Deploy Python ML Models on Runpod—No Docker Needed

Deploying Python machine learning models on Runpod can now be accomplished with minimal stress through a workflow that, while currently somewhat hacky, is expected to become more streamlined with future updates. The process is suitable for containers installed purely from PyPI and requires a good understanding of virtual environments, terminal usage, and network drives. The tutorial provides a step-by-step guide to setting up a Runpod Pytorch environment, creating a virtual environment, installing necessary packages, and developing a text-to-speech engine called Bark. The workflow involves creating and saving relevant Python scripts, testing the API on both Runpod and serverless platforms, and setting up a serverless API using a custom template. While the current setup is complex, the guide anticipates improvements that will simplify the deployment of Python models in the future.

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