torch-dreams 🤝 runwayML

How to run

0. Clone the repo and navigate into the folder.

   $ git clone https://github.com/Mayukhdeb/torch-dreams-runwayML.git
   $ cd torch-dreams-runwayML
   $ pip install -r requirements.txt
   

1. Start the server with:

   $ python runway_model.py
   

You should see an output similar to this:

Initializing model...
dreamer init on:  cuda
Model initialized (0.04s)
Starting model server at http://0.0.0.0:9000...

2. Open a new workspace in RunwayML and press connect. Make sure your localhost port matches the one given by the server script (in our case it's 9000)

3. Select the image you want to work with.

4. Play around with the inference parameters and have fun!

Try reading the torch-dreams docs to create fancier visualizations.

Architecture

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

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.

The runway.yml Config File

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: 10.2
framework: pytorch
files:
    ignore:
        - image_dataset/*
build_steps:
    - pip install runway-python==0.1.0
    - pip install -r requirements.txt