AlgRUC/JittorGeometric

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1177 stars · 184 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

JittorGeometric is a graph machine learning library built on the Jittor deep learning framework, providing implementations of 40+ Graph Neural Network models, dataset loaders, and optimized sparse operations. Version 2.0 adds distributed training, dynamic graph processing, mini-batch support, and NPU (Ascend) compatibility.

Use cases

• train graph neural networks on citation datasets like Cora
• run GNN experiments on the Jittor framework
• distributed multi-GPU training of graph models
• process dynamic graphs with event-based updates
• mini-batch training on large-scale graphs like Reddit
• run graph learning on Huawei Ascend NPUs
• implement graph transformers and self-supervised graph learning

When to choose

• you want a PyG-like GNN library but must use Jittor
• you need GNN support for Ascend NPUs
• you need distributed or mini-batch GNN training in Jittor

When to avoid

• your stack is PyTorch or TensorFlow - use PyTorch Geometric or DGL instead
• you need a very mature ecosystem with broad community support

Facets

library · maturity active

machine-learning deep-learning gpu-computing machine-learning deep-learning graph-processing python cross-platform graph-neural-networks gnn jittor graph-machine-learning model-zoo distributed-training dynamic-graphs sparse-operations algorithms gpu linux

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