auto-sklearn has the following system requirements:
Linux operating system (for example Ubuntu) (get Linux here),
Python (>=3.6) (get Python here).
C++ compiler (with C++11 supports) (get GCC here) and
SWIG (version 3.0.* is required; >=4.0.0 is not supported) (get SWIG here).
For an explanation of missing Microsoft Windows and MAC OSX support please check the Section Windows/OSX compatibility.
Please install all dependencies manually with:
curl https://raw.githubusercontent.com/automl/auto-sklearn/master/requirements.txt | xargs -n 1 -L 1 pip3 install
Then install auto-sklearn:
pip3 install auto-sklearn
pip3 installation command fails, make sure you have the System requirements installed correctly.
To provide a C++11 building environment and the lateste SWIG version on Ubuntu, run:
sudo apt-get install build-essential swig
Anaconda does not ship auto-sklearn, and there are no conda packages for auto-sklearn. Thus, it is easiest to install auto-sklearn as detailed in the Section Installing auto-sklearn.
A common installation problem under recent Linux distribution is the incompatibility of the compiler version used to compile the Python binary shipped by AnaConda and the compiler installed by the distribution. This can be solved by installing the gcc compiler shipped with AnaConda (as well as swig):
conda install gxx_linux-64 gcc_linux-64 swig
auto-sklearn relies heavily on the Python module
is part of Python’s Unix Specific Services
and not available on a Windows machine. Therefore, it is not possible to run
auto-sklearn on a Windows machine.
Possible solutions (not tested):
Windows 10 bash shell
We currently do not know if auto-sklearn works on OSX. There are at least two issues holding us back from actively supporting OSX:
resourcemodule cannot enforce a memory limit on a Python process (see SMAC3/issues/115).
Possible other solutions (not tested):
A Docker image is also provided on dockerhub. To download from dockerhub, use:
docker pull mfeurer/auto-sklearn:master
You can also verify that the image was downloaded via:
docker images # Verify that the image was downloaded
This image can be used to start an interactive session as follows:
docker run -it mfeurer/auto-sklearn:master
To start a Jupyter notebook, you could instead run e.g.:
docker run -it -v $PWD:/opt/nb -p 8888:8888 mfeurer/auto-sklearn:master /bin/bash -c "mkdir -p /opt/nb && jupyter notebook --notebook-dir=/opt/nb --ip='0.0.0.0' --port=8888 --no-browser --allow-root"
Alternatively, it is possible to use the development version of auto-sklearn by replacing all