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此仓库是为了提升国内下载速度的镜像仓库,每日同步一次。 原始仓库: https://github.com/databricks/mlflow
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.travis.yml 4.00 KB
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sudo: true
language: python
services:
- docker
matrix:
include:
- python: 2.7
- python: 3.6
- language: r
dist: trusty
cache: packages
before_install:
- export NOT_CRAN=true
- cd mlflow/R/mlflow
- Rscript -e 'install.packages("devtools")'
- Rscript -e 'devtools::install_deps(dependencies = TRUE)'
- cd ../../..
script:
- cd mlflow/R/mlflow
- R CMD build .
- R CMD check --no-build-vignettes --no-manual --no-tests mlflow*tar.gz
- cd tests
- export LINTR_COMMENT_BOT=false
- Rscript ../.travis.R
after_success:
- export COVR_RUNNING=true
- Rscript -e 'covr::codecov()'
- language: java
script:
- cd mlflow/java
- mvn clean package -q
- language: node_js
node_js:
- "node" # Use latest NodeJS: https://docs.travis-ci.com/user/languages/javascript-with-nodejs/#specifying-nodejs-versions
install:
script:
- cd mlflow/server/js
- npm i
- ./lint.sh
- npm test -- --coverage
install:
- sudo mkdir -p /travis-install
- sudo chown travis /travis-install
# (The conda installation steps below are taken from http://conda.pydata.org/docs/travis.html)
# We do this conditionally because it saves us some downloading if the
# version is the same.
- if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O /travis-install/miniconda.sh;
else
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O /travis-install/miniconda.sh;
fi
- bash /travis-install/miniconda.sh -b -p $HOME/miniconda
- export PATH="$HOME/miniconda/bin:$PATH"
- hash -r
- conda config --set always_yes yes --set changeps1 no
# Useful for debugging any issues with conda
- conda info -a
- conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION
- source activate test-environment
- python --version
- pip install --upgrade pip
# Install Python test dependencies only if we're running Python tests
- if [[ ! -z "$TRAVIS_PYTHON_VERSION" ]]; then
travis_wait pip install -r dev-requirements.txt -q;
travis_wait pip install -r test-requirements.txt -q;
fi
- pip install .
- export MLFLOW_HOME=$(pwd)
# Remove boto config present in Travis VMs (https://github.com/travis-ci/travis-ci/issues/7940)
- sudo rm -f /etc/boto.cfg
# Install protoc
- wget https://github.com/google/protobuf/releases/download/v3.6.0/protoc-3.6.0-linux-x86_64.zip -O /travis-install/protoc.zip
- sudo unzip /travis-install/protoc.zip -d /usr
script:
- ./lint.sh
- sudo ./test-generate-protos.sh
- pip list
- which mlflow
- echo $MLFLOW_HOME
# TODO(czumar): Re-enable container building and associated SageMaker tests once the container
# build process is no longer hanging
# - SAGEMAKER_OUT=$(mktemp)
# - if mlflow sagemaker build-and-push-container --no-push --mlflow-home . > $SAGEMAKER_OUT 2>&1; then
# echo "Sagemaker container build succeeded.";
# else
# echo "Sagemaker container build failed, output:";
# cat $SAGEMAKER_OUT;
# fi
# Run tests that don't leverage specific ML frameworks
- pytest --cov=mlflow --verbose --large --ignore=tests/h2o --ignore=tests/keras
--ignore=tests/pytorch --ignore=tests/pyfunc --ignore=tests/sagemaker --ignore=tests/sklearn
--ignore=tests/spark --ignore=tests/tensorflow
# Run ML framework tests in their own Python processes. TODO: find a better method of isolating
# tests.
- pytest --verbose tests/h2o --large
# TODO(smurching): Re-enable Keras tests once they're no longer flaky
# - pytest --verbose tests/keras --large
- pytest --verbose tests/pytorch --large
- pytest --verbose tests/pyfunc --large
- pytest --verbose tests/sagemaker --large
- pytest --verbose tests/sagemaker/mock --large
- pytest --verbose tests/sklearn --large
- pytest --verbose tests/spark --large
- pytest --verbose tests/tensorflow --large
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