dgl cuda12.4

### DGL (Deep Graph Library) CUDA 12.4 Compatibility and Installation Guide

For users aiming to leverage the power of GPU acceleration with Deep Graph Library (DGL), ensuring compatibility between DGL and specific versions of CUDA is crucial. For CUDA 12.4, a dedicated installation process exists that facilitates this setup on systems like Ubuntu 20.04.

The installation command specifically tailored for integrating DGL with CUDA 12.4 involves using a custom repository URL provided by the official DGL team. This ensures that all dependencies are correctly aligned with the specified version of CUDA:

pip install dgl -f https://data.dgl.ai/wheels/torch-2.4/cu124/repo.html [^2]

This command installs DGL alongside its dependencies optimized for PyTorch version 2.4 and CUDA 12.4. It's important to note that choosing the correct wheel file based on your environment configuration can significantly impact performance and functionality.

Moreover, while CPU-only installations offer convenience for initial exploration or environments lacking suitable GPUs, leveraging CUDA capabilities allows for more efficient processing of large-scale graph data structures as described in practical applications of DGL[^1]. The message passing mechanism within DGL further enhances these benefits through streamlined operations designed explicitly for parallel architectures supported by modern GPUs[^4].