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.appveyor.yml
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.appveyor.yml
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# AppVeyor.com is a Continuous Integration service to build and run tests under
# Windows
# https://ci.appveyor.com/project/artzet-s/eartrack
platform:
- x86
- x64
environment:
global:
# SDK v7.0 MSVC Express 2008's SetEnv.cmd script will fail if the
# /E:ON and /V:ON options are not enabled in the batch script interpreter
# See: http://stackoverflow.com/a/13751649/163740
CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\build_tools\\appveyor\\run_with_env.cmd"
# WHEELHOUSE_UPLOADER_USERNAME: sklearn-appveyor
# WHEELHOUSE_UPLOADER_SECRET:
# secure: BQm8KfEj6v2Y+dQxb2syQvTFxDnHXvaNktkLcYSq7jfbTOO6eH9n09tfQzFUVcWZ
# Make sure we don't download large datasets when running the test on
# continuous integration platform
SKLEARN_SKIP_NETWORK_TESTS: 1
matrix:
- PYTHON: "C:\\Miniconda2-x86"
PYTHON_VERSION: "2.7"
PYTHON_ARCH: "32"
- PYTHON: "C:\\Miniconda2-x64"
PYTHON_VERSION: "2.7"
PYTHON_ARCH: "64"
# Because we only have a single worker, we don't want to waste precious
# appveyor CI time and make other PRs wait for repeated failures in a failing
# PR. The following option cancels pending jobs in a given PR after the first
# job failure in that specific PR.
matrix:
exclude:
- platform: x86
PYTHON: "C:\\Miniconda2-x64"
PYTHON_VERSION: "2.7"
PYTHON_ARCH: "64"
fast_finish: true
install:
- "powershell ./build_tools/appveyor/install.ps1"
- "SET PATH=%PYTHON%;%PYTHON%\\Scripts;%PATH%"
- "conda config --set always_yes yes --set changeps1 no"
- "conda update -q conda"
- "conda info -a"
- "conda create -q -n test-environment python"
- "activate test-environment"
- "conda install -c openalea -c conda-forge openalea.deploy openalea.core numpy matplotlib opencv scikit-image pywin32"
- "python setup.py install"
# # Install the build and runtime dependencies of the project.
# - "%CMD_IN_ENV% pip install --timeout=60 --trusted-host 28daf2247a33ed269873-7b1aad3fab3cc330e1fd9d109892382a.r6.cf2.rackcdn.com -r build_tools/appveyor/requirements.txt"
# - "%CMD_IN_ENV% python setup.py bdist_wheel bdist_wininst -b doc/logos/scikit-learn-logo.bmp"
# - ps: "ls dist"
#
# # Install the generated wheel package to test it
# - "pip install --pre --no-index --find-links dist/ scikit-learn"
# Not a .NET project, we build scikit-learn in the install step instead
build: false
test_script:
- "conda install -c conda-forge nose"
- "nosetests"
#artifacts:
# # Archive the generated wheel package in the ci.appveyor.com build report.
# - path: dist\*
#on_success:
# # Upload the generated wheel package to Rackspace
# # On Windows, Apache Libcloud cannot find a standard CA cert bundle so we
# # disable the ssl checks.
# - "python -m wheelhouse_uploader upload --no-ssl-check --local-folder=dist sklearn-windows-wheels"
#
#notifications:
# - provider: Webhook
# url: https://webhooks.gitter.im/e/0dc8e57cd38105aeb1b4
# on_build_success: false
# on_build_failure: True
#
#cache:
# # Use the appveyor cache to avoid re-downloading large archives such
# # the MKL numpy and scipy wheels mirrored on a rackspace cloud
# # container, speed up the appveyor jobs and reduce bandwidth
# # usage on our rackspace account.
# - '%APPDATA%\pip\Cache'