Quickstart tutorial¶
Tip
👉 Building data pipelines in Orchest is really that easy! Check out our introductory video.
This quickstart will follow an example explaining how to build data science pipelines in Orchest and touches upon some core principles that will be helpful when you get to building your own pipelines. The example pipeline will download the sklearn California housing dataset, explore the data, train some classifiers, and in the final step collect the results from those classifiers.

The resulting pipeline from this quickstart.¶
For the impatient¶
As Miguel Grinberg would say: “If you are the instant gratification type, and the screenshot at the top of this article intrigued you, then head over to the Github repository for the code used in this article. Then come back to learn how everything works!”
To get started in Orchest you can import the GitHub repository URL
https://github.com/orchest/quickstart
through the UI:
Your first project¶
To start, make sure you have installed Orchest and started it:
# Make sure to be in the root-level orchest directory.
./orchest start
Next create a new project named quickstart
. After creating the project, you will see that it
does not yet have any pipelines.
Note
All code in this quickstart is written in Python, nevertheless, we do also support other languages such as R.
Get California housing data¶
The logical next step is to create the first pipeline called California housing
and open the
pipeline editor. This will automatically boot an interactive session so
you can interactively edit the Python script we create (the other steps will be Jupyter Notebooks!):
Create a new step by clicking: + new step.
Enter a Title and File path, respectively
Get housing data
andget-data.py
.
Note
The changes you make to the pipeline (through the pipeline editor) are saved automatically.
Now we can start writing our code through the familiar JupyterLab interface, simply press edit in JupyterLab (making sure you have the step selected) and paste in the following code:
1import orchest
2import pandas as pd
3from sklearn import datasets
4
5# Explicitly cache the data in the "/data" directory since the
6# kernel is running in a Docker container, which are stateless.
7# The "/data" directory is a special directory managed by Orchest
8# to allow data to be persisted and shared across pipelines and
9# even projects.
10print("Dowloading California housing data...")
11data = datasets.fetch_california_housing(data_home="/data")
12
13# Convert the data into a DataFrame.
14df_data = pd.DataFrame(data["data"], columns=data["feature_names"])
15df_target = pd.DataFrame(data["target"], columns=["MedHouseVal"])
16
17# Output the housing data so the next steps can retrieve it.
18print("Outputting converted housing data...")
19orchest.output((df_data, df_target), name="data")
20print("Success!")
As you can see, we have highlighted a few lines in the code to emphasize important nuts and bolts to get a better understanding of building pipelines in Orchest. These nuts and bolts are explained below:
First we start with explaining line
11
in which we cache the data in the/data
directory. This is actually theuserdir/data
directory (from the Orchest GitHub repository) that gets bind mounted in the respective Docker container running your code. This allows you to access the data from any pipeline, even from pipelines in different projects. Data should be stored in/data
not only for sharing purposes, but also to make sure that jobs do not unnecessarily copy the data when creating the snapshot for reprodicibility reasons.Secondly, line
19
showcases the usage of the Orchest SDK to pass data between pipeline steps. Keep in mind that callingorchest.transfer.output()
multiple times will result in the data getting overwritten, in other words: only output data once per step!
To run the code, switch back to the pipeline editor, select the step and press run selected steps. After just a few seconds you should see that the step completed successfully. Let’s check the logs to confirm - the logs contain the latest STDOUT of the script.
Remember that running the code will output the converted housing data, so in the next step we can now retrieve and explore that data!
Data exploration¶
Now that we have downloaded the data, the next pipeline step can explore it. Create another pipeline
step with Title Data exploration
and File path explore-data.ipynb
, and connect the two
pipeline steps.
You can get the code for this pipeline step from the explore-data.ipynb
file in the GitHub
repository.
Maybe you already noticed the imports in the previous step:
import orchest
import pandas as pd
from sklearn import datasets
These dependencies are satisfied by default, because the environments are based on the Jupyter Docker Stacks which come pre-installed with common data science packages.
Note
Adding additional dependencies (even system level dependencies) can be done by using environments.
Finalizing the pipeline¶
To end up with the final pipeline, please refer to the For the impatient section to import the pipeline. You can also build the pipeline from scratch yourself!

A successful pipeline run of the final pipeline.¶
Note
The interactive session does not shut down automatically and thus the
resources will keep running when editing another pipeline, you can shut down the session manually
by clicking on the shut down button. Of course all resources are shut down when you shut down
Orchest with ./orchest stop
.