A Python Library for Deep Graph Networks (DGNs)

A Python Library for Deep Graph Networks (DGNs)

This is a Python library to easily experiment with Deep Graph Networks (DGNs). It provides automatic management of data splitting, loading and the most common experimental settings. It also handles both model selection and risk assessment procedures, by trying many different configurations in parallel (CPU). This repository is built upon the Pytorch Geometric Library, which provides support for data management.

PyDGN

Wiki

Description

This is a Python library to easily experiment with Deep Graph Networks (DGNs). It provides automatic management of data splitting, loading and the most common experimental settings. It also handles both model selection and risk assessment procedures, by trying many different configurations in parallel (CPU). This repository is built upon the Pytorch Geometric Library, which provides support for data management.

If you happen to use or modify this code, please remember to cite our tutorial paper:

Bacciu Davide, Errica Federico, Micheli Alessio, Podda Marco: A Gentle Introduction to Deep Learning for Graphs, Neural Networks, 2020. DOI: 10.1016/j.neunet.2020.06.006.

If you are interested in a rigorous evaluation of Deep Graph Networks, check this out:

Errica Federico, Podda Marco, Bacciu Davide, Micheli Alessio: A Fair Comparison of Graph Neural Networks for Graph Classification. Proceedings of the 8th International Conference on Learning Representations (ICLR 2020). Code

New features

  • Support to multiprocessing in GPU is now provided via Ray (see v0.4.0)!

Installation:

(We assume git and Miniconda/Anaconda are installed)

First, make sure gcc 5.2.0 is installed: conda install -c anaconda libgcc=5.2.0. Then, echo $LD_LIBRARY_PATH should always contain :/home/[your user name]/miniconda3/lib. Then run from your terminal the following command:

source install.sh [<your_cuda_version>]

Where <your_cuda_version> is an optional argument that can be either cpu, cu92, cu101, cu102 or cu110 for Pytorch 1.7.0. If you do not provide a cuda version, the script will default to cpu. The script will create a virtual environment named pydgn, with all the required packages needed to run our code. Important: do NOT run this command using bash instead of source!

Remember that PyTorch MacOS Binaries dont support CUDA, install from source if CUDA is needed

Usage:

Preprocess your dataset (see also Wiki)

python build_dataset.py --config-file [your data config file]

Launch an experiment in debug mode (see also Wiki)

python launch_experiment.py --config-file [your exp. config file] --splits-folder [the splits MAIN folder] --data-splits [the splits file] --data-root [root folder of your data] --dataset-name [name of the dataset] --dataset-class [class that handles the dataset] --max-cpus [max cpu parallelism] --max-gpus [max gpu parallelism] --gpus-per-task [how many gpus to allocate for each job] --final-training-runs [how many final runs when evaluating on test. Results are averaged] --result-folder [folder where to store results]

To debug your code it is useful to add --debug to the command above. Notice, however, that the CLI will not work as expected here, as code will be executed sequentially. After debugging, if you need sequential execution, you can use --max-cpus 1 --max-gpus 1 --gpus-per-task [0/1] without the --debug option.

Credits:

This is a joint project with Marco Podda (Github/Homepage), whom I thank for his relentless dedication.

Many thanks to Antonio Carta (Github/Homepage) for incorporating the Ray library (see v0.4.0) into PyDGN! This will be of tremendous help.

Contributing

This research software is provided as-is. We are working on this library in our spare time.

If you find a bug, please open an issue to report it, and we will do our best to solve it. For generic/technical questions, please email us rather than opening an issue.

License:

PyDGN is GPL 3.0 licensed, as written in the LICENSE file.

Troubleshooting

If you get errors like /lib64/libstdc++.so.6: version GLIBCXX_3.4.21' not found`:

  • make sure gcc 5.2.0 is installed: conda install -c anaconda libgcc=5.2.0
  • echo $LD_LIBRARY_PATH should contain :/home/[your user name]/[your anaconda or miniconda folder name]/lib
  • after checking the above points, you can reinstall everything with pip using the --no-cache-dir option

Download Details:

Author: diningphil Download Link: Download The Source Code Official Website: https://github.com/diningphil/PyDGN

python deep-learning data-science developer

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

Data Science With Python Training | Python Data Science Course | Intellipaat

🔵 Intellipaat Data Science with Python course: https://intellipaat.com/python-for-data-science-training/In this Data Science With Python Training video, you...

Top Deep Learning Development Services | Hire Deep Learning Developer

Inexture's Deep learning Development Services helps companies to develop Data driven products and solutions. Hire our deep learning developers today to build application that learn and adapt with time.

PyTorch for Deep Learning | Data Science | Machine Learning | Python

PyTorch for Deep Learning | Data Science | Machine Learning | Python. PyTorch is a library in Python which provides tools to build deep learning models. What python does for programming PyTorch does for deep learning. Python is a very flexible language for programming and just like python, the PyTorch library provides flexible tools for deep learning.

Basic Data Types in Python | Python Web Development For Beginners

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

How I'd Learn Data Science If I Were To Start All Over Again

A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start?