lightning/.drone.yml

# https://docs.drone.io/pipeline/docker/examples/languages/python/#python-example

kind: pipeline
type: docker
name: torch-GPU

steps:
- name: testing
  image: pytorchlightning/pytorch_lightning:devel-pt_1_4

  environment:
    SLURM_LOCALID: 0
    CODECOV_TOKEN:
      from_secret: codecov_token
    HOROVOD_GPU_ALLREDUCE: NCCL
    HOROVOD_GPU_BROADCAST: NCCL
    HOROVOD_WITH_PYTORCH: 1
    HOROVOD_WITHOUT_TENSORFLOW: 1
    HOROVOD_WITHOUT_MXNET: 1
    HOROVOD_WITH_GLOO: 1
    HOROVOD_WITHOUT_MPI: 1

  #volumes:
  #  # Mount pip cache from host
  #  - name: pip_cache
  #    path: /opt/conda/lib/python3.7/site-packages

  commands:
    - export PATH="$PATH:/root/.local/bin"
    - python --version
    - pip install pip -U
    - pip --version
    - nvidia-smi
    #- bash ./tests/install_AMP.sh
    - apt-get update && apt-get install -y cmake
    - pip install -r ./requirements/base.txt --user -q
    - pip install -r ./requirements/devel.txt --user -q
    #- pip install -r ./requirements/docs.txt --user -q
    - pip list
    - python -c "import torch ; print(' & '.join([torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())]) if torch.cuda.is_available() else 'only CPU')"
    - coverage run --source pytorch_lightning -m py.test pytorch_lightning tests benchmarks -v  --durations=25 # --flake8
    #- cd docs; make doctest; make coverage
    - coverage report
    - codecov --token $CODECOV_TOKEN  # --pr $DRONE_PULL_REQUEST --build $DRONE_BUILD_NUMBER --branch $DRONE_BRANCH --commit $DRONE_COMMIT --tag $DRONE_TAG
    - python tests/collect_env_details.py

trigger:
  branch:
    - master
  event:
    include:
    - push
    - pull_request

#volumes:
#  - name: pip_cache
#    host:
#      path: /tmp/cache/drone/pip