Using Jupyter notebooks with Papermill and Dagster#

You can find the code for this example on Github

In this tutorial, we'll walk you through integrating a Jupyter notebook with Dagster using an example project. Before we get started, let's cover some common approaches to writing and integrating Jupyter notebooks with Dagster:

Doing standalone development in a Jupyter notebook. You could then create two Dagster assets: one for the notebook itself and another for data-fetching logic. This approach, which we'll use to start the tutorial, allows you to configure existing notebooks to work with Dagster.
Using existing Dagster assets as input to notebooks. If the data you want to analyze is already a Dagster asset, you can directly load the asset's value into the notebook. When the notebook is complete, you can create a Dagster asset for the notebook and factor any data-fetching logic into a second asset, if applicable. This approach allows you to develop new notebooks that work with assets that are already a part of your Dagster project.

By the end of this tutorial, you will:

Explore a Jupyter notebook that fetches and explores a dataset
Create a Dagster asset from the notebook
Create a second Dagster asset that only fetches the dataset
Load existing Dagster assets into a new Jupyter notebook

Dagster concepts#

In this guide, we'll use the following Dagster concepts:

Assets - An asset is a software object that models a data asset. The prototypical example is a table in a database or a file in cloud storage. An executed Jupyter notebook file can also be an asset! That's what we'll be creating in this guide.
Repositories - A Dagster repository is a collection of Dagster objects, including assets.
I/O managers - An I/O manager handles storing and loading assets. In this guide, we'll be using a special I/O manager to store executed Jupyter notebook files.

Prerequisites#

To complete this tutorial, you'll need:

To install Dagster and Jupyter. Run the following to install using pip:
```
pip install dagster notebook
```
Refer to the Dagster installation docs for more info.
To download the tutorial_notebook_assets Dagster example and install its dependencies:
```
dagster project from-example --name tutorial_notebook_assets --example tutorial_notebook_assets
cd tutorial_notebook_assets
pip install -e ".[dev]"
```
This example includes:
- A finished version of the tutorial project, which you can use to check out the finished project. This is the tutorial_finished subfolder.
- A template version of the tutorial project, which you can use to follow along with the tutorial. This is the tutorial_template subfolder. In this folder, you'll also find:
  - assets, a subfolder containing Dagster assets. We'll use /assets/__init__.py to write these.
  - notebooks, a subfolder containing Jupyter notebooks. We'll use /notebooks/iris-kmeans.ipynb to write a Jupyter notebook.

Step 1: Explore the Jupyter notebook#

In this tutorial, we'll analyze the Iris dataset, collected in 1936 by the American botanist Edgar Anderson and made famous by statistician Ronald Fisher. The Iris dataset is a basic example of machine learning because it contains three classes of observation: one class is straightforwardly linearly separable from the other two, which can only be distinguished by more sophisticated methods.

The /tutorial_template/notebooks/iris-kmeans.ipynb Jupyter notebook, which is already completed for you, does some analysis on the Iris dataset.

In the Jupyter notebook, we first fetch the Iris dataset:


# /tutorial_template/notebooks/iris-kmeans.ipynb

iris = pd.read_csv(
    "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
    names=[
        "Sepal length (cm)",
        "Sepal width (cm)",
        "Petal length (cm)",
        "Petal width (cm)",
        "Species",
    ],
)

Next, we'll perform some descriptive analysis to explore the dataset. If you execute these cells, several plots of the Iris dataset will be created:

Next, we conduct our K-means analysis:


estimator = sklearn.cluster.KMeans(n_clusters=3)
estimator.fit(
    iris[["Sepal length (cm)", "Sepal width (cm)", "Petal length (cm)", "Petal width (cm)"]]
)

Lastly, we plot the results of the K-means analysis. From the plots, we can see that one species of Iris is separable from the other two, but a more sophisticated model will be required to distinguish the other two species:

Like many notebooks, this example does some fairly sophisticated work, including producing diagnostic plots and a statistical model. For now, this work is locked away in the .ipynb format, only reproducible using a complex Jupyter setup, and only programmatically accessible within the notebook context. We'll address this in the remainder of the tutorial.

Step 2: Create a Dagster asset from the Jupyter Notebook#

By creating a Dagster asset from our notebook, we can integrate the notebook as part of our data platform. This enables us to make its contents more accessible to developers, stakeholders, and other assets in Dagster.

To create a Dagster asset from a Jupyter notebook, we can use the define_dagstermill_asset function. In /tutorial_template/assets/__init__.py add the following code snippet:


# /tutorial_template/assets/__init__.py

from dagstermill import define_dagstermill_asset
from dagster import file_relative_path

iris_kmeans_jupyter_notebook = define_dagstermill_asset(
    name="iris_kmeans_jupyter",
    notebook_path=file_relative_path(__file__, "../notebooks/iris-kmeans.ipynb"),
    group_name="template_tutorial",
)

If you are following along in the template code, uncomment the code block under the TODO 1 comment.

Using define_dagstermill_asset, we've created and returned a Dagster asset. Let's take a look at the arguments we provided:

name - This argument names the asset, in this case iris_kmeans_jupyter
notebook_path - This argument tells Dagster where to find the notebook the asset should use as a source. In this case, that's our /notebooks/iris-kmeans.ipynb file.
group_name - This optional argument places the asset into a group named template_tutorial, which is helpful for organization in Dagit.

When materialized, the iris_kmeans_jupyter asset will execute the notebook (/notebooks/iris-kmeans.ipynb) and store the resulting .ipynb file in a persistent location.

Step 3: Add a Dagster Repository and supply an I/O manager#

We want to execute our Dagster asset and save the resulting notebook to a persistent location. This is called materializing the asset and to do this, we need to add the asset to a Dagster repository.

Additionally, we need to provide a resource to the notebook to tell Dagster how to store the resulting .ipynb file. We'll use an I/O manager to accomplish this.

Open the /tutorial_template/repository.py file and add the following code:


# tutorial_template/repository.py

from dagstermill import local_output_notebook_io_manager
from dagster import repository, with_resources
from . import assets

@repository
def template_tutorial():
    return
        with_resources(
            load_assets_from_package_module(assets),
            resource_defs={
                "output_notebook_io_manager": local_output_notebook_io_manager,
            },
        ),

Let's take a look at what's happening here:

Using with_resources, we've provided resources to the assets in the repository. In this example, that's the local_output_notebook_io_manager resource.
This I/O manager, bound to the output_notebook_io_manager key, is responsible for handling the storage of the notebook asset's resulting .ipynb file.
Using load_assets_from_package_module, we've imported all assets in the assets module into the repository. This approach allows any new assets we create to be automatically added to the repository instead of needing to manually add them one by one.
Finally, we return the result of with_resources from the @repository decorated function. This allows Dagster to find the assets we've defined and instantiate the necessary resource when the asset is materialized.

Step 4: Materialize the notebook asset#

Now that you've created an asset, a resource, and a repository, it's time to materialize the notebook asset! Materializing an asset runs the op it contains and saves the results to persistent storage.

To start Dagit, Dagster's web interface, run the following in /tutorial_template:


dagster dev

Which will result in output similar to:


Serving dagit on http://127.0.0.1:3000 in process 70635

In your browser, navigate to http://127.0.0.1:3000. The page will display the notebook asset in the Asset Graph.
If you click the notebook asset, a sidebar containing info about the asset will slide out from the right side of the page. In the Description section of the panel is a View Source Notebook button:
This button allows you to view the notebook directly in Dagit. When clicked, Dagster will render the notebook - referenced in the notebook_path parameter - that'll be executed when the iris_kmeans_jupyter asset is materialized:
Click the Materialize button. To view the execution as it happens, click the View button in the alert that displays.

After the run completes successfully, you can view the executed notebook in Dagit. Click the asset again and locate the View Notebook button in the Materialization in Last Run section of the sidebar:

Click the button to display the executed notebook - specifically, the notebook that was executed and written to a persistent location:

Step 5: Add an upstream asset#

While our iris-kmeans notebook asset now materializes successfully, there are still some improvements we can make. The beginning of the notebook fetches the Iris dataset, which means that every time the notebook is materialized, the data is re-fetched.

To address this, we can factor the Iris dataset into its own asset. This will allow us to:

Use the asset as input to additional notebooks. This means all notebooks analyzing the Iris dataset will use the same source data, which we only have to fetch once.
Materialize notebooks without fetching data for each materialization. Instead of making potentially expensive API calls, Dagster can fetch the data from the previous materialization of the Iris dataset and provide that data as input to the notebook.

In this step, you'll:

Create the Iris dataset asset
Provide the Iris dataset as input to the notebook
Modify the notebook

Step 5.1: Create the Iris dataset asset#

To create an asset for the Iris dataset, add the following code to /tutorial_template/assets/__init__.py:


# /tutorial_template/assets/__init__.py

from dagstermill import define_dagstermill_asset
from dagster import asset, file_relative_path
import pandas as pd

@asset(
    group_name="template_tutorial"
)
def iris_dataset():
    return pd.read_csv(
        "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
        names=[
            "Sepal length (cm)",
            "Sepal width (cm)",
            "Petal length (cm)",
            "Petal width (cm)",
            "Species",
        ],
    )

If you're following along in the template tutorial, uncomment the code block under the TODO 2 comment.

Let's go over what's happening in this code block:

Using @asset, we create a standard Dagster asset. The name of the Python function (iris_dataset) is the name of the asset.
As with the iris_kmeans_jupyter asset, we set the group_name parameter to organize our assets in Dagit.
The body of the Python function fetches the Iris dataset, renames the columns, and outputs a Pandas DataFrame.

Step 5.2: Provide the iris_dataset asset to the notebook asset#

Next, we need to tell Dagster that the iris_datset asset is input data for the iris-kmeans notebook. To do this, add the ins parameter to the notebook asset:


# tutorial_template/assets/__init__.py
from dagstermill import define_dagstermill_asset
from dagster import asset, file_relative_path, AssetIn
import pandas as pd

# iris_dataset asset removed for clarity

iris_kmeans_jupyter_notebook = define_dagstermill_asset(
    name="iris_kmeans_jupyter",
    notebook_path=file_relative_path(__file__, "../notebooks/iris-kmeans.ipynb"),
    group_name="template_tutorial",
    ins={"iris": AssetIn("iris_dataset")}, # this is the new parameter!
)

If you are following along with the template tutorial, uncomment the line with the TODO 3 comment.

The ins parameter tells Dagster that the iris_dataset asset should be mapped to a variable named iris in our notebook. Recall that in our iris-kmeans notebook, the Iris dataset is assigned to a variable named iris.

Step 5.3: Modify the notebook#

We need to make a small change in our Jupyter notebook to allow Dagster to supply the iris_dataset asset as input. Behind the scenes, Dagster uses papermill to inject parameters into notebooks. papermill works by replacing a notebook cell with the parameters tag with a custom cell that can fetch the desired data.

To accomplish this, we need to tag the cell in the iris-kmeans notebook that fetches the Iris dataset. This allows us to replace the cell with the data-fetching logic that loads the iris_dataset asset and retain the ability to run the Jupyter notebook in a standalone context. We'll cover this in more detail later in the tutorial.

To add the parameters tag, you may need to turn on the display of cell tags in Jupyter:

In Jupyter, navigate to View > Cell Toolbar > Tags:
Click Add Tag to add a parameters tag:

Step 6: Materialize the assets#

Next, we'll materialize our iris_dataset and notebook assets.

In Dagit, open the Asset Graph page.
Click the Reload definitions button. This ensures that Dagit picks up the changes you made in the previous steps.
At this point, the iris_dataset asset should display above the iris_kmeans_jupyter asset as an upstream dependency:
Click the Materialize all button near the top right corner of the page, which will launch a run to materialize the assets.

That's it! You now have working Jupyter and Dagster assets!

Extra credit: Fetch a Dagster asset in a Jupyter notebook#

What if you want to do additional analysis of the Iris dataset and create a new notebook? How can you accomplish this without duplicating code or re-fetching data?

The answer is simple: use the iris_dataset Dagster asset!

In the Jupyter notebook, import the Dagster repository and use the RepositoryDefinition.load_asset_value function to load the data for the iris_dataset asset we created in Step 5.1: Create the Iris dataset asset:


from tutorial_template import template_tutorial

iris = template_tutorial.load_asset_value("iris_dataset")

Then, whenever you run the notebook using Jupyter, you'll be able to work with the iris_dataset asset:


jupyter notebook /path/to/new/notebook.ipynb

Behind the scenes, when load_asset_value is called, Dagster fetches the value of iris_dataset that was most recently materialized and stored by an I/O manager.

To integrate the new notebook, follow the steps from Step 5.3 to add the parameters tag to the cell that fetches the iris_dataset asset via load_asset_value.

Conclusion#

Now we have successfully created an asset from a Jupyter notebook and integrated it with our Dagster project! To learn about additional dagstermill features, refer to the Dagstermill integration reference.