# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# 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:cuda-extras-py3.7-torch1.5

  environment:
    SLURM_LOCALID: 0
    CODECOV_TOKEN:
      from_secret: codecov_token
    MKL_THREADING_LAYER: GNU
    HOROVOD_GPU_OPERATIONS: 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:
    # todo: remove unsets as in correct image Horovod shall be set
    - unset HOROVOD_GPU_ALLREDUCE
    - unset HOROVOD_GPU_BROADCAST
    - export PATH="$PATH:/root/.local/bin"
    - python --version
    - pip install pip -U
    - pip --version
    - nvidia-smi
    #- bash ./requirements/install_AMP.sh
    - apt-get update && apt-get install -y cmake
    - pip uninstall -y horovod  # todo: this shall not be needed
    - pip install -r ./requirements/devel.txt --user -q --upgrade-strategy only-if-needed  --no-cache-dir
    #- pip install -r ./requirements/docs.txt --user -q
    - pip install -r ./requirements/examples.txt --user -q --upgrade-strategy only-if-needed
    - 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 pytest pytorch_lightning tests -v --color=yes --durations=25 # --flake8
    - python -m py.test benchmarks pl_examples -v --color=yes --maxfail=2 --durations=0 # --flake8
    #- cd docs; make doctest; make coverage
    - coverage report
    # see: https://docs.codecov.io/docs/merging-reports
    - codecov --token $CODECOV_TOKEN --flags=gpu,pytest --name="GPU-coverage" --env=linux --build $DRONE_BUILD_NUMBER --commit $DRONE_COMMIT
    # --build $DRONE_BUILD_NUMBER --branch $DRONE_BRANCH --commit $DRONE_COMMIT --tag $DRONE_TAG --pr $DRONE_PULL_REQUEST
    # - codecov --token $CODECOV_TOKEN --flags=gpu,pytest --build $DRONE_BUILD_NUMBER
    - python tests/collect_env_details.py

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

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