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ID Embedding Training (LightGCN) #367

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0b19adb
Added DMP tests (single process) and simple fix to deal with awaitabl…
swong3-sc Oct 16, 2025
96c1ed8
Initial commit for LightGCN training loop
swong3-sc Oct 23, 2025
ae9c731
Fixed mypy
swong3-sc Oct 25, 2025
c655537
Minor tweaks, removing comments
swong3-sc Oct 28, 2025
7a9a045
Adding homogenous config file for id embedding
swong3-sc Oct 28, 2025
756b39d
Fixed wrong total nodes computation
swong3-sc Nov 5, 2025
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Empty file added examples/id_embeddings/__init__.py
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41 changes: 41 additions & 0 deletions examples/id_embeddings/configs/e2e_hom_cora_sup_task_config.yaml
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# This config is used to run homogeneous CORA supervised training and inference using in memory GiGL SGS. This can be run with `make run_hom_cora_sup_test`.
graphMetadata:
edgeTypes:
- dstNodeType: paper
relation: cites
srcNodeType: paper
nodeTypes:
- paper
datasetConfig:
dataPreprocessorConfig:
dataPreprocessorConfigClsPath: gigl.src.mocking.mocking_assets.passthrough_preprocessor_config_for_mocked_assets.PassthroughPreprocessorConfigForMockedAssets
dataPreprocessorArgs:
# This argument is specific for the `PassthroughPreprocessorConfigForMockedAssets` preprocessor to indicate which dataset we should be using
mocked_dataset_name: 'cora_homogeneous_node_anchor_edge_features_user_defined_labels'
trainerConfig:
trainerArgs:
# Example argument to trainer
log_every_n_batch: "50" # Frequency in which we log batch information
num_neighbors: "[10, 10]" # Fanout per hop, specified as a string representation of a list for the homogeneous use case
command: python -m examples.id_embeddings.homogeneous_training
inferencerConfig:
inferencerArgs:
# Example argument to inferencer
log_every_n_batch: "50" # Frequency in which we log batch information
num_neighbors: "[10, 10]" # Fanout per hop, specified as a string representation of a list for the homogeneous use case
inferenceBatchSize: 512
command: python -m examples.id_embeddings.homogeneous_inference
sharedConfig:
shouldSkipAutomaticTempAssetCleanup: false
shouldSkipInference: false
# Model Evaluation is currently only supported for tabularized SGS GiGL pipelines. This will soon be added for in-mem SGS GiGL pipelines.
shouldSkipModelEvaluation: true
taskMetadata:
nodeAnchorBasedLinkPredictionTaskMetadata:
supervisionEdgeTypes:
- dstNodeType: paper
relation: cites
srcNodeType: paper
featureFlags:
should_run_glt_backend: 'True'
data_preprocessor_num_shards: '2'
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