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Release Notes

August 20, 2026

Release 3.0.0

Breaking Changes

  • Multi-node multi-GPU (MNMG) pipeline — training now requires torchrun and the new gpu: multi / gpu: single config field. Legacy non-torchrun entrypoints are no longer supported, while single-GPU runs remain supported with gpu: single (or NPROC=1).
  • New config schema — the YAML/JSON config format has changed substantially. The FullConfig schema now groups all settings under paths and models[].hyperparameters.gnn / models[].hyperparameters.xgb. Old flat configs are not compatible and must be migrated.
  • Triton server version — the serving container is now nvcr.io/nvidia/tritonserver:26.04-py3. Artifacts generated by earlier training containers are not compatible with this server version.
  • kind: GNN_XGBoost_NP — Node Prediction is now a first-class kind option in the config rather than a separate container entrypoint.
  • Mask file placement — train/val/test mask files are now placed in edges/ (EP) or nodes/ (NP). The previous location is no longer supported.
  • Edge filename parser — the src/rel/dst parsing now always takes the first token as src and the last token as dst. Multi-token node type names (e.g., credit_card) in edge filenames are no longer supported.

New Features

  • Automated HPO — llm_tune.sh (LLM-guided iterative tuning) and grid_search.py (Cartesian grid search) ship inside the container.
  • LLM Explainability — fraud_pipeline_explained ensemble chains GNN → XGBoost Shapley Value Sampling → OpenAI-compatible LLM for human-readable fraud explanations. Works with any OpenAI-compatible endpoint (NVIDIA NIM, OpenAI, Ollama, etc.).
  • Multi-GPU horizontal inference scaling — serve.sh accepts NUM_GPUS and INSTANCES_PER_GPU and patches all config.pbtxt files atomically.
  • Parquet and ORC support — node, edge, label, and attribute files can now be CSV, Parquet, or ORC. Formats can be mixed within a dataset.
  • XGBoost external memory modes — batched, extmem, and memmap options for datasets that exceed CPU RAM.
  • Focal loss — focal_gamma and focal_alpha GNN hyperparameters for severe class imbalance.
  • Production threshold calibration — prior_test parameter corrects the decision threshold for the production class ratio.
  • Zero-downtime model updates — Triton model versioning support documented; serve.sh supports rolling updates.
  • inf_num_neighbors — separate neighbor sampling cap for inference, enabling deployment on graphs with high-degree nodes.