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