An obvious trend in deep learning area is the high integration of deep learning framework. These frameworks provide us an off-the-shelf tool to conveniently and quickly deploy neural networks but also keep the necessary model flexibility for customising specific architectures. In the area of graph neural networks, there are also several frameworks. The two most popular frameworks are Deep Graph Library(DGL) and PyTorch Geometric(PyG). Here we will briefly introduce some of these frameworks.
A graph processing framework (GPF) is a set of tools oriented to process graphs. Graph vertices are used to model data and edges model relationships between vertices. Here we briefly introduce several graph processing and analyzing tools.
Here we introduce some tools for GNN profiling and performance analysis on Linux system.