Projects

What can you do with projects?

Projects do a lot, from shared data sources across multiple notebooks to native version control. Here are some highlights:

• Projects contain notebooks and therefore can be used to organize related work (e.g., EDA notebook, ML notebook)
• Projects define your environment, including the Python version, required libraries, and machine specifications (i.e. RAM and number of cores).
• Projects have their own file system and provide access to database integrations.

Creating a project

From the left panel in your Workspace, you can click the "+" symbol next to PROJECTS to create a new project, either from scratch or from Deepnote's built-in templates. In addition, clicking on the ellipses beside a project will provide options for creating, duplicating, and moving projects (and more).

Don't forget that you can arrange your projects into folders here, too. Just drag and drop them to suit how you like to organize your work.

Let's move to the newly created Project panel now. Notice that the left panel has changed and you are focused on the context of your Project.

Learning the most important bits

The left sidebar contains your notebooks. Click Settings in the top-right corner to open integrations, files, and machine settings in the right sidebar.

Notebooks

The Notebooks section can contain multiple notebooks. This is helpful since multiple notebooks are often needed to accomplish an overall goal. Read more about Deepnote's powerful notebooks here.

Integrations

The Integrations section is all about hooking up resources to your project. It's where you connect to databases/warehouses, buckets, Docker container registries, and secrets. Your collaborators can share these connections without having to bother with additional setup.

Files

Think of this section as your local computer's file system. It's snappy like that. Drag a CSV into it or upload the other scripts and files you need. The file system is a shared space for all notebooks within a single project. To learn more about working with the file system, read our general guidelines here.

Environment

Open Settings and find the Machine section at the bottom of the right sidebar.

Click the Python environment selector to open Environment selection, where you can choose a built-in environment or set up a custom Docker image.

Machine: Click the machine selector to compare CPU, memory, and pricing options. Learn more about machines in Deepnote.

Built-in environments: Choose a Python environment in Environment selection. The data science environments include preinstalled libraries. The default is Python 3.13 for data science.

Initialization notebook: There are times when you want to run some "starter" code before your notebook is used. You can place such code in a notebook called 'Init'. Read more about setting up custom project initialization.

Incoming connections: Open More options beside the machine controls to find Incoming connections. Enable it when you need to run a web server in Deepnote, such as Streamlit or TensorBoard.

Terminals

We all need a CLI every now and then, even if we are notebook lovers. You can create terminals by clicking the + button in the Notebooks section and selecting Create a new terminal from the dropdown menu. Terminals appear below your notebooks in the same section. Read more about terminals here.

Project templates

In case you'll find yourself attaching the same integrations, or using the same environment in most of your projects, you can save the project as a template. To do that, click on the dropdown option of your project, and click "Add to templates".

Going deeper with projects

Projects can do so much more. We encourage you to check out how to use comments, how to version projects and view their history, and how to connect to GitHub.