Hello everyone 👋,

In this quick post I’ll show you how to deploy a FastAI model using FastAPI.

It’s a follow-up to my previous post from here and it is partially based on this FastAI lecture:

Exporting the Model

Before we create the API, we need to export the model as a pickle file.

Add learn.export("<path>/cat_or_dog.pkl") with the full absolute export path at the end of the script:

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# ...
learn = vision_learner(dls, resnet34, metrics=error_rate)
learn.fine_tune(1)

learn.export("C:\\Users\\nutiu\PycharmProjects\\fastaiIntro\cat_or_dog.pkl")

Deployment with FastAPI

Let’s create a new project and install the required dependencies:

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pip install fastai
pip install fastapi
pip install python-multipart
pip install "uvicorn[standard]"

Now create a main.py Python file that we’ll use to define our FastAPI app. Note that this is just a quick tutorial, it’s not meant to be a full introduction to FastAPI, you can find more details about it here.

The FastAPI handler is a simple post endpoint that accepts a file and returns a JSON response with the prediction, in case the file is not an image, it will return a 400 response.

The is_cat function is required for importing the model, if it’s not defined the model will not be loaded correctly.

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import PIL
from fastai.learner import load_learner
from fastapi import FastAPI, UploadFile, HTTPException

app = FastAPI()


def is_cat(x):
    return x[0].isupper()


cat_or_dog_model = load_learner(open("cat_or_dog.pkl", "rb"))


@app.post("/predict/cat-or-dog")
async def post_predict_cat_or_dog(file: UploadFile):
    try:
        file_contents = await file.read()
        is_cat, _, confidence = cat_or_dog_model.predict(file_contents)
        return {
            "is_cat": True if is_cat == "True" else False,
            "confidence": float(confidence[1])
        }
    except PIL.UnidentifiedImageError as e:
        raise HTTPException(status_code=400, detail="File is not an image")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

That’s it, you can now run the app using python main.py and test it using curl:

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curl -X 'POST' \
  'http://localhost:8000/predict/cat-or-dog' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'file=@photo-1608848461950-0fe51dfc41cb.jfif;type=image/jpeg'

You should get a response similar to this:

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{
  "is_cat": true,
  "confidence": 1
}

I get around 70ms response time on my machine.

Conclusion

I’ve showcased a simple way to deploy a FastAI model using FastAPI, by exporting it to a pickle file and then loading it in the API.

Another more advanced way to deploy the model would be to use the ONNX Runtime. Other ways to deploy the model are also showcased by Jeremy in the video above.

Thank you for reading! 📚🫶

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