Deploy Machine Learning Models with Keras, FastAPI, Redis and Docker

We will use the same architecture from the aforementioned posts by Adrian Rosebrock but substitute the web server frameworks (FastAPI + Uvicorn for Flask + Apache) and, more importantly, containerize the whole setup for ease of use. We will also be using most parts of Adrian’s code as he has done a splendid job with the processing, serialization, and wrangling with a few NumPy gotchas.

The main function of the web server is to serve a /predict endpoint through which other applications will call our machine learning model. When the endpoint is called, the web server routes the request to the Redis, which acts as an in-memory message queue for many concurrent requests. The model server simply polls the Redis message queue for a batch of images, classifies the batch of images, then returns the results to Redis. The web server picks up the results and returns that.

You can find all the code used in this tutorial here:

Serve a production-ready and scalable Keras-based deep learning model image classification using FastAPI, Redis and…

github.com

I chose to use the tiangolo/uvicorn-gunicorn-fastapi for the web server. This Docker image provides a neat ASGI stack (Uvicorn managed by Gunicorn with FastAPI framework) which promises significant performance improvements over the more common WSGI-based flask-uwsgi-nginx.

This decision was largely driven by wanting to try out an ASGI stack and high-quality docker images like tiangolo’s have made experimentation a lot easier. Also, as you’ll see in the code later, writing simple HTTP endpoints in FastAPI isn’t too different from how we’d do it in Flask.

The webserver/Dockerfile is quite simple. It takes the above-mentioned image and installs the necessary Python requirements and copies the code into the container:

FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7

COPY requirements.txt /app/

RUN pip install -r /app/requirements.txt

COPY . /app

The webserver/main.py file runs the FastAPI server, exposing the/predict endpoint which takes the uploaded image, serializes it, pushes it to Redis and polls for the resulting predictions.

webserver/main.py

The code is mostly kept as-is with some housekeeping for a Dockerized environment, namely separating helper functions and parameters for the web and model server. Also, the parameters are passed into the Docker container via environment variables (more on that later).

The modelserver/Dockerfile is also quite simple:

FROM python:3.7-slim-buster

COPY requirements.txt /app/

RUN pip install -r /app/requirements.txt# Download ResNet50 model and cache in image RUN python -c “from keras.applications import ResNet50; ResNet50(weights=’imagenet’)”COPY . /app

CMD [“python”, “/app/main.py”]

Here I used the python:3.7-slim-buster image. The slim variant reduces the overall image size by about 700mb. The alpine variant does not work with tensorflow so I’ve chosen not to use it.

I also chose to downloaded the machine learning model in the Dockerfile so it’ll be cached in the Docker image. Otherwise the model will be downloaded at the point of running the model server. This is not an issue aside from adding a few minutes delay to the replication process (as each worker that starts up needs to first download the model).

Once again, the Dockerfile installs the requirements and then runs the main.py file.

modelserver/main.py

The model server polls Redis for a batch of images to predict on. Batch inference is particularly efficient for deep learning models, especially when running on GPU. The BATCH_SIZE parameter can be tuned to offer the lowest latency.

We also have to use redis-py’s pipeline (which is a misnomer as it is by default transactional in redis-py) to implement an atomic left-popping of multiple element (see lines 45–48). This becomes important in preventing race conditions when we replicate the model servers.

docker-compose.yml

We create 3 services — Redis, model server and web server — that are all on the same Docker network.

The “global” parameters are in the app.env file while the service-specific parameters (such as SERVER_SLEEP and BATCH_SIZE) are passed in as environment variables to the containers.

The deploy parameters are used only for Docker Swarm (more on that in the following post) and will be safely ignored by Docker Compose.

We can spin everything up with docker-compose up which will build the images and start the various services. That’s it!

Now test the service by curling the endpoints:

$ curl http://localhost
“Hello World!”$ curl -X POST -F img_file=@doge.jpg http://localhost/predict
{“success”:true,”predictions”:[{“label”:”dingo”,”probability”:0.6836559772491455},{“label”:”Pembroke”,”probability”:0.17909787595272064},{“label”:”basenji”,”probability”:0.07694739103317261},{“label”:”Eskimo_dog”,”probability”:0.01792934536933899},{“label”:”Chihuahua”,”probability”:0.005690475460141897}]}

Success! There’s probably no “shiba inu” class in ImageNet so “dingo” will have to do for now. Close enough.

Locust is a load testing tool designed for load-testing websites. It is intended for load testing websites but also works great for simple HTTP endpoints like ours.

It’s easy to get it up and running. First install it with pip install locustio then start it up by running within the project directory:

locust –host=http://localhost

This uses the provided locustfile to test the /predict endpoint. Note that we’re pointing the host to localhost — we’re testing the response time of our machine learning service without any real network latency.

Now point your browser to http://localhost:8089 to access the locust web ui.

Locust Web UI

We’ll simulate 50 users (who are all hatched at the start).

Green for median response time; yellow for p95

A p95 response time of around 5000ms means that 95% of requests should complete within 5 seconds. Depending on your use case and expected load, this could be far too slow.

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