rdf2vecgpu 0.2.3

GPU-Accelerated RDF2Vec – A high-performance GPU implementation of RDF2Vec that uses CUDA and RAPIDS to generate scalable, embeddings for large dense knowledge graphs.

gpuRDF2Vec

A scalable GPU based implementation of RDF2Vec embeddings for large and dense Knowledge Graphs.

[!IMPORTANT] This package is under active development in the beta phase. The overall class/ method design will most probably change and introduce breaking changes between releases

Table of contents

The content of this repository readme can be found here:

• gpuRDF2Vec
• Table of contents
• Package installation
• Repository setup
• gpuRDF2Vec overview
  • Repository Structure
  • Capability overview
• Quick start
• Implementation Details
• License
• Roadmap
• Report issues and bugs

Package installation

Install the package rdf2vecgpu by running the following command:

pip install rdf2vecgpu

[!IMPORTANT] Make sure to install the accompanying cuda version as outlined in the following section

Repository setup

The repository setup builds on top of two major libraries. Both Pytorch lightning as well as the RAPIDS libraries cuDF and cuGraph. We provide the exeplanatory installation details for Cuda 12.6:

1. Pytorch installation page Cuda 12.6 installation

pip install torch torchvision torchaudio

2. Detailed cudf installation instruction here. Cudf Cuda 12 install

pip install \
    --extra-index-url=https://pypi.nvidia.com \
    "cudf-cu12==25.4.*" "dask-cudf-cu12==25.4.*" \
    "cugraph-cu12==25.4.*" "nx-cugraph-cu12==25.4.*" \
    "nx-cugraph-cu12==25.4.*"

The requirement files and conda environment files can be found here:

• Conda environment
• Requirement file

gpuRDF2Vec overview

RDF2Vec is a powerful technique to generate vector embeddings of entities in RDF graphs via random walks and Word2Vec. This repository provides a GPU-optimized reimplementation, enabling:

• Speedups on dense graphs with millions of nodes
• Scalability to industrial-scale knowledge bases
• Reproducible experiments to test and qualify the overall implementation details

Repository Structure

.
├── README.md
├── data
├── data_preparation
│   ├── converstion_to_ttl.py
│   └── merge_text_file.py
├── img
│   └── github_repo_header.png
├── jrdf2vec-1.3-SNAPSHOT.jar
├── performance
│   ├── env_files
│   │   ├── jrdf2vec_environment.yml
│   │   ├── jrdf2vec_requirements.txt
│   │   ├── pyrdf2vec_environment.yml
│   │   ├── pyrdf2vec_requirements.txt
│   │   ├── rdf2vecgpu_environment.yml
│   │   ├── rdf2vecgpu_requirements.txt
│   │   ├── sparkrdf2vec_environment.yml
│   │   └── sparkrdf2vec_requirements.txt
│   ├── evaluation_parameters.py
│   ├── gpu_rdf2vec_performance.py
│   ├── graph_creation.py
│   ├── graph_statistics.py
│   ├── jrdf2vec_based_performance.py
│   ├── pyrdf2vec_based_performance.py
│   ├── spark_rdf2vec_performance.py
│   └── wandb_analysis.py
├── src
│   ├── __init__.py
│   ├── corpus
│   │   ├── __init__.py
│   │   └── walk_corpus.py
│   ├── cpu_based_rdf2vec_approach.py
│   ├── embedders
│   │   ├── __init__.py
│   │   ├── word2vec.py
│   │   └── word2vec_loader.py
│   ├── gpu_rdf2vec.py
│   ├── helper
│   │   ├── __init__.py
│   │   └── functions.py
│   └── reader
│       ├── __init__.py
│       └── kg_reader.py
└── test
    ├── helper
    └── reader
        ├── functions_test.py
        └── kg_reader_test.py

Capability overview

• GPU-backed walk generation over CUDA Kernels
• Batched Word2Vec training with Pytorch lightning
• Pluggable rdf loaders and parquet, csv, txt integration
• Performance comparison can be found in the following folder

Quick start

from rdf2vecgpu.gpu_rdf2vec import GPU_RDF2Vec
# Instantiate the gpu RDF2Vec library settings
gpu_rdf2vec_model = GPU_RDF2Vec(
    walk_strategy="random",
    walk_depth=4,
    walk_number=100,
    embedding_model="skipgram",
    epochs=5,
    batch_size=None,
    vector_size=100,
    window_size=5,
    min_count=1,
    learning_rate=0.01,
    negative_samples=5,
    random_state=42,
    reproducible=False,
    multi_gpu=False,
    generate_artifact=False,
    cpu_count=20
)
# Path to the triple dataset
path = "data/wikidata5m/wikidata5m_kg.parquet"
# Load data and receive edge data
edge_data = gpu_rdf2vec_model.load_data(path)
# Fit the Word2Vec model and transform the dataset to an embedding
embeddings = gpu_rdf2vec_model.fit_transform(edge_df=edge_data, walk_vertices=None)
# Write embedding to file format. Return format is a cuDf dataframe
embeddings.to_parquet("data/wikidata5m/wikidata5m_embeddings.parquet", index=False)

• Supported file formats:
  • .csv
  • .txt
  • .parquet
  • .nt
  • All supported RDFlib file formats
• gpuRDF2Vec Parameters:
  • walk_strategy: [random, bfs]
  • walk_depth: int
  • walk_number: int
  • embedding_model: [skipgram, cbow]
  • epochs: int
  • batch_size: [None | int] --> If the batch size is None, we guess internally the batch size based on the data loader and the number of CPU counts provided
  • vector_size
  • window_size: int
  • min_count: int
  • learning_rate: float
  • negative_samples: int
  • random_state: int
  • reproducible: bool
  • multi_gpu: bool
  • generate_artifact: bool
  • cpu_count: int

Implementation Details

We achieve order-of-magnitude for large and dense graphs over CPU-bound RDF2Vec by engineering both the walk extraction and the Word2Vec training pipelines:

1. GPU-Native Walk Extraction

   • All random-walk and BFS operations leverage cuDF/cuGraph kernels to avoid CPU–GPU data transfers and minimize latency.
   • To generate k walks per node in one pass, we replicate node indices in a single cuDF DataFrame rather than looping—fully utilizing GPU parallelism and eliminating Python-loop overhead (∼15× speedup).
   • BFS walks currently use GPU-side recursive joins; future work will reconstruct walks entirely in CUDA to remove join overhead.

2. cuDF→PyTorch Lightning Handoff

   • Replaced Lightning’s default CPU-based DataLoader with a cuDF-backed pipeline: context/center columns live on GPU as DLPack tensors.
   • Initial deep-copy loads incur extra VRAM, but thereafter all sampling/preprocessing occurs on-device, eliminating PCIe stalls.
   • An “index-only” strategy (workers pull tensor indices instead of slices) uses CUDA’s pointer arithmetic for constant-time access, collapsing DataLoader overhead from ~85% of epoch time to near parity with model compute.

3. Optimized Word2Vec Training

   • Batch-Size Heuristic: Estimate per-sample GPU footprint from cuDF loader, then set initial batch = (total VRAM) / (4 × footprint). This “divide-by-four” rule quickly homes in on a viable batch size, reducing tuning runs.
   • Kernel Fusion: All sampling and tensor transforms migrated into PyTorch’s C++ back end, removing Python loops and the GIL, for consistent high throughput.

4. Scalable Data-Parallel Training

   • We use PyTorch Distributed + NCCL: each GPU holds the same graph shard but a unique walk corpus.
   • Gradients are synchronized via all_reduce at regular intervals (~500 ms), amortizing PCIe/NVLink costs and ensuring linear scaling across nodes.

License

The overview of the used MIT license can be found here

Roadmap

• Order aware Word2Vec following the details of Ling, Wang, et al. "Two/too simple adaptations of word2vec for syntax problems.. Issue item
• Provide spilling to single GPU training to work around potential OOM issues faced during rdf2vec training Issue Item
• Provide weighted walks for spatial datasets Issue item
• Provide logging capabilities of complete Word2Vec pipeline for Wandb and mlflow. Issue item

Report issues and bugs

In case you have found a bug or unexpected behaviour, please reach out by opening an issue:

1. When opening an issue, please tag the issue with the label Bug. Please include the following information:

   • Environment: OS, Python/CUDA/PyTorch/RAPIDS versions (cuDF, cuGraph)
   • Reproduction steps: Exact commands or small code snippet
   • Input data graph format & size (attach a minimal sample if possible)
   • Observed vs. expected behavior
   • Error messages/ stack traces (copy-paste or attach logs)

2. We aim to respond to open issues within 3 business days

3. If you have identified a fix, fork the repo, branch off main, implement & test then open a PR referencing the issue.