Lightning-GPU installation

Standard installation

For the majority of cases,

Lightning-GPU can be installed by following our installation instructions at pennylane.ai/install.

Install Lightning-GPU from source

Note

The below contains instructions for installing Lightning-GPU *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.

To install Lightning-GPU from the package sources using the direct SDK path, Lightning-Qubit should be install before Lightning-GPU (compilation is not necessary):

git clone https://github.com/PennyLaneAI/pennylane-lightning.git
cd pennylane-lightning
pip install -r requirements.txt
pip install custatevec-cu12
PL_BACKEND="lightning_qubit" python scripts/configure_pyproject_toml.py
SKIP_COMPILATION=True pip install -e . --config-settings editable_mode=compat -vv

Then a CUQUANTUM_SDK environment variable can be set:

export CUQUANTUM_SDK=$(python -c "import site; print( f'{site.getsitepackages()[0]}/cuquantum')")

The Lightning-GPU can then be installed with pip:

PL_BACKEND="lightning_gpu" python scripts/configure_pyproject_toml.py
python -m pip install -e . --config-settings editable_mode=compat -vv

To simplify the build, we recommend using the containerized build process described in Docker support section.

Install Lightning-GPU with MPI

Note

Building Lightning-GPU with MPI also requires the NVIDIA cuQuantum SDK (currently supported version: custatevec-cu12), mpi4py and CUDA-aware MPI (Message Passing Interface). CUDA-aware MPI allows data exchange between GPU memory spaces of different nodes without the need for CPU-mediated transfers. Both the MPICH and OpenMPI libraries are supported, provided they are compiled with CUDA support. It is recommended to install the NVIDIA cuQuantum SDK and mpi4py Python package within pip or conda inside a virtual environment. Please consult the `cuQuantum SDK`_ , mpi4py, MPICH, or OpenMPI install guide for more information.

Before installing Lightning-GPU with MPI support using the direct SDK path, please ensure that:

Note

• Lightning-Qubit, CUDA-aware MPI and custatevec are installed.

• The environment variable CUQUANTUM_SDK is set properly.

• Add the path/to/libmpi.so to LD_LIBRARY_PATH.

Then Lightning-GPU with MPI support can then be installed in the editable mode:

PL_BACKEND="lightning_gpu" python scripts/configure_pyproject_toml.py
CMAKE_ARGS="-DENABLE_MPI=ON" python -m pip install -e . --config-settings editable_mode=compat -vv

Test Lightning-GPU with MPI

You may test the Python layer of the MPI enabled plugin as follows:

mpirun -np 2 python -m pytest mpitests --tb=short

The C++ code is tested with

rm -rf ./BuildTests
cmake . -BBuildTests -DBUILD_TESTS=1 -DBUILD_TESTS=1 -DENABLE_MPI=ON -DCUQUANTUM_SDK=<path to sdk>
cmake --build ./BuildTests --verbose
cd ./BuildTests
for file in *runner_mpi ; do mpirun -np 2 ./BuildTests/$file ; done;

lightning_gpu/installation

 

Download Python script

 

Download Notebook

 

View on GitHub