Install & Compatibility
Where this runs
tested against v1.3.6 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
py 3.10
✕ build_error
✓ 87.3s
py 3.11
✕ build_error
✓ 84.4s
py 3.12
✕ build_error
✓ 72.9s
py 3.13
✕ build_error
✓ 70.8s
py 3.9
✕ build_error
✕ timeout
5094MB installed
● package 5094MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
dataset classes
✓ from ogb.nodeproppred import PygNodePropPredDataset
✗ from ogb.nodeproppred import NodePropertyPrediction
OGB provides PyG/DGL wrapper datasets; raw import is rarely used.
Evaluator
✓ from ogb.nodeproppred import Evaluator
✗ from ogb.utils import Evaluator
Evaluator is dataset-specific, import from the correct submodule.
Load the ogbn-arxiv dataset using PyG wrapper and run a dummy evaluation.
from ogb.nodeproppred import PygNodePropPredDataset
from ogb.nodeproppred import Evaluator
dataset = PygNodePropPredDataset(name='ogbn-arxiv')
split_idx = dataset.get_idx_split()
train_idx, valid_idx, test_idx = split_idx['train'], split_idx['valid'], split_idx['test']
graph = dataset[0]
# For evaluation, use the evaluator
evaluator = Evaluator(name='ogbn-arxiv')
# Example: dummy predictions and labels (for illustration only)
import torch
y_pred = torch.randn(len(graph.y))
y_true = graph.y
result = evaluator.eval({'y_pred': y_pred, 'y_true': y_true})
print(result)
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Version history
1.3.6latest on PyPI · released Apr 7, 2023
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Dependencies
numpyrequiredCore dependency for graph data structures
pandasrequiredUsed for tabular data loading
tqdmrequiredProgress bars for downloads
scikit-learnoptionalFor evaluation metrics (e.g., ROC-AUC)
torchoptionalFor PyTorch tensor conversion and dataloaders