Skip to main content

Distributed Convolutional Dictionary Learning

Project description

Build Status codecov

This package is still under development. If you have any trouble running this code, please open an issue on GitHub.

DiCoDiLe

Package to run the experiments for the preprint paper Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals.

Installation

All the tests should work with python >=3.6. This package depends on the python library numpy, matplotlib, scipy, mpi4py, joblib. The package can be installed with the following command run from the root of the package.

pip install  -e .

Or using the conda environment:

conda env create -f dicodile_env.yml

To build the doc use:

pip install  -e .[doc]
cd docs
make html

To run the tests:

pip install  -e .[test]
pytest .

Usage

All experiments are with mpi4py and will try to spawned workers depending on the parameters set in the experiments. If you need to use an hostfile to configure indicate to MPI where to spawn the new workers, you can set the environment variable MPI_HOSTFILE=/path/to/the/hostfile and it will be automatically detected in all the experiments. Note that for each experiments you should provide enough workers to allow the script to run.

All figures can be generated using scripts in benchmarks. Each script will generate and save the data to reproduce the figure. The figure can then be plotted by re-running the same script with the argument --plot. The figures are saved in pdf in the benchmarks_results folder. The computation are cached with joblib to be robust to failures.

Alternatively, you can also restrict the used interface by setting environment variables OMPI_MCA_btl_tcp_if_include or OMPI_MCA_btl_tcp_if_exclude

$ export OMPI_MCA_btl_tcp_if_include="wlp2s0"

$ export OMPI_MCA_btl_tcp_if_exclude="docker0"``

BSD 3-Clause License

Copyright (c) 2019-2021, the DiCoDiLe developers. All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

  1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

  2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

  3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dicodile-0.3.tar.gz (66.1 kB view hashes)

Uploaded Source

Built Distribution

dicodile-0.3-py3-none-any.whl (60.0 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page