python-gfm 0.1.19

Installation · Supported Baselines · Configs (HF download) · Experiment workflow · Documentation

pygfm is a unified Python toolkit for Graph Foundation Model (GFM) research. It integrates 11 state-of-the-art baseline methods under a single, pip-installable package with shared utilities, standardized interfaces, and fully reproducible experiment pipelines.

Developed by Beihang University · School of Computer Science and Engineering · ACT Lab · MAGIC GROUP.

Framework Overview

PyGFM is organized into four stacked layers — Graph Data Abstraction → Alignment & Fusion Bridge → Representation Backbones → Task Heads & Orchestration — with a unified CLI, model recipes, and an auto-experiment tracker sitting on top.

Highlights

• One package, 11 baselines — prompt-based GFMs, structure-aware models, LLM-integrated approaches, and retrieval-augmented methods all available via a single pip install.
• Reproducible pipelines — every baseline ships with YAML-driven experiment configs, training scripts, and evaluation helpers.
• Shared backbone library — common GNN encoders, loss functions, and data utilities are factored out and reused across all baselines, reducing code duplication.
• CLI-first design — launch pre-training, fine-tuning, and evaluation jobs directly from the command line without writing any boilerplate.
• LLM-ready — first-class support for LLM-integrated GFMs (GraphText) with HuggingFace-compatible YAML configs.

Installation

CUDA (recommended)

Default (fresh env): torch + light together — PyTorch wheel index + PyPI + PyG find-links:

pip install "python-gfm[torch,light]" --index-url https://download.pytorch.org/whl/cu128 --extra-index-url https://pypi.org/simple -f https://data.pyg.org/whl/torch-2.8.0+cu128.html

If CUDA PyTorch / PyG is already in the env — install [light] from PyPI only:

pip install "python-gfm[light]"

LLM-integrated GFMs — after [torch] and [light] are in place:

pip install "python-gfm[llm]"

CPU: --index-url https://download.pytorch.org/whl/cpu and -f https://data.pyg.org/whl/torch-2.8.0+cpu.html.

Extras overview

Optional dev extra

pip install "python-gfm[dev]" adds pytest and ruff for testing and linting.

Package layout (installed wheel)

pygfm/
├── baseline_models/   # GFM baseline implementations
├── public/            # Shared utilities, losses, backbone encoders
├── private/           # Core encoders and internal helpers
└── cli/               # Console entry points

Supported Baselines

Reproducing baselines (config download)

Published YAML configs and toolbox assets live in a Hugging Face dataset. With python-gfm installed (stdlib only; no extra deps for this step), run:

python -m pygfm.cli.download --repo aboutime233/gtb --path gfmtoolbox_docs

Outputs go under --outdir (default: downloads/). Command-line options for the downloader (repo, revision, path, output directory, etc.) are described in the official documentation on the project homepage.

Experiment workflow

Typical end-to-end flow (YAML names and paths are examples — point -c at the configs you downloaded or arranged for your baseline):

# Download config files, or manually fetch them from the Hugging Face dataset:
# https://huggingface.co/datasets/aboutime233/gtb
python -m pygfm.cli.download

# Configure datasets and other settings following each baseline’s official documentation on the project site.

# Step 1: Generate few-shot downstream splits
python -m pygfm.cli.run_yaml -c configs/mdgpt/01_split_cora_1shot.yaml
# -> downstream_data/mdgpt/splits.pt

# Step 2: Leave-one-domain pre-training
python -m pygfm.cli.run_yaml -c configs/mdgpt/02_pretrain_cora.yaml
# -> ckpts/mdgpt/preprompt.pth

# Step 3: Downstream fine-tuning & evaluation
python -m pygfm.cli.run_yaml -c configs/mdgpt/03_finetune_cora_1shot.yaml
# -> Cora 1-shot node classification accuracy (and other logged outputs)

The same YAML driver is available as pygfm / gfm (see Console Commands): pygfm -c configs/mdgpt/02_pretrain_cora.yaml.

Console Commands

Configuration

After downloading configs, drive stages with pygfm / gfm or python -m pygfm.cli.run_yaml and -c (see Experiment workflow). For each baseline, read the official documentation on the project homepage (hyperparameters, data roots, optional API keys, etc.); do not commit secrets.

Baseline Documentation

Each baseline’s setup, data layout, and evaluation notes are published in the official documentation on the project homepage. Index of per-method guides:

Requirements

Install [torch,light] for GNN baselines; add [llm] on top for LLM-integrated methods. Pinned versions above come from pyproject.toml; see that file for the full dependency specification.

License

This project is licensed under the Apache License 2.0.

Team

MAGIC GROUP — Beihang University, School of Computer Science and Engineering, ACT Lab.