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.gitlab-ci.yml
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stages:
- pylint_test
- dev_test
- pip_test
- pip_publish
- release_notes
pylintjob:
stage: pylint_test
except:
- 139-correct-toynet-demo-instructions
- 148-publish-niftynet-v0-2-0-on-python-package-index-pypi
- 170-add-niftynet-paper-on-rtd-doc
- 167-document-cli-option-for-path-to-new-networks
- 176-document-pip-installer-bundling-guidelines
- 174-design-a-workflow-that-allows-prs-from-github-to-be-merged
- 206-improve-the-handling-of-release-notes
script:
- pylint --rcfile=tests/pylintrc niftynet/engine
- pylint --rcfile=tests/pylintrc niftynet/io/image_*
testjob:
stage: dev_test
only:
- master
- dev
- dev-staging
- tags
script:
# !!kill coverage in case of hanging processes
- if pgrep coverage; then pkill -f coverage; fi
# print system info
- which nvidia-smi
- nvidia-smi
- pwd
- python -c "import tensorflow as tf; print tf.__version__"
- python -c "import tensorflow as tf; from tensorflow.python.client import device_lib; print device_lib.list_local_devices()"
- ls -la /dev | grep nvidia
- echo $(python tests/get_gpu_index.py)
- export CUDA_VISIBLE_DEVICES=$(python tests/get_gpu_index.py)
# download data
# - wget -q https://www.dropbox.com/s/y7mdh4m9ptkibax/example_volumes.tar.gz
# - tar -xzvf example_volumes.tar.gz
- wget -q https://www.dropbox.com/s/lioecnpv82r5n6e/example_volumes_v0_2.tar.gz
- tar -xzvf example_volumes_v0_2.tar.gz
# - wget -q https://www.dropbox.com/s/94wa4fl8f8k3aie/testing_data.tar.gz
# - tar -xzvf testing_data.tar.gz
- wget -q https://www.dropbox.com/s/p7b3t2c3mewtree/testing_data_v0_2.tar.gz
- tar -xzvf testing_data_v0_2.tar.gz
#### python 3 tests ###################################
# save NiftyNet folder path just in case
- export niftynet_dir=$(pwd)
# create a virtual env to dev-test
- venv="niftynet-dev-test-py3"
- mypython=$(which python3)
- virtualenv -p $mypython $venv
- cd $venv
- venv_dir=$(pwd)
- source bin/activate
# print Python version to CI output
- which python
- python --version
- cd $niftynet_dir
- pip install -r requirements-gpu.txt
# tests
- python net_segment.py train -c config/highres3dnet_config.ini --batch_size=1 --num_threads=2 --queue_length=40 --max_iter=10
- python net_segment.py inference -c config/highres3dnet_config.ini --batch_size 8 --spatial_window_size 64,64,64 --queue_length 64
- python net_segment.py train -c config/scalenet_config.ini --batch_size 1 --queue_length 5 --num_threads 2
- python net_segment.py inference -c config/scalenet_config.ini --batch_size 16 --spatial_window_size 64,64,64 --queue_length 32
- python net_segment.py train -c config/vnet_config.ini --batch_size 1 --queue_length 5 --num_threads 2 --activation_function relu
- python net_segment.py inference -c config/vnet_config.ini --batch_size 16 --spatial_window_size 64,64,64 --queue_length 32 --activation_function relu
# need a large GPU to run
#- python net_segment.py train -c config/unet_config.ini --batch_size 1 --queue_length 5 --num_threads 2
#- python net_segment.py inference -c config/unet_config.ini --batch_size 1 --spatial_window_size 96,96,96 --queue_length 5
#- python net_segment.py train -c config/deepmedic_config.ini --batch_size 128 --queue_length 48 --num_threads 4
#- python net_segment.py inference -c config/deepmedic_config.ini --batch_size 12 --spatial_window_size 135,135,135 --queue_length 128
- python net_segment.py train -c config/default_segmentation.ini --batch_size 3 --queue_length 6
- python net_segment.py train -c config/default_segmentation.ini --batch_size 3 --queue_length 6 --starting_iter 0 --max_iter 15
- python net_segment.py inference -c config/default_segmentation.ini --spatial_window_size 84,84,84 --batch_size 7 --queue_length 14 --inference_iter 14
- python net_segment.py train -c config/default_multimodal_segmentation.ini --batch_size 3
- python net_segment.py inference -c config/default_multimodal_segmentation.ini --spatial_window_size 64,64 --batch_size 7
- python net_regress.py train -c config/default_monomodal_regression.ini --batch_size=1 --name toynet --max_iter 10
- python net_regress.py inference -c config/default_monomodal_regression.ini --batch_size=7 --name toynet --spatial_window_size 84,84,84
- python net_gan.py train -c config/GAN_demo_train_config.ini --max_iter 5
- python net_gan.py inference -c config/GAN_demo_train_config.ini
- python net_autoencoder.py train -c config/vae_config.ini --max_iter 5
- python net_autoencoder.py inference -c config/vae_config.ini --inference_type sample
- python net_autoencoder.py inference -c config/vae_config.ini --inference_type encode --save_seg_dir output/vae_demo_features
- python net_autoencoder.py inference -c config/vae_config.ini --inference_type encode-decode
- python -m unittest discover -s "tests" -p "*_test.py"
# deactivate virtual environment
- deactivate
- cd $niftynet_dir
###############end of python3
######### Python 2 ###################### run python2 code with coverage wrapper
- coverage run -a --source . net_segment.py train -c config/highres3dnet_config.ini --batch_size=1 --num_threads=2 --queue_length=40 --max_iter=10
- coverage run -a --source . net_segment.py inference -c config/highres3dnet_config.ini --batch_size 8 --spatial_window_size 64,64,64 --queue_length 64
- coverage run -a --source . net_segment.py train -c config/scalenet_config.ini --batch_size 1 --queue_length 5 --num_threads 2
- coverage run -a --source . net_segment.py inference -c config/scalenet_config.ini --batch_size 16 --spatial_window_size 64,64,64 --queue_length 32
- coverage run -a --source . net_segment.py train -c config/vnet_config.ini --batch_size 1 --queue_length 5 --num_threads 2 --activation_function relu
- coverage run -a --source . net_segment.py inference -c config/vnet_config.ini --batch_size 16 --spatial_window_size 64,64,64 --queue_length 32 --activation_function relu
# need a large GPU to run
#- coverage run -a --source . net_segment.py train -c config/unet_config.ini --batch_size 1 --queue_length 5 --num_threads 2
#- coverage run -a --source . net_segment.py inference -c config/unet_config.ini --batch_size 1 --spatial_window_size 96,96,96 --queue_length 5
#- coverage run -a --source . net_segment.py train -c config/deepmedic_config.ini --batch_size 128 --queue_length 48 --num_threads 4
#- coverage run -a --source . net_segment.py inference -c config/deepmedic_config.ini --batch_size 12 --spatial_window_size 135,135,135 --queue_length 128
- coverage run -a --source . net_segment.py train -c config/default_segmentation.ini --batch_size 3 --queue_length 6
- coverage run -a --source . net_segment.py train -c config/default_segmentation.ini --batch_size 3 --queue_length 6 --starting_iter 0 --max_iter 15
- coverage run -a --source . net_segment.py inference -c config/default_segmentation.ini --spatial_window_size 84,84,84 --batch_size 7 --queue_length 14 --inference_iter 14
- coverage run -a --source . net_segment.py train -c config/default_multimodal_segmentation.ini --batch_size 3
- coverage run -a --source . net_segment.py inference -c config/default_multimodal_segmentation.ini --spatial_window_size 64,64 --batch_size 7
- coverage run -a --source . net_regress.py train -c config/default_monomodal_regression.ini --max_iter 10 --name toynet --batch_size=2
- coverage run -a --source . net_run.py train -a net_regress -c config/default_monomodal_regression.ini --max_iter 10 --name toynet --batch_size=2
- coverage run -a --source . net_regress.py inference -c config/default_monomodal_regression.ini --name toynet --spatial_window_size 84,84,84 --batch_size 7
- coverage run -a --source . net_gan.py train -c config/GAN_demo_train_config.ini --max_iter 5
- coverage run -a --source . net_run.py train -a net_gan -c config/GAN_demo_train_config.ini --max_iter 5
- coverage run -a --source . net_gan.py inference -c config/GAN_demo_train_config.ini
- coverage run -a --source . net_autoencoder.py train -c config/vae_config.ini --max_iter 5
- coverage run -a --source . net_autoencoder.py inference -c config/vae_config.ini --inference_type sample
- coverage run -a --source . net_autoencoder.py inference -c config/vae_config.ini --inference_type encode --save_seg_dir output/vae_demo_features
- coverage run -a --source . net_autoencoder.py inference -c config/vae_config.ini --inference_type encode-decode
- coverage run -a --source . net_download.py dense_vnet_abdominal_ct_model_zoo -r
- coverage run -a --source . -m unittest discover -s "tests" -p "*_test.py"
- coverage report -m
- echo 'finished test'
tags:
- gift-linux
quicktest:
stage: dev_test
except:
- master
- dev
- dev-staging
- tags
- 177-merging-net_regress-to-dev
- 112-publish-api-docs-online
- 147-revise-contribution-guidelines-to-include-github
- 150-properly-format-the-bibtex-entry-to-the-ipmi-2017-paper-on-the-main-readme
- 139-correct-toynet-demo-instructions
- 148-publish-niftynet-v0-2-0-on-python-package-index-pypi
- 170-add-niftynet-paper-on-rtd-doc
- 167-document-cli-option-for-path-to-new-networks
- 176-document-pip-installer-bundling-guidelines
- 174-design-a-workflow-that-allows-prs-from-github-to-be-merged
- 206-improve-the-handling-of-release-notes
script:
# print system info
- which nvidia-smi
- nvidia-smi
- pwd
- python -c "import tensorflow as tf; print tf.__version__"
#- python -c "import tensorflow as tf; from tensorflow.python.client import device_lib; print device_lib.list_local_devices()"
- ls -la /dev | grep nvidia
- echo $(python tests/get_gpu_index.py)
- export CUDA_VISIBLE_DEVICES=$(python tests/get_gpu_index.py)
# download data
# - wget -q https://www.dropbox.com/s/y7mdh4m9ptkibax/example_volumes.tar.gz
# - tar -xzvf example_volumes.tar.gz
- wget -q https://www.dropbox.com/s/lioecnpv82r5n6e/example_volumes_v0_2.tar.gz
- tar -xzvf example_volumes_v0_2.tar.gz
#- wget -q https://www.dropbox.com/s/94wa4fl8f8k3aie/testing_data.tar.gz
#- tar -xzvf testing_data.tar.gz
- wget -q https://www.dropbox.com/s/p7b3t2c3mewtree/testing_data_v0_2.tar.gz
- tar -xzvf testing_data_v0_2.tar.gz
- coverage erase
# run only fast tests
- QUICKTEST=True coverage run -a --source . -m unittest discover -s "tests" -p "*_test.py"
- coverage report -m
# run global config tests
# These need to be run separately because NiftyNetGlobalConfig is a singleton, AND
# its operations pertain to a global configuration file (~/.niftynet/config.ini).
- GLOBAL_CONFIG_TEST_gcs=True python -m unittest tests.niftynet_global_config_test
- GLOBAL_CONFIG_TEST_necfc=True python -m unittest tests.niftynet_global_config_test
- GLOBAL_CONFIG_TEST_ecfl=True python -m unittest tests.niftynet_global_config_test
- GLOBAL_CONFIG_TEST_icfbu=True python -m unittest tests.niftynet_global_config_test
- GLOBAL_CONFIG_TEST_nenhc=True python -m unittest tests.niftynet_global_config_test
- GLOBAL_CONFIG_TEST_enhnt=True python -m unittest tests.niftynet_global_config_test
- echo 'finished quick tests'
tags:
- gift-linux
pip-installer:
stage: pip_test
only:
- master
- dev
- dev-staging
- tags
- 177-merging-net_regress-to-dev
- 117-support-for-user-defined-networks-using-pip-installed-niftynet
script:
# get the shortened version of last commit's hash
- LAST_COMMIT=$(git rev-parse --short HEAD)
# source utils
- source ci/utils.sh
# following three lines copied over from dev script:
- ls -la /dev | grep nvidia
- echo $(python tests/get_gpu_index.py)
- export CUDA_VISIBLE_DEVICES=$(python tests/get_gpu_index.py)
# create a Python file that will import all available packages from the pip installer
- package_importer="$(pwd)/import_niftynet_packages.py"
# traverse the file hierarchy recursively to discover all packages
- find niftynet -type f \( ! -name . \) -print | grep '.py$' | grep -v __init__ | sed 's/\.\.\///g;s/\//\./g;s/\.py//g;s/^niftynet/import niftynet/g' > $package_importer
# save NiftyNet folder path just in case
- export niftynet_dir=$(pwd)
# create the NiftyNet wheel
- rm -rf dist # remove dist directory, just in case
- sh ci/bundlewheel.sh
- source ci/findwheel.sh
- echo $niftynet_wheel
# ============= Python 2 ============================
# create a virtual env to test pip installer
- venv="niftynet-pip-installer-venv-py2"
- mypython=$(which python2)
- virtualenv -p $mypython $venv
- cd $venv
- venv_dir=$(pwd)
- source bin/activate
# print Python version to CI output
- which python
- python --version
# NiftyNet console entries should fail gracefully if TF not installed
# i.e. check that the warning displays the TF website
- cd $niftynet_dir
- set +e
- python -c "import niftynet" 2>&1 | grep "https://www.tensorflow.org/"
- set -e
- cd $venv_dir
# install TF
- pip install tensorflow-gpu==1.3
# install using built NiftyNet wheel
- pip install $niftynet_wheel
# install SimpleITK for package importer test to work properly
- pip install simpleitk
# create symlink to required datasets
- ln -s /home/gitlab-runner/environments/niftynet/data/example_volumes ./example_volumes
# check user-defined networks discovered
# 1) create configuration + NiftyNet home
- CONFIG_HOME=~/.niftynet
- mkdir -p $CONFIG_HOME
- NIFTYNET_HOME=~/niftynet-$LAST_COMMIT
- mkdir $NIFTYNET_HOME
- GLOBAL_CONFIG=$CONFIG_HOME/config.ini
- echo "[global]" > $GLOBAL_CONFIG
- echo "home = $NIFTYNET_HOME" >> $GLOBAL_CONFIG
- cat $GLOBAL_CONFIG
# 2) create extension hierarchy
- mkdir -p $NIFTYNET_HOME/niftynetext/network
- touch $NIFTYNET_HOME/niftynetext/__init__.py
- touch $NIFTYNET_HOME/niftynetext/network/__init__.py
# 3) replicate working ToyNet example
- TOYNET_REPLICA=$NIFTYNET_HOME/niftynetext/network/notoynet.py
- cp $niftynet_dir/niftynet/network/toynet.py $TOYNET_REPLICA
- sed -i 's/ToyNet/NoToyNet/g' $TOYNET_REPLICA
# 4) run e.g. segmentation using ToyNet replica
- net_segment train -c $niftynet_dir/config/default_segmentation.ini --name niftynetext.network.notoynet.NoToyNet --batch_size 3 --max_iter 5
- net_segment inference -c $niftynet_dir/config/default_segmentation.ini --name niftynetext.network.notoynet.NoToyNet --spatial_window_size 80,80,80 --batch_size 8
# 5) clean up created config + NiftyNet home hierarchy
- rm -rf $CONFIG_HOME
- unset CONFIG_HOME
- rm -rf $NIFTYNET_HOME
- unset NIFTYNET_HOME
# check whether all packages are importable
- cat $package_importer
- python $package_importer
# test niftynet command
- net_segment train -c $niftynet_dir/config/default_segmentation.ini --name toynet --batch_size 3 --max_iter 5
- net_segment inference -c $niftynet_dir/config/default_segmentation.ini --name toynet --spatial_window_size 80,80,80 --batch_size 8
- net_run train --app net_segment -c $niftynet_dir/config/default_segmentation.ini --name toynet --batch_size 3 --max_iter 5
- net_run inference --app net_segment -c $niftynet_dir/config/default_segmentation.ini --name toynet --spatial_window_size 80,80,80 --batch_size 8
- net_regress train -c $niftynet_dir/config/default_monomodal_regression.ini --max_iter 10 --name toynet --batch_size=2
- net_regress inference -c $niftynet_dir/config/default_monomodal_regression.ini --name toynet --spatial_window_size 84,84,84 --batch_size 7
- net_run train -a net_regress -c $niftynet_dir/config/default_monomodal_regression.ini --max_iter 10 --name toynet --batch_size=2
- net_run inference -a net_regress -c $niftynet_dir/config/default_monomodal_regression.ini --name toynet --spatial_window_size 84,84,84 --batch_size 7
- net_gan train -c $niftynet_dir/config/GAN_demo_train_config.ini --max_iter 5
- net_gan inference -c $niftynet_dir/config/GAN_demo_train_config.ini
- net_run train --app net_gan -c $niftynet_dir/config/GAN_demo_train_config.ini --max_iter 5
- net_run inference --app net_gan -c $niftynet_dir/config/GAN_demo_train_config.ini
- net_autoencoder train -c $niftynet_dir/config/vae_config.ini --max_iter 5
- net_autoencoder inference -c $niftynet_dir/config/vae_config.ini --inference_type sample
- net_autoencoder inference -c $niftynet_dir/config/vae_config.ini --inference_type encode --save_seg_dir output/vae_demo_features
- net_autoencoder inference -c $niftynet_dir/config/vae_config.ini --inference_type encode-decode
- net_run train --app net_autoencoder -c $niftynet_dir/config/vae_config.ini --max_iter 5
- net_run inference --app net_autoencoder -c $niftynet_dir/config/vae_config.ini --inference_type sample
- net_run inference --app net_autoencoder -c $niftynet_dir/config/vae_config.ini --inference_type encode --save_seg_dir output/vae_demo_features
- net_run inference --app net_autoencoder -c $niftynet_dir/config/vae_config.ini --inference_type encode-decode
- net_download dense_vnet_abdominal_ct_model_zoo -r
# deactivate virtual environment
- deactivate
- cd $niftynet_dir
# ============= Python 3 ============================
# create a virtual env to test pip installer
- venv="niftynet-pip-installer-venv-py3"
- mypython=$(which python3)
- virtualenv -p $mypython $venv
- cd $venv
- venv_dir=$(pwd)
- source bin/activate
# print Python version to CI output
- which python
- python --version
# NiftyNet console entries should fail gracefully if TF not installed
# i.e. check that the warning displays the TF website
- cd $niftynet_dir
- set +e
- python -c "import niftynet" 2>&1 | grep "https://www.tensorflow.org/"
- set -e
- cd $venv_dir
# install TF
- pip install tensorflow-gpu==1.3
# install using built NiftyNet wheel
- pip install $niftynet_wheel
# install SimpleITK for package importer test to work properly
- pip install simpleitk
# check whether all packages are importable
- cat $package_importer
- python $package_importer
# test niftynet command
- ln -s /home/gitlab-runner/environments/niftynet/data/example_volumes ./example_volumes
- net_segment train -c $niftynet_dir/config/default_segmentation.ini --name toynet --batch_size 3 --max_iter 5
- net_segment inference -c $niftynet_dir/config/default_segmentation.ini --name toynet --spatial_window_size 80,80,80 --batch_size 8
- net_run train --app net_segment -c $niftynet_dir/config/default_segmentation.ini --name toynet --batch_size 3 --max_iter 5
- net_run inference --app net_segment -c $niftynet_dir/config/default_segmentation.ini --name toynet --spatial_window_size 80,80,80 --batch_size 8
- net_regress train -c $niftynet_dir/config/default_monomodal_regression.ini --max_iter 10 --name toynet --batch_size=2
- net_regress inference -c $niftynet_dir/config/default_monomodal_regression.ini --name toynet --spatial_window_size 84,84,84 --batch_size 7
- net_run train -a net_regress -c $niftynet_dir/config/default_monomodal_regression.ini --max_iter 10 --name toynet --batch_size=2
- net_run inference -a net_regress -c $niftynet_dir/config/default_monomodal_regression.ini --name toynet --spatial_window_size 84,84,84 --batch_size 7
- net_gan train -c $niftynet_dir/config/GAN_demo_train_config.ini --max_iter 5
- net_gan inference -c $niftynet_dir/config/GAN_demo_train_config.ini
- net_run train --app net_gan -c $niftynet_dir/config/GAN_demo_train_config.ini --max_iter 5
- net_run inference --app net_gan -c $niftynet_dir/config/GAN_demo_train_config.ini
- net_autoencoder train -c $niftynet_dir/config/vae_config.ini --max_iter 5
- net_autoencoder inference -c $niftynet_dir/config/vae_config.ini --inference_type sample
- net_autoencoder inference -c $niftynet_dir/config/vae_config.ini --inference_type encode --save_seg_dir output/vae_demo_features
- net_autoencoder inference -c $niftynet_dir/config/vae_config.ini --inference_type encode-decode
- net_run train --app net_autoencoder -c $niftynet_dir/config/vae_config.ini --max_iter 5
- net_run inference --app net_autoencoder -c $niftynet_dir/config/vae_config.ini --inference_type sample
- net_run inference --app net_autoencoder -c $niftynet_dir/config/vae_config.ini --inference_type encode --save_seg_dir output/vae_demo_features
- net_run inference --app net_autoencoder -c $niftynet_dir/config/vae_config.ini --inference_type encode-decode
- net_download dense_vnet_abdominal_ct_model_zoo -r
# deactivate virtual environment
- deactivate
- cd $niftynet_dir
tags:
- gift-adelie
pip-camera-ready:
stage: pip_publish
only:
- tags
script:
# Copy wheel created in previous stage to a specific location on GIFT-Adelie
- export niftynet_dir=$(pwd)
# create the NiftyNet wheel
- rm -rf dist # remove dist directory, just in case
- sh ci/bundlewheel.sh
- source ci/findwheel.sh
- echo $niftynet_wheel
# Creat camera-ready folder if doesn't exist
- camera_ready_dir=/home/gitlab-runner/environments/niftynet/pip/camera-ready
- mkdir -p $camera_ready_dir
- ls -lrtha $camera_ready_dir
# Clean up the camera-ready folder if already there
- rm -rf $camera_ready_dir/*.whl
# Finally do copy
- cp $niftynet_wheel $camera_ready_dir
- ls -lrtha $camera_ready_dir
# Instruct developer which file to publish
- echo "Camera-ready pip installer bundle (wheel) created:"
- echo "$(ls $camera_ready_dir/*.whl)"
tags:
- gift-adelie
release-notes:
stage: release_notes
only:
- tags
script:
- echo $CI_COMMIT_REF_NAME
- echo $CI_COMMIT_TAG
# Fail if nothing's been added to changelog for this version
- echo "Checking changelog modified for current release ..."
- grep ${CI_COMMIT_TAG:1} CHANGELOG.md
- echo "... DONE"
# Do actually push release notes to GitHub
- |
if [[ "$CI_COMMIT_TAG" == v* ]]; then
echo "Doing a chandler push for $CI_COMMIT_TAG ..."
chandler push $CI_COMMIT_TAG --github=NifTK/NiftyNet
echo "... DONE"
fi
tags:
- gift-adelie