A PySpark implementation of the Blue Brain Project Functionalizer
Project description
Functionalizer
Functionalizer is a tool for filtering the output of a touch detector (the “touches”) according to morphological models, given in in the form of recipe prescription as described in the SONATA extension.
To process the large quantities of data optimally, this software uses PySpark.
Installation
The easiest way to install functionalizer is via:
pip install functionalizerDue to a dependency on mpi4py, a MPI implementation needs to be installed on the system used. On Ubuntu, this can be achieved with:
apt-get install -y libopenmpi-devFor manual installation from sources via pip, a compiler handling C++17 will be necessary. Furthermore, all git submodules should be checked out:
gh repo clone BlueBrain/functionalizer -- --recursive --shallow-submodules
cd functionalizer
pip install .Spark and Hadoop should be installed and set up as runtime dependencies.
Usage
Basic usage follows the pattern:
functionalizer --s2f --circuit-config=circuit_config.json --recipe=recipe.json edges.h5
Where the final argument edges.h5 may also be a directory of Parquet files. When running on a cluster with multiple nodes, care should be taken that every rank occupies a whole node, Spark will then spread out across each node.
Acknowledgment
The development of this software was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.
Copyright (c) 2017-2024 Blue Brain Project/EPFL
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