Deep learning on graphs has attracted tremendous attention from the graph learning community in recent years. It has been widely adopted in various real-world applications from diverse domains, such as social and information networks, biological graphs, and molecular graphs. In this paper, we present CogDL--an extensive toolkit for deep learning on graphs--that allows researchers and developers to easily conduct experiments and build applications. In CogDL, we propose a unified design for the training loop of graph neural network (GNN) models, making it unique among existing graph learning libraries. By utilizing this unified trainer, we can optimize the GNN training loop with several training techniques such as distributed training and mixed precision training. Moreover, we develop efficient sparse operators for CogDL, enabling it to become the most competitive graph library for efficiency. Additionally, another important CogDL feature is its focus on ease of use with the goal of facilitating open, robust, and reproducible graph learning research. We leverage CogDL to report and maintain benchmark results on the fundamental graph tasks such as node classification and graph classification, which can be reproduced and directly used by the community. Finally, we demonstrate the effectiveness and efficiency of CogDL for real-world applications in AMiner--a large-scale academic mining and search system. The CogDL toolkit is available at: https://github.com/thudm/cogdl.

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