Transformer models are great. Still, they’re large models, and prediction latency can be a problem. This is the problem that Hugging Face Infinity solves with a single Docker command.
In this video, I start from a pre-trained model hosted on the Hugging Face hub. Using an AWS CPU instance based on the Intel Ice Lake architecture (c6i.xlarge), I optimize my model using the Infinity Multiverse Docker container.
Then, I push the model back to the Hugging Face hub, and I deploy it on a prediction API running in an Infinity container on my AWS instance.
Finally, I predict with the optimized model and get a 5x speedup compared to the original model.
Original model: https://huggingface.co/juliensimon/autonlp-imdb-demo-hf-16622767
Code: https://huggingface.co/juliensimon/imdb-demo-infinity/tree/main/code
New to Transformers? Check out the Hugging Face course at https://huggingface.co/course
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