scikit-learn/build_tools/travis/install.sh

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#!/bin/bash
# This script is meant to be called by the "install" step defined in
# .travis.yml. See http://docs.travis-ci.com/ for more details.
# The behavior of the script is controlled by environment variabled defined
# in the .travis.yml in the top level folder of the project.
# License: 3-clause BSD
# Travis clone scikit-learn/scikit-learn repository in to a local repository.
# We use a cached directory with three scikit-learn repositories (one for each
# matrix entry) from which we pull from local Travis repository. This allows
# us to keep build artefact for gcc + cython, and gain time
set -e
# Fix the compilers to workaround avoid having the Python 3.4 build
# lookup for g++44 unexpectedly.
export CC=gcc
export CXX=g++
echo 'List files from cached directories'
echo 'pip:'
ls $HOME/.cache/pip
if [[ "$DISTRIB" == "conda" ]]; then
# Deactivate the travis-provided virtual environment and setup a
# conda-based environment instead
deactivate
# Use the miniconda installer for faster download / install of conda
# itself
pushd .
cd
mkdir -p download
cd download
echo "Cached in $HOME/download :"
ls -l
echo
if [[ ! -f miniconda.sh ]]
then
wget http://repo.continuum.io/miniconda/Miniconda-3.6.0-Linux-x86_64.sh \
-O miniconda.sh
fi
chmod +x miniconda.sh && ./miniconda.sh -b
cd ..
export PATH=/home/travis/miniconda/bin:$PATH
conda update --yes conda
popd
# Configure the conda environment and put it in the path using the
# provided versions
if [[ "$INSTALL_MKL" == "true" ]]; then
conda create -n testenv --yes python=$PYTHON_VERSION pip nose \
numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION numpy scipy \
cython=$CYTHON_VERSION libgfortran mkl
else
conda create -n testenv --yes python=$PYTHON_VERSION pip nose \
numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION cython=$CYTHON_VERSION \
libgfortran nomkl
fi
source activate testenv
# Install nose-timer via pip
pip install nose-timer
elif [[ "$DISTRIB" == "ubuntu" ]]; then
# At the time of writing numpy 1.9.1 is included in the travis
# virtualenv but we want to use the numpy installed through apt-get
# install.
deactivate
# Create a new virtualenv using system site packages for python, numpy
# and scipy
virtualenv --system-site-packages testvenv
source testvenv/bin/activate
pip install nose nose-timer cython
elif [[ "$DISTRIB" == "scipy-dev-wheels" ]]; then
# Set up our own virtualenv environment to avoid travis' numpy.
# This venv points to the python interpreter of the travis build
# matrix.
virtualenv --python=python ~/testvenv
source ~/testvenv/bin/activate
pip install --upgrade pip setuptools
# We use the default Python virtualenv provided by travis
echo "Installing numpy master wheel"
pip install --pre --upgrade --no-index --timeout=60 \
--trusted-host travis-dev-wheels.scipy.org \
-f https://travis-dev-wheels.scipy.org/ numpy scipy
pip install nose nose-timer cython
fi
if [[ "$COVERAGE" == "true" ]]; then
pip install coverage coveralls
fi
if [ ! -d "$CACHED_BUILD_DIR" ]; then
mkdir -p $CACHED_BUILD_DIR
fi
rsync -av --exclude '.git/' --exclude='testvenv/' \
$TRAVIS_BUILD_DIR $CACHED_BUILD_DIR
cd $CACHED_BUILD_DIR/scikit-learn
# Build scikit-learn in the install.sh script to collapse the verbose
# build output in the travis output when it succeeds.
python --version
python -c "import numpy; print('numpy %s' % numpy.__version__)"
python -c "import scipy; print('scipy %s' % scipy.__version__)"
python setup.py develop