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Moving an ML model from experiment to production requires significant engineering lift. Scale Launch provides ML engineers a simple Python interface for turning a local code snippet into a production service. A ML engineer needs to call a few functions from Scale's SDK, which quickly spins up a production-ready service. The service efficiently utilizes compute resources and automatically scales according to traffic.

Latest API/SDK reference can be found here.

Central to Scale Launch are the notions of a ModelBundle and a ModelEndpoint. A ModelBundle consists of a trained model as well as the surrounding preprocessing and postprocessing code. A ModelEndpoint is the compute layer that takes in a ModelBundle, and is able to carry out inference requests by using the ModelBundle to carry out predictions. The ModelEndpoint also knows infrastructure-level details, such as how many GPUs are needed, what type they are, how much memory, etc. The ModelEndpoint automatically handles infrastructure level details such as autoscaling and task queueing.

Steps to deploy your model via Scale Launch:

1. First, you create and upload a ModelBundle.

2. Then, you create a ModelEndpoint.

3. Lastly, you make requests to the ModelEndpoint.

TODO: link some example colab notebook

Clone from github and install as editable

git clone git@github.com:scaleapi/launch-python-client.git
cd launch-python-client
pip3 install poetry
poetry install

Please install the pre-commit hooks by running the following command:

poetry run pre-commit install

The tests can be run with:

poetry run pytest

Updating documentation: We use mkdocs to autogenerate our API Reference from docstrings and markdown files.

To test your local docstring changes, run the following commands from the repository's root directory:

poetry shell
mkdocs serve

The above command will spin up a server on localhost (port 8000 by default) that will watch for and automatically rebuild a version of the API reference based on your local docstring and markdown changes.