"## Defining Neighbor Sampler and Data Loader in DGL\n",
"\n",
"DGL provides tools to iterate over the dataset in minibatches while generating the computation dependencies to compute their outputs with the MFGs above. For node classification, you can use `dgl.graphbolt.MultiProcessDataLoader` for iterating over the dataset. It accepts a data pipe that generates minibatches of nodes and their labels, sample neighbors for each node, and generate the computation dependencies in the form of MFGs. Feature fetching, block creation and copying to target device are also supported. All these operations are split into separate stages in the data pipe, so that you can customize the data pipeline by inserting your own operations.\n",
"DGL provides tools to iterate over the dataset in minibatches while generating the computation dependencies to compute their outputs with the MFGs above. For node classification, you can use `dgl.graphbolt.DataLoader` for iterating over the dataset. It accepts a data pipe that generates minibatches of nodes and their labels, sample neighbors for each node, and generate the computation dependencies in the form of MFGs. Feature fetching, block creation and copying to target device are also supported. All these operations are split into separate stages in the data pipe, so that you can customize the data pipeline by inserting your own operations.\n",
"\n",
"Let’s say that each node will gather messages from 4 neighbors on each layer. The code defining the data loader and neighbor sampler will look like the following.\n"
