To deploy the Type4Py model for the production environment, we convert the pre-trained PyTorch model to an ONNX model which allows us to query the model on both GPUs and CPUs with very fast inference speed and lower VRAM consumption. Thanks to Annoy, Type Clusters are memory-mapped into RAM from disk, which consumes less memory.
To handle concurrent type prediction requests from users, we employ Gunicorn ‘s HTTP server with Nginx as a proxy. This allows us to have quite a number of asynchronous workers that have an instance of Type4Py’s ONNX model plus Type Clusters each.
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