Concrete ML
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0.3
0.3
  • What is Concrete ML?
  • Getting Started
    • Installation
    • Key Concepts
  • Built-in Models
    • Linear Models
    • Tree-based Models
    • Neural Networks
    • Examples
  • Deep Learning
    • Using Torch
    • Using ONNX
    • Examples
    • Debugging Models
  • Advanced topics
    • Quantization
    • Pruning
    • Production Deployment
    • Compilation
    • More about ONNX
    • FHE Op-graphs
    • Using Hummingbird
    • Using Skorch
  • Developer Guide
    • Set Up the Project
    • Set Up Docker
    • Documentation
    • Support and Issues
    • Contributing
    • API
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On this page
  • Using PyPi
  • Requirements
  • Installation
  • Using Docker

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  1. Getting Started

Installation

PreviousWhat is Concrete ML?NextKey Concepts

Last updated 2 years ago

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Using PyPi

Requirements

Installing Concrete-ML using PyPi requires a Linux-based OS or macOS running on an x86 CPU. For Apple Silicon, Docker is the only currently supported option (see ).

Installing on Windows can be done using Docker or WSL. On WSL, Concrete-ML will work as long as the package is not installed in the /mnt/c/ directory, which corresponds to the host OS filesystem.

Installation

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

pip install -U pip wheel setuptools
pip install concrete-ml

Using Docker

Concrete-ml can be installed using Docker by either pulling the latest image or a specific version:

docker pull zamafhe/concrete-ml:latest
# or
docker pull zamafhe/concrete-ml:v0.3.0

The image can be used with Docker volumes, .

The image can then be used via the following command:

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

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

This will launch a Concrete-ML enabled Jupyter server in Docker that can be accessed directly from a browser.

Alternatively, a shell can be lauched in Docker, with or without volumes:

docker run --rm -it zamafhe/concrete-ml /bin/bash
see the Docker documentation here
below