imagedims-python

Creating a Function from a Docker Image

This sample walks you through how to use a custom Docker image to define a function. Although functions are packaged as Docker images, when developing functions using the Fn CLI developers are not directly exposed to the underlying Docker platform. Docker isn't hidden (you can see Docker build output and image names and tags), but you aren't required to be very Docker-savvy to develop functions with Fn.

However, sometimes you need to handle advanced use cases and must take complete control of the creation of the function container image. Fortunately the design and implementation of Oracle Functions enables you to do exactly that. Let's build a simple custom function container image to become familiar with the key elements of the process.

One of the most common reasons for writing a custom Dockerfile for a function is to install a Linux package that your function needs. In our example we're going to use the the ever-popular ImageMagick to do some image processing in our function and while there are several Python libraries for ImageMagick, they are just wrappers on the underlying native library. So we'll have to install the native library in addition to adding the Python library (Wand) to our requirements.txt dependencies.

As you make your way through this tutorial, look out for this icon. Whenever you see it, it's time for you to perform an action.

Prerequisites

Before you deploy this sample function, make sure you have run steps A, B and C of the Oracle Functions Quick Start Guide for Cloud Shell

• A - Set up your tenancy
• B - Create application
• C - Set up your Cloud Shell dev environment

Review and customize the function

Review the following files in the current folder:

• the code of the function, func.py
• the function dependencies, requirements.txt
• the function metadata, func.yaml
• the function Dockerfile, Dockerfile

The function takes a binary image as its argument, writes it to a tmp file, and then uses ImageMagick to obtain the width and height of the image. There are lots of interesting elements to this function but the key one for us is the use of the "wand" Python library for ImageMagick image processing.
To use it we need to include it along with our other dependencies in the requirements.txt file. With all Python functions, we include the Fn Python FDK as a dependency, in this samples we add "wand" for imagemagick image processing.
This function metadata file func.yaml is pretty standard for a Python function except that instead of declaring the runtime as "Python" we specify "docker".
One more small point worthy of note is that if you don't declare the runtime property then it will default to docker and the CLI will look for a Dockerfile.

The Dockerfile that "fn build" would automatically generate to build a Python 3.6 function container image looks like this:

FROM fnproject/python:3.6-dev as build-stage
WORKDIR /function
ADD requirements.txt /function/

RUN pip3 install --target /python/  --no-cache --no-cache-dir -r requirements.txt &&\
  rm -fr ~/.cache/pip /tmp* requirements.txt func.yaml Dockerfile .venv &&\
  chmod -R o+r /python
ADD . /function/
RUN rm -fr /function/.pip_cache
FROM fnproject/python:3.6
WORKDIR /function
COPY --from=build-stage /python /python
COPY --from=build-stage /function /function
RUN chmod -R o+r /function
ENV PYTHONPATH=/function:/python
ENTRYPOINT ["/python/bin/fdk", "/function/func.py", "handler"]

It's a two stage build with the first stage containing python with build tools and the second stage with only the python runtime and minimum required dependencies. This approach is designed to ensure that deployable function container images are as small as possible, which is beneficial for a number of reasons including the time it takes to transfer the image from a Docker respository to the compute node where the function is to be run.

The fnproject/python:3.6 container image needs ImageMagick from the EPEL repo, so we'll need to install the ImageMagick package using the microdnf package management utility.

RUN microdnf install oracle-epel-release-el8 &&\
      microdnf install ImageMagick &&\
      microdnf remove oracle-epel-release-el8 &&\
      microdnf clean all

We want to install ImageMagick into the runtime image, not the build image, so we need to add the above lines in the second stage as shown in the merged Dockerfile.

Deploy the function

In Cloud Shell, run the fn deploy command to build the function and its dependencies as a Docker image, push the image to OCIR, and deploy the function to Oracle Functions in your application.

fn -v deploy --app <app-name>

Invoke the function

With the function deployed let's invoke it to make sure it's working as expected. You'll need a jpeg or png file so either find one on your machine or download one. The 3x3.jpg image in this repo has a height and width of 3 pixels.

 cat 3x3.jpg | fn invoke <app-name> imagedims-python

You should see the following output:

{"width": 3, "height": 3}

Monitoring Functions

Learn how to configure basic observability for your function using metrics, alarms and email alerts:

• Basic Guidance for Monitoring your Functions

Conclusion

One of the most powerful features of Fn and Oracle Functions is the ability to use custom-defined Docker container images as functions. This feature makes it possible to customize your function's runtime environment including letting you install any Linux libraries or utilities that your function might need. And thanks to the Fn CLI's support for Dockerfiles it's the same user experience as when developing any function.