Concrete ML
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  • Welcome
  • Get Started
    • What is Concrete ML?
    • Installation
    • Key concepts
    • Inference in the cloud
  • Built-in Models
    • Linear models
    • Tree-based models
    • Neural networks
    • Nearest neighbors
    • Encrypted dataframe
    • Encrypted training
  • LLMs
    • Inference
    • Encrypted fine-tuning
  • Deep Learning
    • Using Torch
    • Using ONNX
    • Step-by-step guide
    • Debugging models
    • Optimizing inference
  • Guides
    • Prediction with FHE
    • Production deployment
    • Hybrid models
    • Serialization
    • GPU acceleration
  • Tutorials
    • See all tutorials
    • Built-in model examples
    • Deep learning examples
  • References
    • API
  • Explanations
    • Security and correctness
    • Quantization
    • Pruning
    • Compilation
    • Advanced features
    • Project architecture
      • Importing ONNX
      • Quantization tools
      • FHE Op-graph design
      • External libraries
  • Developers
    • Set up the project
    • Set up Docker
    • Documentation
    • Support and issues
    • Contributing
    • Support new ONNX node
    • Release note
    • Feature request
    • Bug report
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Libraries

  • TFHE-rs
  • Concrete
  • Concrete ML
  • fhEVM

Developers

  • Blog
  • Documentation
  • Github
  • FHE resources

Company

  • About
  • Introduction to FHE
  • Media
  • Careers
On this page
  • Get started
  • Build with Concrete ML
  • Explore more
  • References & Explanations
  • Support channels
  • Developers

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Welcome

Concrete ML is an open-source, privacy-preserving, machine learning framework based on Fully Homomorphic Encryption (FHE).

Last updated 1 month ago

Was this helpful?

Get started

Learn the basics of Concrete ML, set it up, and make it run with ease.

Build with Concrete ML

Start building with Concrete ML by exploring its core features, discovering essential guides, and learning more with user-friendly tutorials.

Explore more

Access to additional resources and join the Zama community.

References & Explanations

Refer to the API, review product architecture, and access additional resources for in-depth explanations while working with Concrete ML.

Support channels

Ask technical questions and discuss with the community. Our team of experts usually answers within 24 hours in working days.

Developers

Collaborate with us to advance the FHE spaces and drive innovation together.


Zama 5-Question Developer Survey

We want to hear from you! Take 1 minute to share your thoughts and helping us enhance our documentation and libraries. 👉 to participate.

Community channels
Contribute to Concrete ML
Check the latest release note
Request a feature
Report a bug
Click here
Security and correctness
API
Quantization
Pruning
Compilation
Advanced features
Project architecture
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Fundamentals

Explore core features.

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Guides

Deploy your projects.

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Tutorials

Learn more with tutorials.

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What is Concrete ML

Understand the Concrete ML library with a full example.

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Installation

Follow the step-by-step guide to install Concrete ML in your project.

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Key concepts

Understand important cryptographic concepts to implement Concrete ML.

Built-in models
Deep learning
Prediction with FHE
Production deployment
Start here
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