Last month I was at Kubecon Barcelona with with some of the OpenFaaS community, we were asked about deploying data science models to Kubernetes and, of course, can OpenFaaS help deploy models? In this post we will introduce a new function template aimed at Python data scientists and walk through a concrete example of deploying a PyTorch model.

The pydatascience template

The pydatascience template showcases a couple of ideas that anyone can leverage in their own functions and templates:

  1. using the Conda package manager
  2. setting up a non-root user Python environment
  3. multi-module Python function
  4. using HTTP mode in of-watchdog to load an asset (in this case a model) into memory during startup

The name classifier function

In this post, we share an example name classifier. It is a relatively simple function that accepts a name and then attempts to guess the nationality of that name, returning the top three guesses.

echo "Lucas" | faas-cli invoke classify

You can try this in your own OpenFaaS cluster using

faas-cli deploy --image=theaxer/classify:latest --name=classify

Caution, this model was built on a relatively small data set and is intended to be a simple demonstration. And remember, with all things data science, “with great power comes great responsibility”.

Understanding the template

What is Conda?

I have recently started to using the Conda package manager for many of my own experiments with Python, especially when they involve Pandas and Numpy, because the install is very fast and environment management is fairly easy. For function development and build, the speed is really nice. For this project it was also the easiest way to install PyTorch. Finally, Conda also supports non-Python packages, e.g. curl, yarn-js, and zeromq are installable via Conda.

Set up a non-root function

A best practice for deploying Docker images is to make sure that the user running your code is not privileged, this is also known as “non-root”. These images tend to be much safer to deploy. Fortunately, OpenFaaS makes it easy to enforce, even when the original image is not non-root by default. When we build new templates, it is important that we consider non-root deployments as the default. For Python environments, Conda made this very easy.

The first thing the Dockerfile does is create a new user and then setup and fix the permissions on some standard folders. This will ensure that when we change users later, Conda and the function code will run smoothly. The /opt/conda folder is often used by Conda and the function code will eventually be put into /home/app (as is done in many of the core templates).

RUN addgroup app && adduser app --system --ingroup app \
    && mkdir -p /opt/conda && chown -R app /opt/conda

ENV HOME /home/app
ENV PATH=$HOME/conda/bin:$PATH

RUN apt-get update \
    && apt-get -y install curl bzip2 ${ADDITIONAL_PACKAGE} \
    && curl -sSL -o /tmp/ \
    && curl -sSL > /usr/bin/fwatchdog \
    && chown app /tmp/ \

Once the additional packages and watchdog are installed, we can switch to the non-root app user and finish the Python installation

WORKDIR /home/app/
USER app

RUN bash /tmp/ -bfp $HOME/conda \
    && conda install -y python=3 \
    && conda update conda \
    && conda clean --all --yes \
    && rm -rf /tmp/

From here on out, the Docker image will use the app user, improving the security of the final function. This pattern should work for almost any template. The hard part is knowing which folders the app user will need full permissions for. This is where Conda was helpful, because it was easy to find and setup the required permissions and once those folders are configured, Conda behaves as expected when run as a non-root user.

A multi-module template

Earlier this year we highlighted how to use multiple files and modules to organize your Python functions. This new template follows this pattern out of the box. It provides a “core” module for putting utility methods, a handler file, which is where you need to implement your function logic, and a “training” file where you can put the logic for training your model.

├── core
│   ├──
│   ├──
│   └──
├── requirements.txt

Like the basic python template, only the implementation is required, and both the core module and the could be replaced if you needed, but this structure starts you on a path to keeping the code organized.

From a template to actual code

The name classifier function uses the neural network implementation in PyTorch. PyTorch has a great introduction and walk-through for neural network package and model training. In this post we focus on what a concrete and complete implementation looks like. All of the interesting details about the implementation of the model, the utilities used to parse the training data, and the utility for applying the model to a new inputs are stored in the core module. This core module can then be used in both the function handler and the training script. This code is also easier to reuse in a new function because it is cleanly isolated from the function itself.

├── core
│   ├──
│   ├──
│   ├──
│   ├──
│   └──
├── data
│   ├──
│   └── names
│       ├── Arabic.txt
│       ├── Chinese.txt
│       ├── ...
│       └── Vietnamese.txt
├── requirements.txt

Train the model

Another point to note is the training data folder is also included here, data/names and a serialized model is also include data/

This template is designed to run the training as part of the build process. You can easily trigger the training using


This will generate a new serialized model data/

There are two methods for saving PyTorch models:

  1. save the entire model using pickle, or
  2. saving just the model state_dict.

We have opted for the second method because it is more portable, the pickle method is sensitive to the Python environment and folder structure., "data/")

Loading the model into function memory

Using the HTTP mode in the of-watchdog enables us to load this model into memory once and reuse it for multiple requests. We only need to load the model at the start of the implementation

import json
import os
from pathlib import PurePath
from typing import Any, List

import torch
from torch.autograd import Variable

from .core import const, model, utils

FUNCTION_ROOT = os.environ.get("function_root", "/home/app/function/")

# init model
RNN = model.RNN(const.N_LETTERS, const.N_HIDDEN, const.N_CATEGORIES)
# fill in weights
    torch.load(str(PurePath(FUNCTION_ROOT, "data/")))

def predict(line: str, n_predictions: int = 3) -> List[Any]:
    """omitted for brevity"""

def handle(req: bytes) -> str:
    """handle a request to the function
        req (bytes): request body

    if not req:
        return json.dumps({"error": "No input provided", "code": 400})

    name = str(req)
    output = predict(name)

    return json.dumps(output)

When a function instance is instantiated, e.g. during scaling or deployment, this file is parsed and the model is loaded into memory. This will happen exactly once because the HTTP mode loads this handler as a very small background web server. The original forking mode in the watchdog would instead load this file for every invocation. When loading models, this creates additional latency.

This template uses Flask to power the background web server. The handle can return any value that Flask would accept. This provides a lot of flexibility to control the response. For example, you could instead set the error status code and body like this

return json.dumps({"error": "No input provided"}), 400

Big models: if your model file is very large and takes a few seconds to load, you can use the com.openfaas.ready.http.initialDelay to extend the function readiness check so that the function has enough time to load the file. Check the docs for and example.


This template is designed to bundle the pre-trained model into the final Docker image. This means that the deployment steps look like this

cd classify && python && cd ..
faas-cli build classify

This results in a completely self-contained Docker image that does not need access to a database or S3 during runtime. This provides several benefits:

  1. This model becomes very easy to share because you don’t need to share access to your model storage.
  2. It also means that each new deployment is completely versioned, you know the version of the model from the Docker tag. This makes rolling backward or forward as simple as changing the Docker tag on the deployment.
  3. One final benefit is that startup time can be slightly faster because once a node has the Docker image in its cache, it has everything it needs to start new instances of the function. If you load the model from an external source, then every function instance that starts must curl/copy from that external location, there is no way to cache it for new instances.

On the other hand, this does result in slightly larger images, in this case the model is 107k. This size will, of course, depend on the libraries being used and the type of model being serialized. For many models, the simplicity of a self-contained image out-weighs the cost of the additional size. Often Docker layer caching will hide almost all of it.

You can try the latest version of this function in your own cluster using

faas-cli deploy --image=theaxer/classify:latest --name=classify

A note about Python 2 deprecation

I decided not to create a Python 2 template in the pydatascience-template repo because we are coming very quickly to the end-of-life for Python 2. If you are still using Python 2, the templates could be forked and modified, but please be careful. Most packages will start deprecating Python 2 support soon.

Wrapping up

Data science is of course a popular topic these days and OpenFaaS can help simplify, standardize, and speed up the deployment process for your team. This post introduces just one example of what a data science focused template could look like.

Have you struggled to package your machine-learning model for platforms like AWS Lambda? OpenFaaS functions don’t have an arbitrary size limit and may provide an easier alternative for you.

Connect with us to discuss further or to share what you’ve built.

Going further

Star or Fork the code for the name-classifier on Github.

OpenFaaS can support various machine learning frameworks beyond PyTorch, if you prefer TensorFlow, checkout this tensorflow-serving-openfaas example from Alex on Github.

You may also like

Lucas Roesler

Core Team @openfaas. Senior Engineer and Team Lead @contiamo