Linux operating system (support for Windows is planned, see https://github.com/brian-team/brian2cuda/issues/225)
NVIDIA CUDA GPU with compute capability 3.5 or larger
CUDA Toolkit with
Python version 3.6 or larger
Brian2: Each Brian2CUDA version is compatible with a specific Brian2 version. The correct Brian2 version is installed during the Brian2CUDA installation.
We recommend installing Brian2CUDA in a separate Python environment, either using a “virtual environment” or a “conda environment”. If you are unfamiliar with that, check out the Brian2 installation instructions.
To install Brian2CUDA with a compatible Brian2 version, use
python -m pip install brian2cuda
Use the install command together with the
python -m pip install --upgrade brian2cuda
This will also update the installed Brian2 version if required.
When you encounter a problem in BrianCUDA, we will sometimes ask you to install Brian2CUDA’s latest development version, which includes changes that were included after its last release.
We regularly upload the latest development version of Brian2CUDA to PyPI’s test server. You can install it via:
python -m pip install --upgrade --pre -i https://test.pypi.org/simple/ brian2cuda
Note that this requires that you already have a compatible Brian2 version and all of its dependencies installed.
If you have
git installed, you can also install directly from github:
python -m pip install git+https://github.com/brian-team/brian2cuda.git
If you want to either contribute to Brian’s development or regularly test its
latest development version, you can directly clone the git repository at github
(https://github.com/brian-team/brian2cuda) and then run
pip install -e
/path/to/brian2cuda, to install Brian2CUDA in “development mode”. As long as
the compatible Brian2 version doesn’t change, updating the git repository is in
general enough to keep up with changes in the code, i.e. it is not necessary to
install it again. If the compatible Brian2 versions changes though, you need to
manually update Brian2.
Brian2CUDA tries to automatically detect your CUDA toolkit installation and choose the newest GPU on your system to run simulations. To test if this detection and your installation were successful, you can run this test simulation:
import brian2cuda brian2cuda.example_run()
If the automatic CUDA and GPU detection fails or you want to manually change it, read Configuring the CUDA backend.
If you have the pytest testing utility installed, you can run Brian2CUDA’s test suite:
import brian2cuda brian2cuda.test()
This runs all standalone-comatible tests from the Brian2 test suite and additional Brian2CUDA tests (see the Brian2 developer documentation on testing for more details) and can take 1-2 hours, depending on your hardware. The test suite should end with “OK”, showing a number of skipped tests but no errors or failures. If you want to run individual tests instead of the entire test suite (e.g. during development), check out the Brian2CUDA tools directory.