=XX" pip install -e . --config-settings editable_mode=compat -vv
Where XX
is the architecture of the GPU you are using.
For example, if you are using an A100 GPU, you should use CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=80"
.
For more options, check the compute capabilities of your GPU in the CUDA documentation.
Lightning-Tensor also requires additional NVIDIA libraries including nvJitLink
, cuSOLVER
, cuSPARSE
, cuBLAS
, and CUDA runtime
. These can be installed through the CUDA Toolkit or from pip
.
Please refer to the plugin documentation as well as to the PennyLane documentation for further reference.