Installation¶
The easiest way to install Elephant is by creating a conda environment, followed by pip install elephant.
Below is the explanation of how to proceed with these two steps.
Prerequisites¶
Elephant requires Python 3.8, 3.9, 3.10, 3.11 or 3.12.
Create your conda environment (e.g., elephant):
conda create --name elephant python=3.8 numpy scipy tqdm
Activate your environment:
conda activate elephant
Open a terminal and run:
sudo apt-get install python-pip python-numpy python-scipy python-pip python-six python-tqdm
Installation¶
The easiest way to install Elephant is via pip:
pip install elephant
If you want to use advanced features of Elephant, install the package with extras:
pip install elephant[extras]
To upgrade to a newer release use the --upgrade flag:
pip install --upgrade elephant
If you do not have permission to install software systemwide, you can
install into your user directory using the --user flag:
pip install --user elephant
If you have Git installed on your system, it is also possible to install the development version of Elephant.
Before installing the development version, you may need to uninstall the previously installed version of Elephant:
pip uninstall elephant
Clone the repository and install the local version:
git clone git://github.com/NeuralEnsemble/elephant.git cd elephant
pip install -e .
conda remove -n elephant --all # remove the previous environment
conda env create -f requirements/environment.yml
conda activate elephant
pip install -e .
MPI support¶
Some Elephant modules (ASSET, SPADE, etc.) are parallelized to run with MPI.
In order to make use of MPI parallelization, you need to install mpi4py
package:
conda install -c conda-forge mpi4py
sudo apt install -y libopenmpi-dev openmpi-bin
pip install mpi4py
To run a python script that supports MPI parallelization, run in a terminal:
mpiexec -n numprocs python -m mpi4py pyfile [arg] ...
For more information, refer to mpi4py documentation.
CUDA and OpenCL support¶
Analysis of Sequences of Synchronous EvenTs (ASSET) module supports CUDA and OpenCL. These are experimental features. You can have one, both, or none installed in your system.
To leverage CUDA acceleration on an NVIDIA GPU card, CUDA toolkit must installed on your system. Then run the following command in a terminal:
pip install pycuda
In case you experience issues installing PyCUDA, this guide offers a step-by-step installation manual.
If PyCUDA is detected and installed, CUDA backend is used by default in
Elephant ASSET module. To turn off CUDA support, set ELEPHANT_USE_CUDA
environment flag to 0.
If you have a laptop with a built-in Intel Graphics Card, you can still leverage significant performance optimization with OpenCL backend. The simplest way to install PyOpenCL is to run a conda command:
conda install -c conda-forge pyopencl intel-compute-runtime
However, if you have root (sudo) privileges, it’s recommended to install up-to-date Intel Graphics Compute Runtime system-wide and then install PyOpenCL as follows:
conda install -c conda-forge pyopencl ocl-icd-system
Set ELEPHANT_USE_OPENCL environment flag to 0 to turn off
PyOpenCL support.
Note
Make sure you’ve disabled GPU Hangcheck as described in the Intel GPU developers documentation. Do it with caution - using your graphics card to perform computations may make the system unresponsive until the compute program terminates.
Resolving compilation issues¶
Some modules in Elephant make use of C extensions to speed up computation. However, those extensions need to be compiled before use. In some cases, this causes problems. For example, the compiler on the current machine does not fulfill the requirements for the extension, certain libraries are missing, or no compiler is available at all.
In order to circumvent this problem, the following commands allow to avoid the compilation for specific or for all C extensions.
Use the following to install elephant without C extensions:
pip install elephant --install-option='--no-compile'
To avoid compilation of the c++ extension fim.cpp used in Spike Pattern Detection and Evaluation (SPADE), install the package with:
pip install elephant --install-option='--no-compile-spade'
In this case the pure python implementation of Spike Pattern Detection and Evaluation (SPADE) is still available.
Dependencies¶
Elephant relies on two special packages, installed by default:
quantities - support for physical quantities with units (mV, ms, etc.)
neo - electrophysiology data manipulations