CogDL

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CogDL is an open-source AI Frameworks & Infra tool that provides a comprehensive library for graph deep learning. It allows researchers and developers to easily train and compare baseline or customized models for various graph tasks.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is CogDL?

CogDL is a comprehensive, open-source library designed for graph deep learning, enabling researchers and developers to efficiently train and compare models. It supports key tasks such as node classification, graph classification, and other important graph domain applications. The library emphasizes efficiency through optimized operators for faster training and reduced GPU memory usage, ease of use with intuitive APIs for hyper-parameter search, and extensibility for applying GNN models to new scenarios. CogDL also incorporates features like fast sparse matrix-matrix multiplication (SpMM) for accelerated GNN training and supports mixed-precision training. It has been accepted by WWW 2023 and offers resources like a free GNN course and a discussion forum.

Best used for

Ideal for developers and data scientists who need to efficiently train graph neural networks, compare various GNN models, and apply them to tasks like node or graph classification. Especially valuable for researchers looking for an extensible framework with optimized performance for graph deep learning.

Common actions

train graph models

compare GNN models

develop graph deep learning

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Capabilities

Key features

Optimized GNN operators
Easy-to-use APIs
Hyper-parameter search
Fast SpMM operator
Mixed-precision training
Unified training loop

Target Audience

developerdata scientistresearcher

Integrations

pytorch

Pricing & Plans

Open Source

Free

FAQs

What are the system requirements for CogDL?

CogDL requires Python version 3.7 or higher and PyTorch version 1.7.1 or higher. Users should follow the official PyTorch installation instructions before installing CogDL via pip or from source. Specific CUDA versions might be needed for certain optimized features like GE-SpMM.

How does CogDL ensure efficient GNN training?

CogDL achieves efficiency through well-optimized operators, including a fast sparse matrix-matrix multiplication (GE-SpMM) for GPU acceleration. It also supports mixed-precision training (fp16) to further speed up training and reduce GPU memory consumption, making it suitable for large-scale graph datasets.

Can I use my own datasets and models with CogDL?

Yes, CogDL is designed for extensibility. It provides APIs that allow users to integrate their own datasets and custom models. Examples are available in the documentation, and it supports using modules from other popular graph libraries like PyTorch Geometric (PyG) within its framework.

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Coding & Development › Open Source & Models Research & Education › Scientific Computing

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