Comparing PyTorch Geometric and Kumo

PyG vs. Kumo

Kumo uses the open source framework PyG to help you operationalize graph learning for enterprise-grade production

PyG Users

Kumo uses PyG's open source framework to help you operationalize graph learning for enterprise-grade production

What Should I Use?


PyTorch Geometric, built by the core members of the Kumo team, is the leading open source framework for building and training Graph Neural Networks. PyG is built for the academic and research communities, offering a toolbox of application-specific libraries that make it easy to build new, custom algorithms or architectures for tackling any research problem. The framework gives you full customization and flexibility to build cutting edge models from the ground up, using the familiar design principles of PyTorch. PyG does not provide any production workflow or orchestration tooling, and is not meant to scale to billions of nodes.

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If you are looking to build a production-ready application and are interested in leveraging graph learning, or if you have business data and you’d like to make predictions about the future, Kumo is the fastest and easiest approach. Kumo is a highly scalable, production-ready platform built on top of PyG. You can leverage fully automated and optimized machine learning capabilities that are robust, performant, and scalable out-of-the-box. Kumo gives you automated support around graph building, distributed model training, support for rich and complex graphs, elastic and horizontal scalability out-of-the-box, prediction workflows, and integrations with the modern data stack.

Operationalize Graph LearningIn what ways does Kumo support your graph learning applications beyond what PyG can offer?

  • Ingest your data and automate the graph creation process - as easy as defining a schema
  • Skip the tedious process of manual encodings and feature engineering - the Kumo graph is optimized for your data specifically, inferring semantic relationships between columns
  • Use Kumo’s interface to directly pull the latest optimized embeddings from the graph
  • Kumo lets you embed your entire dataset, allowing your embeddings to maximize the context they learn from
  • Kumo provides production orchestration and workflow tooling to help you go from raw data to predictions
  • Kumo’s distributed, elastic architecture enables you to scale on demand and manage concurrency
  • Use Kumo’s feature and graph store to scale to massive enterprise-wide data while optimizing feature and graph look-up
  • Kumo automatically updates your graph as either new data comes in or as your data changes, that way your graph is always up-to-date and can perform inference without needing to retrain your graph
  • The latest embeddings are pulled directly at prediction time
  • Define the graph once and re-use for any downstream predictions
  • Kumo embeds your entire dataset, so the same graph can be used for many different ML use cases

PyG vs Kumo

feature comparison
Automated ML out-of-the-box
Automated data processing, graph generation, and feature encoding
Production orchestration and workflows - Automated end-to-end pipelines
Security and Governance Features - protect your data and your workloads
Monitoring, Evaluation, and Explainability OOB
Horizontal Scalability out-of-the-box
Expert Support - Unparalleled support by leading experts
Integrations - compatible with common tools in ecosystem
Scale to Many Applications
Graph lifecycle management
Elastic and Distributed Architecture

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