runway.yml

# Specify the version of the runway.yml spec.
version: 0.1
# Supported python versions are 2.7 and 3.6
python: 3.6
# The command to run your model. This value is used as the CMD value in
# the generated Docker image.
entrypoint: python runway_model.py
# Which NVIDIA CUDA version to use. Supported versions include 10, 9.2, and 9.
cuda: 9.2
# Which ML framework would you like to pre-install? The appropriate GPU/CPU
# versions of these libraries are selected automatically. Accepts values
# "tensorflow" and "pytorch", installinv Tensorflow v1.12 and Pytorch v1.0
# respectively.
framework: pytorch
# Builds are created for CPU and GPU environments by default. You can use the
# spec object to limit your builds to one environment if you'd like, for
# instance if your model doesn't use CUDA or run on a GPU you can set
# gpu: False.
spec:
    cpu: True
    gpu: True
files:
    # All files in the root project directory will be copied to the Docker image
    # automatically. Builds that require excessive storage can fail or take a
    # very long time to install on another user's machine. You can use the
    # files.ignore array to exclude files from your build.
    ignore:
        - my_dataset/*
        - secrets.txt
# The build_steps array allows you to run shell commands at build time. Each
# Each build step is executed in the order it appears in the array.
build_steps:
    #- apt-get -y install libsm6 libxrender1 libfontconfig1
    - pip install -r requirements.txt
    # The if_gpu and if_cpu directives can be used to run build steps
    # conditionally depending on the build environment.
    - if_gpu: echo "Building in a GPU environment..."
    - if_cpu: echo "Building in a CPU only environment..."