Quickstart tutorial

This quickstart follows 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.

Tip

👉 Building data pipelines in Orchest is easy! Check out our introductory video.

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 “Quickstart Pipeline” example Project through the UI:

Create your first Project in Orchest

To start, make sure you have installed Orchest or go to your Orchest Cloud account. Next, create a new Project named quickstart. After creating the Project, you will automatically be taken to the Pipeline editor. Note that Orchest has created an empty default Pipeline for you, called main.orchest.

Note

All code in this quickstart is written in Python, nevertheless, Orchest also supports other languages such as R.

Get California housing data

After creating the Project, it is still empty. Let’s create a new file to start building your Pipeline. Name your file get-data and choose the .py extension. Make sure to check the Create a new step for this file box to automatically create a Step for the file:

Note

The changes you make to the Pipeline (through the Pipeline editor) are saved automatically.

Now you can start writing some code through the familiar JupyterLab interface, simply press edit in JupyterLab and paste in the following code:

import orchest
import pandas as pd
from sklearn import datasets

# Explicitly cache the data in the "/data" directory since the
# kernel is running in a container, which are stateless.
# The "/data" directory is a special directory managed by Orchest
# to allow data to be persisted and shared across pipelines and
# even projects.
print("Dowloading California housing data...")
data = datasets.fetch_california_housing(data_home="/data")

# Convert the data into a DataFrame.
df_data = pd.DataFrame(data["data"], columns=data["feature_names"])
df_target = pd.DataFrame(data["target"], columns=["MedHouseVal"])

# Output the housing data so the next steps can retrieve it.
print("Outputting converted housing data...")
orchest.output((df_data, df_target), name="data")
print("Success!")

A few lines in the code above are highlighted to emphasize important nuts and bolts to get a better understanding of building pipelines in Orchest. These nuts and bolts are explained below:

Line 2 and 3 import third-party packages that need to be installed in the Environment. Environments define the execution environment in which your scripts are executed. To install pandas and sklearn simply head over to the Environments tab, add pip install pandas sklearn to the setup script and press Build. That is all it takes to build a container under the hood in which your script will be executed!

Line 11 caches the data in the /data directory. This is actually the userdir/data directory (from the Orchest GitHub repository) that gets mounted in the respective 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 reproducibility reasons.

Lastly, line 19 showcases the usage of the Orchest SDK to pass data between pipeline steps. Keep in mind that calling orchest.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 and press run all. After just a few seconds you should see that the Step completed successfully. Select the Step and check the logs to confirm - they contain the latest STDOUT of the get-data.py script.

Remember that running the code will output the converted housing data, so in the next Step you can now retrieve and explore that data!

Data exploration

Now that you have downloaded the data, the next Pipeline Step can explore it. Create another file called explore-data.ipynb (again make sure to check the box to automatically create a Step for it), 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 by copy-pasting the code, or using the upload file functionality from the built-in file-manager:

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 the toggle button in the Pipeline sessions section in the Pipeline editor.