.travis.yml

language: python
python: 2.7
dist: trusty
sudo: false

env:
  global:
    - NBCONVERT_ARGS="--to html --execute --ExecutePreprocessor.kernel_name=python --ExecutePreprocessor.timeout=-1"

install:
  # xvfb
  # https://docs.travis-ci.com/user/gui-and-headless-browsers/
  - "export DISPLAY=:99.0"
  - "sh -e /etc/init.d/xvfb start"
  # install conda
  # https://conda.io/docs/travis.html
  - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
      wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh;
    else
      wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
    fi
  - bash miniconda.sh -b -f -p $HOME/miniconda
  - export PATH="$HOME/miniconda/bin:$PATH"
  - hash -r
  - conda config --set always_yes yes --set changeps1 no
  - conda update -q conda
  - conda config --add channels conda-forge
  - conda info -a
  - conda create -q --name fastai --file requirements.txt python=$TRAVIS_PYTHON_VERSION
  - source activate fastai
  - conda list

script:
  - mkdir -p deeplearning1/nbs/data
  - if [ ! -d deeplearning1/nbs/data/dogscats ]; then
    curl -sSLO "http://files.fast.ai/data/dogscats.zip" &&
    unzip -qq -d deeplearning1/nbs/data dogscats.zip;
    fi
  - travis_wait jupyter nbconvert deeplearning1/nbs/lesson1.ipynb $NBCONVERT_ARGS
  - travis_wait jupyter nbconvert deeplearning1/nbs/lesson2.ipynb $NBCONVERT_ARGS

before_cache:
  # We only cache the archives but not the extracted packages,
  # such that the miniconda installer script can extract correctly.
  - rm -rf $HOME/miniconda/pkgs/*/
cache:
  timeout: 1000
  directories:
    - $HOME/miniconda/pkgs
    - $HOME/.keras/models
    - deeplearning1/nbs/data