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
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  • Concrete ML
  • fhEVM

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On this page
  • FHE constraints considerations
  • List of Examples
  • 1. Step-by-step guide to building a custom NN
  • 2. Custom convolutional NN on the Digits data-set

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  1. Tutorials

Deep learning examples

PreviousBuilt-in model examplesNextAPI

Last updated 1 month ago

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These examples illustrate the basic usage of Concrete ML to build various types of neural networks. They use simple data-sets, focusing on the syntax and usage of Concrete ML. For examples showing how to train high-accuracy models on more complex data-sets, see the section.

FHE constraints considerations

The examples listed here make use of to perform evaluation over large test sets. Since FHE execution can be slow, only a few FHE executions can be performed. The of Concrete ML ensure that accuracy measured with simulation is the same as that which will be obtained during FHE execution.

Some examples constrain accumulators to 7-8 bits, which can be sufficient for simple data-sets. Up to 16-bit accumulators can be used, but this introduces a slowdown of 4-5x compared to 8-bit accumulators.

List of Examples

1. Step-by-step guide to building a custom NN

This shows how to use Quantization Aware Training and pruning when starting out from a classical PyTorch network. This example uses a simple data-set and a small NN, which achieves good accuracy with low accumulator size.

2. Custom convolutional NN on the data-set

Following the , this notebook implements a Quantization Aware Training convolutional neural network on the MNIST data-set. It uses 3-bit weights and activations, giving a 7-bit accumulator.

Demos and Tutorials
Quantization aware training example
Digits
Convolutional Neural Network
Step-by-step guide
correctness guarantees
simulation