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This tutorial explains how one can construct a containerized sentiment evaluation API utilizing Hugging Face, FastAPI and Docker
Many AI initiatives fail, in keeping with varied reviews (eg. Hardvard Business Review). I speculate that a part of the barrier to AI mission success is the technical step from having constructed a mannequin to creating it extensively out there for others in your group.
So how do you make your mannequin simply out there for consumption? A method is to wrap it in an API and containerize it in order that your mannequin could be uncovered on any server with Docker put in. And that’s precisely what we’ll do on this tutorial.
We’ll take a sentiment evaluation mannequin from Hugging Face (an arbitrary alternative simply to have a mannequin that’s simple to indicate for instance), write an API endpoint that exposes the mannequin utilizing FastAPI, after which we’ll containerize our sentiment evaluation app with Docker. I’ll present code examples and explanations all the best way.
The tutorial code has been examined on Linux, and may work on Home windows too.
We’ll use the Pipeline class from Hugging Face’s transformers library. See Hugging Face’s tutorial for an introduction to the Pipeline in the event you’re unfamiliar with it.
The pipeline makes it very simple to make use of fashions similar to sentiment fashions. Try Hugging Face’s sentiment analysis tutorial for a radical introduction to the idea.
You possibly can instantiate the pipe with a number of totally different constructor arguments. A method is to move in a kind of job:
from transformers import pipelinepipe = pipeline(job="sentiment-analysis")
This may use Hugging Face’s default mannequin for the supplied job.
One other manner is to move the mannequin argument specifying which mannequin you need to use. You don’t…
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