Install

Supported Platforms

Falkon is only tested on Linux.

GPU support is achieved via CUDA, so using Falkon from Windows will be tricky.

Using the library from the CPU only is much more portable since there is no CUDA requirement, and it will likely work on Windows and Mac OS more easily.

Prerequisites

PyTorch and CUDA

Falkon depends on PyTorch, and on the NVIDIA Toolkit (for NVIDIA GPU support) which is usually installed alongside PyTorch. PyTorch can be installed in several ways by following the instructions at Install PyTorch. If GPU support is desired, make sure that PyTorch CUDA bindings are working:

import torch
assert torch.cuda.is_available()

Intel MKL

If Falkon is not installed with GPU support, it will try to link to Intel MKL to speed-up certain sparse operations. MKL shared libraries are usually distributed with numpy, so this should not be a problem. In case sparse matrix support is not needed, the MKL library will not be loaded.

Installing

There are three ways of installing Falkon:

  1. From source by running

    $ pip install git+https://github.com/falkonml/falkon.git

  2. From pypi with JIT compilation (the C++ extension will be compiled when the library is first used):

    $ pip install falkon

  3. From pre-built wheels which are available for the following versions of PyTorch and CUDA:

    Linux

    cu113

    cu115

    cu116

    cu117

    cu118

    torch 1.11.0

    ✅

    ✅

    torch 1.13.0

    ✅

    ✅

    torch 2.0.0

    ✅

    ✅

    As an example, if you already have installed PyTorch 1.13 and CUDA 11.7 on your system, you should run

    $ pip install falkon -f https://falkon.dibris.unige.it/torch-1.13.0_cu117.html

    Similarly for CPU packages

    $ pip install falkon -f https://falkon.dibris.unige.it/torch-2.0.0_cpu.html

    please check here for a list of supported wheels.

For options 1 and 2, you will need the CUDA toolkit to be setup properly on your system in order to compile the sources. Compilation may take a few minutes. To speed it up you can try to install ninja (pip install ninja) which parallelizes the build process.

Testing the installation

To check that everything works correctly you can follow the Kernel ridge regression notebook.

Development

For development purposes the library should be installed in editable mode (i.e. pip install -e . from the falkon directory.

To build the documentation go into the doc directory and run make html.