Model management

Model chaining

Everinfer allows to "chain" multiple ONNX graphs in a single pipeline. Use pipeline creation syntax in the following way...

client.register_pipeline('model_chaining_example', 
['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.

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!

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