How to Load Deep Learning Model in FastAPI: A Step-by-Step Guide

Are you tired of struggling to deploy your deep learning models in FastAPI? Do you want to learn how to load and use your models efficiently in your FastAPI application? Look no further! In this comprehensive guide, we’ll walk you through the process of loading a deep learning model in FastAPI, step-by-step.

Prerequisites

Before we dive in, make sure you have the following:

• Familiarity with Python and deep learning concepts
• FastAPI installed and running on your system
• A trained deep learning model (we’ll use a simple Keras model as an example)
• A basic understanding of APIs and RESTful architecture

Step 1: Prepare Your Model

Assuming you have a trained deep learning model, let’s prepare it for deployment in FastAPI.

Model Serialization

In order to load the model in FastAPI, we need to serialize it using a format that can be easily loaded and used in our application. For this example, we’ll use the .h5 format, which is a popular choice for Keras models.

# Serialize the model
model.save('model.h5')

Model Structure

For the sake of simplicity, let’s assume our model has the following structure:

• A single input layer with 784 neurons
• Two hidden layers with 256 neurons each
• A single output layer with 10 neurons

We’ll use this structure to create a simple Keras model:

from keras.models import Sequential
from keras.layers import Dense

# Create the model
model = Sequential()
model.add(Dense(256, activation='relu', input_shape=(784,)))
model.add(Dense(256, activation='relu'))
model.add(Dense(10, activation='softmax'))

# Compile the model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

Step 2: Create a FastAPI App

Now that we have our model prepared, let’s create a new FastAPI application.

from fastapi import FastAPI
from fastapi.responses import JSONResponse
from fastapi.requests import Request

app = FastAPI()

Step 3: Load the Model in FastAPI

With our FastAPI app created, let’s load our serialized model using the keras.models.load_model() function:

from keras.models import load_model

# Load the model
model_loaded = load_model('model.h5')

We’ll create a new route to load the model and store it in a global variable:

@app.on_event("startup")
async def load_model():
    global model_loaded
    model_loaded = load_model('model.h5')

Step 4: Create an Endpoint to Use the Model

Now that our model is loaded, let’s create an endpoint to use it. For this example, we’ll create a simple API that takes an input image and returns the predicted class.

@app.post("/predict")
async def predict(input_data: dict):
    # Get the input data
    input_array = np.array(input_data['image'])

    # Reshape the input data to match the model's input shape
    input_array = input_array.reshape((1, 784))

    # Make a prediction using the loaded model
    prediction = model_loaded.predict(input_array)

    # Get the predicted class
    predicted_class = np.argmax(prediction)

    # Return the predicted class as a JSON response
    return JSONResponse(content={"predicted_class": predicted_class}, media_type="application/json")

Step 5: Test the API

Let’s test our API using a tool like curl or a API client like Postman.

curl -X POST \
  http://localhost:8000/predict \
  -H 'Content-Type: application/json' \
  -d '{"image": [1, 2, 3, 4, 5, ...]}'

Replace the [1, 2, 3, 4, 5, ...] with a sample input image. You should receive a JSON response with the predicted class.

Conclusion

And that’s it! You’ve successfully loaded a deep learning model in FastAPI and created an endpoint to use it. This is just the beginning of deploying your models in production.

Tips and Tricks

Here are some additional tips to keep in mind:

• Use a robust model serialization format like .h5 or .pb
• Optimize your model for deployment using techniques like model pruning and quantization
• Use a load balancer and scaling to handle high traffic and large payloads
• Implement authentication and authorization to secure your API

Resources

For further learning and reference:

• FastAPI Documentation
• Keras Documentation
• TensorFlow Documentation

We hope this guide has helped you load your deep learning model in FastAPI efficiently. Happy coding!

Model	Description
Keras Model	A simple neural network with two hidden layers

Don’t hesitate to reach out if you have any questions or need further assistance. Happy deploying!

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Frequently Asked Question

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