MixGCN: Scalable Graph Convolutional Network Training by Mixture of Parallelism and Mixture of Accelerators is a novel framework designed to address the challenges of training Graph Convolutional Networks (GCNs) on large-scale graphs. GCNs are widely used for graph-based learning tasks, but their scalability is often hindered by memory limitations, communication bottlenecks, and the computational complexity of alternating between sparse and dense matrix operations. MixGCN introduces innovative techniques to overcome these challenges, enabling efficient and scalable GCN training.