This repository contains an example model template that can be used as reference when porting machine learning models to Runway. This template uses the Runway Model SDK Python module, check out the module documentation for more info.

See the Importing Models into Runway tutorial for a walk-through illustrating how to port a model to Runway.

Each Runway model consists of two special files:

• runway_model.py: A Python script that imports the runway module (SDK) and exposes its interface via one or more @runway.command() functions. This file is used as the entrypoint to your model.
• runway.yml: A configuration file that describes dependencies and build steps needed to build and run the model.

The runway_model.py entrypoint file is the file the Runway app will use to query the model. This file can have any name you want, but we recommend calling it runway_model.py.

All Runway models expose a standard interface that allows the Runway desktop application to interact with them over HTTP. This is accomplished using three functions: @runway.setup(), @runway.command(), and runway.run().

import runway
from runway.data_types import number, text, image
from example_model import ExampleModel

# Setup the model, initialize weights, set the configs of the model, etc.
# Every model will have a different set of configurations and requirements.
# Check https://sdk.runwayml.com/en/latest/runway_module.html to see a complete
# list of supported configs. The setup function should return the model ready to
# be used.
setup_options = {
    'truncation': number(min=5, max=100, step=1, default=10),
    'seed': number(min=0, max=1000000)
}
@runway.setup(options=setup_options)
def setup(opts):
    msg = '[SETUP] Run with options: seed = {}, truncation = {}'
    print(msg.format(opts['seed'], opts['truncation']))
    model = ExampleModel(opts)
    return model

# Every model needs to have at least one command. Every command allows to send
# inputs and process outputs. To see a complete list of supported inputs and
# outputs data types: https://sdk.runwayml.com/en/latest/data_types.html
@runway.command(name='generate',
                inputs={ 'caption': text() },
                outputs={ 'image': image(width=512, height=512) })
def generate(model, args):
    print('[GENERATE] Ran with caption value "{}"'.format(args['caption']))
    # Generate a PIL or Numpy image based on the input caption, and return it
    output_image = model.run_on_input(args['caption'])
    return {
        'image': output_image
    }

if __name__ == '__main__':
    # run the model server using the default network interface and ports,
    # displayed here for convenience
    runway.run(host='0.0.0.0', port=8000)

See the example_model.py file for the simple ExampleModel class used in the example above.

Each Runway model must have a runway.yml configuration file in its root directory. This file defines the steps needed to build and run your model for use with Runway. This file is written in YAML, a human-readable superset of JSON. Below is an example of a runway.yml file. This example file illustrates how you can provision your model’s environment.

version: 0.1
python: 3.6
entrypoint: python runway_model.py
cuda: 9.2
framework: tensorflow
files:
    ignore:
        - image_dataset/*
build_steps:
    - pip install runway-python==0.1.0
    - pip install -r requirements.txt

While you're developing your model it's useful to run and test it locally.

## Optionally create and activate a Python 3 virtual environment
# virtualenv -p python3 venv && source venv/bin/activate

# Install the Runway Model SDK (`pip install runway-python`) and the Pillow
# image library, used in this example.
pip install -r requirements.txt

# Run the entrypoint script
python runway_model.py

You should see an output similar to this, indicating your model is running.

Setting up model...
[SETUP] Ran with options: seed = 0, truncation = 10
Starting model server at https://0.0.0.0:8000...

You can test your model once its running by POSTing a caption argument to the the /generate command.

curl \
    -H "content-type: application/json" \
    -d '{ "caption": "red" }' \
    https://localhost:8000/generate

You should receive a JSON object back, containing a cryptic base64 encoded URI string that represents a red image:

{"image": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQ..."}