Everinfer allows to "chain" multiple ONNX graphs in a single pipeline. Use pipeline creation syntax in the following way...
['model_1.onnx', 'model_2.onnx', ...., 'model_N.onnx'])
...to merge multiple models in a single graph. Outputs of each model will be used as inputs for the next model.
Output names of the model must match input names of the next one!
This can be used in a multitude of ways, for example:
- Fuse pre- and post-processing in a single graph with the main model. Check out our FasterRCNN example, showcasing that approach.
- Do simple computations locally and offload demanding models to Everinfer. Stable Diffusion example does exactly that, offloading U-Net model to remote GPUs.
- Deploy huge models, like Large Language Models, by splitting them into multiple graphs.
Got cool ideas and use cases for model chaining on Everinfer?
Please, hit us up through [email protected] , we will be glad to include them as examples and give credits to you!