Concrete ML
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  • What is Concrete ML?
  • Installing
    • Installing
  • How To
    • Scikit-learn
    • Torch
    • Compute with Quantized Functions
    • Use Concrete ML ONNX Support
    • Debug / Get Support / Submit Issues
  • Advanced examples
    • Advanced examples
  • Explanations
    • Philosophy of the Design
    • Quantization
    • Pruning
    • Virtual Lib
    • Resources
  • Developper How To
    • Set Up the Project
    • Set Up Docker
    • Document
    • Create a Release on GitHub
    • Contribute
  • Developper Explanations
    • Concrete Stack
    • Quantization
    • Using ONNX as IR for FHE Compilation
    • Hummingbird Usage
    • Skorch Usage
  • API
    • API
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  1. Installing

Installing

PreviousWhat is Concrete ML?NextScikit-learn

Last updated 2 years ago

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Concrete ML can be run on Linux based OSes as well as macOS on x86 CPUs. These hardware requirements are dictated by Concrete-Lib.

Do note that since WSL on Windows is a Linux based OS, Concrete ML will work as long as the package is not mandated in the /mnt/c/ directory, which corresponds to the host OS filesystem.

Python package

To install Concrete-ML from PyPi, run the following:

pip install concrete-ml

Note that concrete-ml installs concrete-numpy with all extras, including pygraphviz to draw graphs.

pygraphviz requires graphviz packages being installed on your OS, see

graphviz packages are binary packages that won't automatically be installed by pip. Do check for instructions on how to install graphviz for pygraphviz.

Docker image

You can also get the concrete-ml Docker image by either pulling the latest Docker image or a specific version:

docker pull zamafhe/concrete-ml:latest
# or
docker pull zamafhe/concrete-ml:v0.1.0

The image can be used with Docker volumes, .

You can then use this image with the following command:

# Without local volume:
docker run --rm -it -p 8888:8888 zamafhe/concrete-ml:v0.1.0

# With local volume to save notebooks on host:
docker run --rm -it -p 8888:8888 -v /host/path:/data zamafhe/concrete-ml:v0.1.0

This will launch a Concrete-ML enabled Jupyter server in Docker, that you can access from your browser.

Alternatively, you can just open a shell in Docker with or without volumes:

docker run --rm -it zamafhe/concrete-ml:v0.2.0 /bin/bash
https://pygraphviz.github.io/documentation/stable/install.html
https://pygraphviz.github.io/documentation/stable/install.html
see the Docker documentation here