2. Write the Processing script

Now that you created your Processing on Picsellia, let's link this with your code by building the Docker Image that will be used to launch your scripts automatically.

To easily follow the process, you can go to this Github repository and use it as a template for your own Processing!

1. Repository structure

The main goal when you create a Processing is to package your code in a Docker Image containing all your requirements that will run alongside code from us that will capture the logs so you can see them live on the platform.

Let's have a look at the structure of our repository

It's pretty simple here is the function of each file:

  • The Dockerfile contains all the instructions to build the image and launch our launch_processing script.
  • requirements.txt contains the packages needed to run our code, you can add any package that you like and need there!
  • launch_processing.py is the entry point of our Processing, it's a file that you should not touch that will launch a subprocess with you actual code that you created.
  • main.py is your script, this is where you can retrieve the information from your Processing, your DatasetVersion and perform the actions that you like (for example data-augmentation).
  • The utils_folder is there to put all your sub scripts, functions, and classes that will be imported by your script, it's better that you put everything there so you are sure you can import it from _main.py (although everything could also be in the main file, that's your choice).
FROM python:3.8-bullseye as base

ARG DEBIAN_FRONTEND=noninteractive

RUN pip3 install --no-cache-dir picsellia
COPY requirements.txt .
RUN pip3 install -r requirements.txt
COPY . /

CMD ["/picsellia/launch_processing.py"]
ENTRYPOINT ["python3"]
RUN chown -R 42420 /picsellia

The main things to understand here are:

  • The base image is a basic and lightweight Python Image, you can change this to whatever is needed to run your script.
  • We install the Picsellia Python package (a mandatory step to communicate with Picsellia).
  • We install all the packages from our requirements.txt file.
  • We set the entry point to our launch_processing.py file, which means that the script will be launched automatically.
  • We give write permissions on the whole directory.


We will not go over the launch_processing.py file in this tutorial, just leave it this way and it will run your script and capture its stdout

Now let's see our data-augmentation script!

A. Initialization

from picsellia import Client
from picsellia.sdk.dataset import DatasetVersion
from utils.data_augmentation import simple_rotation
import os

api_token = os.environ["api_token"]
organization_id = os.environ["organization_id"]
job_id = os.environ["job_id"]

client = Client(

job = client.get_job_by_id(job_id)

context = job.sync()["datasetversionprocessingjob"]
input_dataset_version_id = context["input_dataset_version_id"]
output_dataset_version = context["output_dataset_version_id"]
parameters = context["parameters"]

input_dataset_version: DatasetVersion = client.get_dataset_version_by_id(

You usually shouldn't have to change the first part of the script until the context variable definition. Here we just initialize the Picsellia client, fetch the Job that is running on Picsellia and associated with your Processing and retrieve its context.

The context is very important because it contains the information to:

  • Retrieve the DatasetVersion you are running your Processing on.
  • Retrieve the target DatasetVersion where our new images will lie.
  • Retrieve the potential parameters of our Processing.

Then we fetch the DatasetVersion thanks to the id we got from the context.


file_list = [os.path.join("data", path) for path in os.listdir("data")]

target_path = "rotated_data"
simple_rotation(file_list, target_path=target_path)

new_file_list = [os.path.join(target_path, path) for path in os.listdir(target_path)]

datalake = client.get_datalake()
data_list = datalake.upload_data(new_file_list, tags=["augmented", "processing"])

output_dataset: DatasetVersion = client.get_dataset_version_by_id(


If you are already familiar with our Python SDK, you should read this last piece easily.

In short, what it does is:

  • It downloads the image from our input DatasetVersion.
  • Rotate each file thanks to the function in data_augmentation.py and save them in a new directory.
  • Upload every rotated image in the Datalake.
  • Add all those Data to the DatasetVersion that has been created when launching the Processing.
from PIL import Image
from pathlib import Path
import os

def simple_rotation(filepaths: list, target_path: str):
    for path in filepaths:
        filename = Path(path).name
        image = Image.open(path)
        rotated_image = image.rotate(45)
        rotated_image.save(os.path.join(target_path, filename))

And finally here is our little function that rotates our images.

Now that you know how to organize your script to run your own Processing, let's build this so it becomes available on Picsellia and you can run it anytime.

2. Build and push Docker image

A. Using the Public Docker Hub

If you don't have a Private Docker Registry, we suggest you push your Docker images to the Docker Hub.

To do this, just log in with your usual credentials.

Then, open a shell in the processing folder of the repository and enter the following command:

docker build . -t <:image_name>:<tag>

For example, if I wanted to push to the Picsellia Docker Hub and I want to name my Docker image: processing-rotate, my command would look like this:

docker build . -t picsellpn/processing-rotate:1.0

Then proceed to push your Docker image with the following command:

docker push <:image_name>/<:tag>

B. Using a Picsellia Private Docker Registry

If you have a Picsellia Enterprise account, you can have access to a Private Docker Registry hosted by us.

You can ask us for your credentials, and we will provide you with the following:

  • A Registry URL, something like l76gd76h.gra7.container-registry.ovh.net
  • A username
  • A password

First, you have to login to your private registry with the following command, and enter your username and p password when asked to:

docker login REGISTRY_URL

Now build your image using the following command:

docker build . -t <:REGISTRY_URL>/picsellia/<:image_name>:<:tag>


Here picsellia is your username in your private registry so DO NOT change or remove it or it will not work

For example, if I wanted to build my processing-rotate Docker image I would do the following:

docker build . -t l76gd76h.gra7.container-registry.ovh.net/picsellia/processing-rotate:1.0

And finally, you can push your Docker image to the Private Registry:

docker push l76gd76h.gra7.container-registry.ovh.net/picsellia/processing-rotate:1.0

And tada πŸŽ‰ You now have a running Docker Image ready to use in your Picsellia Private Registry!