The PyTorch ecosystem's equivalent of Tensorflow Serving is TorchServe. It's optimized for serving PyTorch models across several domains including vision, NLP, tabular data, and audio.
Like Tensorflow Serving, it is designed for large-scale cloud deployments and can require custom configuration for things like pre- and post-processing and deploying multiple models. Because of its wide mandate it lacks many vision-specific features (like video streaming).
Chose TorchServe if: you're looking for a way to scale and customize the deployment of your PyTorch models and don't need vision-specific functionality.
Inference turns any computer or edge device into a command center for your computer vision projects.
Inference turns any computer or edge device into a command center for your computer vision projects.