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[Dist] New distributed data preparation pipeline (#4386)  
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# DGL Utility Scripts

This folder contains the utilities that do not belong to DGL core package as standalone executable
scripts.

## Graph Chunking

`chunk_graph.py` provides an example of chunking an existing DGLGraph object into the on-disk
[chunked graph format](http://13.231.216.217/guide/distributed-preprocessing.html#chunked-graph-format).

<!-- TODO: change the link of documentation once it's merged to master -->

An example of chunking the OGB MAG240M dataset:

```python
import ogb.lsc

dataset = ogb.lsc.MAG240MDataset('.')
etypes = [
    ('paper', 'cites', 'paper'),
    ('author', 'writes', 'paper'),
    ('author', 'affiliated_with', 'institution')]
g = dgl.heterograph({k: tuple(dataset.edge_index(*k)) for k in etypes})
chunk_graph(
    g,
    'mag240m',
    {'paper': {
        'feat': 'mag240m_kddcup2021/processed/paper/node_feat.npy',
        'label': 'mag240m_kddcup2021/processed/paper/node_label.npy',
        'year': 'mag240m_kddcup2021/processed/paper/node_year.npy'}},
    {},
    4,
    'output')
```

The output chunked graph metadata will go as follows (assuming the current directory as
`/home/user`:

```json
{
    "graph_name": "mag240m",
    "node_type": [
        "author",
        "institution",
        "paper"
    ],
    "num_nodes_per_chunk": [
        [
            30595778,
            30595778,
            30595778,
            30595778
        ],
        [
            6431,
            6430,
            6430,
            6430
        ],
        [
            30437917,
            30437917,
            30437916,
            30437916
        ]
    ],
    "edge_type": [
        "author:affiliated_with:institution",
        "author:writes:paper",
        "paper:cites:paper"
    ],
    "num_edges_per_chunk": [
        [
            11148147,
            11148147,
            11148146,
            11148146
        ],
        [
            96505680,
            96505680,
            96505680,
            96505680
        ],
        [
            324437232,
            324437232,
            324437231,
            324437231
        ]
    ],
    "edges": {
        "author:affiliated_with:institution": {
            "format": {
                "name": "csv",
                "delimiter": " "
            },
            "data": [
                "/home/user/output/edge_index/author:affiliated_with:institution0.txt",
                "/home/user/output/edge_index/author:affiliated_with:institution1.txt",
                "/home/user/output/edge_index/author:affiliated_with:institution2.txt",
                "/home/user/output/edge_index/author:affiliated_with:institution3.txt"
            ]
        },
        "author:writes:paper": {
            "format": {
                "name": "csv",
                "delimiter": " "
            },
            "data": [
                "/home/user/output/edge_index/author:writes:paper0.txt",
                "/home/user/output/edge_index/author:writes:paper1.txt",
                "/home/user/output/edge_index/author:writes:paper2.txt",
                "/home/user/output/edge_index/author:writes:paper3.txt"
            ]
        },
        "paper:cites:paper": {
            "format": {
                "name": "csv",
                "delimiter": " "
            },
            "data": [
                "/home/user/output/edge_index/paper:cites:paper0.txt",
                "/home/user/output/edge_index/paper:cites:paper1.txt",
                "/home/user/output/edge_index/paper:cites:paper2.txt",
                "/home/user/output/edge_index/paper:cites:paper3.txt"
            ]
        }
    },
    "node_data": {
        "paper": {
            "feat": {
                "format": {
                    "name": "numpy"
                },
                "data": [
                    "/home/user/output/node_data/paper/feat-0.npy",
                    "/home/user/output/node_data/paper/feat-1.npy",
                    "/home/user/output/node_data/paper/feat-2.npy",
                    "/home/user/output/node_data/paper/feat-3.npy"
                ]
            },
            "label": {
                "format": {
                    "name": "numpy"
                },
                "data": [
                    "/home/user/output/node_data/paper/label-0.npy",
                    "/home/user/output/node_data/paper/label-1.npy",
                    "/home/user/output/node_data/paper/label-2.npy",
                    "/home/user/output/node_data/paper/label-3.npy"
                ]
            },
            "year": {
                "format": {
                    "name": "numpy"
                },
                "data": [
                    "/home/user/output/node_data/paper/year-0.npy",
                    "/home/user/output/node_data/paper/year-1.npy",
                    "/home/user/output/node_data/paper/year-2.npy",
                    "/home/user/output/node_data/paper/year-3.npy"
                ]
            }
        }
    },
    "edge_data": {}
}
```