Unified Neuromorphic Datasets Repository

• Unified Neuromorphic Datasets Repository
  • Getting Started
    • Install the undr module
    • Generate a default configuration file
    • Download the datasets
    • Generate a BibTex file
  • Python module
    • Python APIs
  • Dataset format specification
  • Dataset mirrors
    • Example configuration
      • Apache
      • Nginx
      • Upload a dataset
  • Contribute
    • Publish the module
    • Build the app
    • Download with existing CLI

Getting Started

Install the undr module

pip3 install undr

Generate a default configuration file

python3 -m undr init

The generated undr.toml file is written in TOML (https://github.com/toml-lang/toml). It lists the datasets that will be downloaded or streamed, hence it needs to be ajusted to your needs.

The line directory = 'datasets' specifies the directory where downloaded files are stored (relatively to the configuration file). All the files generated by undr (directory indexes, downloaded data, temporary files...) are stored in this directory.

Datasets are listed as [[datasets]] entries with three mandatory properties: name, url and mode. The optional server_type property is used internally to speed up the download process. To discard a dataset, you can either remove it from the configuration file or comment all its lines with # signs.

mode changes the download strategy on a per-dataset basis, with three possible values:

• 'remote' only downloads the dataset's file index. The undr Python package can be used to process the dataset files as if they were on your hard drive by streaming them from the server. This option is particularly useful for large datasets that do not fit on your disk but requires a fast internet connection since files are re-downloaded every time they are processed.
• 'local' downloads all the dataset files locally but does not decompress them (most datasets are stored as Brotli archives). The undr Python library transparently decompresses files in memory when you read them, making this option a good trade-off between disk usage and processing speed.
• 'local-decompressed' downloads all the dataset files locally and decompresses them. Decompressed files use a relatively inefficient plain binary file format so this option requires vast amounts of disk space (3 to 5 times as much as the Brotli archives). On the other hand, the plain binary format facilitates processing with other languages such as Matlab or C++.

undr also supports hybrid configurations where only part of a dataset is downloaded or decompressed. You may also use local directories without a server. See [NOT DOCUMENTED YET] for details.

Download the datasets

python3 -m undr install

This command downloads the datasets file indexes. If the mode is 'compressed' or 'decompress', it also downloads the dataset files (and possibly decompresses them).

This command can be interrupted at any time with CTRL + C. Re-running it will resume download where it left off.

Generate a BibTex file

python3 -m undr bibtex --output datasets.bib

The UNDR project does not claim authorship of the datasets. Please use this file to cite the origiinal articles.

Python module

pip3 install undr

Python APIs

All three approches support progress persistence. Progress persistence sightly increases the code complexity but makes it possible to resume processing after a network or power failure.

Dataset format specification

-index.json rationale

• prepend a special character to make sure the index file is ordered first in ASCII
• use an unreserved URL character to avoid escaping problems
• out of the unreserved URL characters (- / . / _ / ~, see https://www.rfc-editor.org/rfc/rfc3986#section-2):
  • . would result in a hidden file on UNIX systems
  • _ comes after alpha-numerical characters in ASCII
  • ~ is a shortcut for the user's home directory in many shells

Many command-line programs treat -index.json as a flag, hence a command such as cat -index.json returns an error. Prepending ./ to the filename avoids the problem: cat ./-index.json.

Dataset mirrors

Example configuration

Apache

<VirtualHost *:80>
    Alias / /path/to/local/directory/
    <Directory "/path/to/local/directory/">
        Require all granted
        Options +Indexes
    </Directory>
</VirtualHost>

To use another port, remember to edit /etc/apache2/ports.conf as well.

Nginx

server {
    listen 80;
    location / {
        alias /path/to/local/directory/;
        autoindex on;
        sendfile on;
        tcp_nopush on;
        sendfile_max_chunk 1m;
    }
}

Upload a dataset

• python3 -m undr check-conformity /path/to/dataset

• Caveat: An UNDR server can provide multiple compressed files (different formats) for each resource. The Python UNDR library always picks the best compression (smallest encoded size). check-conformity only checks the best compression and will not report errors for other compressions.

• for macOS users (.DS_Store)

Add to ~/.zshrc:

# rmdsstore removes .DS_Store files recursively
rmdsstore() {
    if [ $# -eq 0 ]; then
        printf 'usage: rmdsstore directory\n' >&2
        return
    fi
    find "$1" -name ".DS_Store" -delete -print
}

Run rmdsstore /path/to/dataset before running python3 -m undr check-conformity /path/to/dataset.

Contribute

cd python
black . # format the source code (see https://github.com/psf/black)
pyright . #check types (see https://github.com/microsoft/pyright)
python3 -m pip install -e . # local installation

Publish the module

1. Bump the version number in setup.py.

2. Install twine

pip3 install twine

3. Upload the source code to PyPI:

rm -rf dist
python3 setup.py sdist
python3 -m twine upload dist/*

Build the app

1. Copy the UNDR library to the app build tree

python3 app/interface-prebuild.py

2. Package the Python app using Cubuzoa

cd /path/to/cubuzoa
python3 cubuzoa.py build /path-to-undr/app/python --os linux --version '==3.8'
python3 cubuzoa.py build /path-to-undr/app/python --os 'macos|windows' --version '==3.9'

or build only for your platform

cd app/interface
mkdir local-build
cd local-build
pyinstaller --distpath ../build --add-data ../undr/-index_schema.json:undr --add-data ../undr/undr_default.toml:undr --add-data ../undr/undr_schema.json:undr -n interface-cp39-macosx -y ../interface.py

3. Delete the UNDR library copy

rm -rf app/python/undr

4. Build the Electron app

cd app
npm run release # or npm run watch for continuous development

Download with existing CLI

wget --no-parent --recursive --level=inf http://localhost:5432/dvs09/ find . -iname '*.br' | while read filename; do brotli -d -j "$filename"; done;