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 https://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
# Fail fast
echo "CPU Arch: ${TRAVIS_CPU_ARCH}"
# jq is used in travis_fastfail.sh, it's already pre-installed in non arm64
# environments
sudo apt-get install jq
build_tools/travis/travis_fastfail.sh
# Imports get_dep
source build_tools/shared.sh
echo "List files from cached directories"
echo "pip:"
ls $HOME/.cache/pip
export CC=/usr/lib/ccache/gcc
export CXX=/usr/lib/ccache/g++
# Useful for debugging how ccache is used
# export CCACHE_LOGFILE=/tmp/ccache.log
# ~60M is used by .ccache when compiling from scratch at the time of writing
ccache --max-size 100M --show-stats
# Deactivate the travis-provided virtual environment and setup a
# conda-based environment instead
# If Travvis has language=generic, deactivate does not exist. `|| :` will pass.
deactivate || :
# Install miniconda
if [[ "$TRAVIS_CPU_ARCH" == "arm64" ]]; then
wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-aarch64.sh -O miniconda.sh
else
fname=Miniconda3-latest-Linux-x86_64.sh
wget https://repo.continuum.io/miniconda/$fname -O miniconda.sh
fi
MINICONDA_PATH=$HOME/miniconda
chmod +x miniconda.sh && ./miniconda.sh -b -p $MINICONDA_PATH
export PATH=$MINICONDA_PATH/bin:$PATH
conda update --yes conda
# Create environment and install dependencies
conda create -n testenv --yes python=3.7
source activate testenv
if [[ "$TRAVIS_CPU_ARCH" == "amd64" ]]; then
pip install --upgrade pip setuptools
echo "Installing numpy and scipy master wheels"
dev_anaconda_url=https://pypi.anaconda.org/scipy-wheels-nightly/simple
pip install --pre --upgrade --timeout=60 --extra-index $dev_anaconda_url numpy scipy pandas
pip install --pre cython
echo "Installing joblib master"
pip install https://github.com/joblib/joblib/archive/master.zip
echo "Installing pillow master"
pip install https://github.com/python-pillow/Pillow/archive/master.zip
else
conda install -y scipy numpy pandas cython
pip install joblib threadpoolctl
fi
pip install $(get_dep pytest $PYTEST_VERSION) pytest-cov pytest-xdist
# Build scikit-learn in the install.sh script to collapse the verbose
# build output in the travis output when it succeeds.
python --version
python -c "import numpy; print('numpy %s' % numpy.__version__)"
python -c "import scipy; print('scipy %s' % scipy.__version__)"
if [[ "$BUILD_WITH_ICC" == "true" ]]; then
wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2023.PUB
sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2023.PUB
rm GPG-PUB-KEY-INTEL-SW-PRODUCTS-2023.PUB
sudo add-apt-repository "deb https://apt.repos.intel.com/oneapi all main"
sudo apt-get update
sudo apt-get install intel-oneapi-icc
source /opt/intel/inteloneapi/setvars.sh
# The build_clib command is implicitly used to build libsvm-skl. To compile
# with a different compiler we also need to specify the compiler for this
# command.
python setup.py build_ext --compiler=intelem -i -j "${CI_CPU_COUNT}" build_clib --compiler=intelem
else
# Use setup.py instead of `pip install -e .` to be able to pass the -j flag
# to speed-up the building multicore CI machines.
python setup.py build_ext --inplace -j "${CI_CPU_COUNT}"
fi
python setup.py develop
ccache --show-stats
# Useful for debugging how ccache is used
# cat $CCACHE_LOGFILE
# fast fail
build_tools/travis/travis_fastfail.sh