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Choose local Jupyter when you want notebooks to use your computer’s environment and resources; choose Google Colab when you want to start coding in a browser without installing Jupyter locally. Colab notebooks use the Jupyter format, but Colab supplies a hosted runtime with its own availability and sharing rules. This guide shows how to start with either option, decide between them, and share notebooks without assuming that recipients inherit your machine or setup.
What is the difference between Jupyter and Colab?
Project Jupyter is an open-source project with multiple tools and interfaces. Its Notebook interface is a web application for creating documents that combine executable code with narrative text, equations, and visualizations. “Jupyter” therefore refers to an ecosystem, not just one hosted service or interface. Project Jupyter’s installation guide directs users to instructions for the specific tool they want.
Google Colab is a hosted notebook service that runs Python in a browser. Colab notebooks are Jupyter notebooks, and users can save them in Google Drive or load them from GitHub. You can begin without installing Jupyter on your computer. Colab’s welcome notebook is a practical starting point.
| What matters | Local Jupyter | Google Colab |
|---|---|---|
| Setup | Install and launch a Jupyter tool locally; the installation steps depend on the tool. Jupyter installation guide. | Open it in a browser; Google describes Colab as requiring no setup. Colab welcome notebook. |
| Compute and files | Uses the local environment and resources when the notebook runs locally. Colab can also connect to a local runtime. Local-runtime documentation. | Uses a hosted runtime. Compute availability, hardware, and usage limits can vary. Colab FAQ. |
| Sharing | Sharing depends on the Jupyter interface or deployment you use; there is no single sharing behavior for the whole ecosystem. Jupyter installation guide. | Notebooks can be shared through Drive, but the recipient does not get your running machine, custom files, or installed libraries. Colab storage and sharing FAQ. |
| Team controls | JupyterHub is one option in the ecosystem for multi-user interactive computing. Jupyter installation guide. | Colab Enterprise is a managed, collaborative Google Cloud environment with security and compliance capabilities. Colab Enterprise overview. |
There is no universal winner: choose based on whether you value local control and an existing environment, browser convenience and notebook sharing, or managed team controls. This comparison does not cover every hosted Jupyter provider or installation distribution.
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How do I start a notebook?
Run Jupyter Notebook on your computer
- Install the Jupyter tool you intend to use by following its tool-specific instructions in Jupyter’s installation guide. There is not one installation route that fits every operating system, package manager, or user.
- Open a terminal in the directory you want to work in and run
jupyter notebook. - When the server opens its web application in your browser, use the file dashboard to work with notebooks under the directory where you launched it. Jupyter documents this launch process in Running the Notebook.
To execute a notebook from the command line instead of opening the interactive interface, Jupyter documents jupyter execute notebook.ipynb. The same running guide explains this route.
Start in Google Colab
- Open Colab’s welcome notebook, or create a notebook in Colab. You can also open a notebook from Drive or GitHub.
- Run a cell with its play button, or use Command+Enter on macOS or Ctrl+Enter on Windows and Linux.
- Write explanatory text alongside code as needed. Colab notebooks support rich text, images, HTML, and LaTeX in addition to code.
Notebook cells share kernel state: a variable created in one cell can be used by later cells. If you run cells out of order, the current state may no longer match the order shown in the notebook, which can cause confusing results. When debugging, check which cells have run and in what order.
Where are my notebooks stored, and can I share them?
Colab notebooks can be stored in Google Drive or loaded from GitHub. Sharing a notebook shares its saved contents, including text, code, outputs, and comments. It does not transfer the runtime machine or any custom files and libraries you set up there. Google’s storage and sharing FAQ describes these distinctions.
To make a shared notebook useful to someone else, put the steps to install dependencies and load data in notebook cells instead of relying on changes made only in your current runtime. If saved outputs should not be included, use Colab’s setting to omit cell output when saving. Before sharing, inspect outputs and comments as well as code: they are part of the notebook contents.
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Mounting Google Drive lets notebook code access files in your Drive. Treat that access deliberately: review the notebook and understand what its code does before granting access.
For local Jupyter, sharing depends on how you installed or deployed the tool; Project Jupyter covers multiple interfaces and deployment models rather than one universal sharing workflow. For team-based interactive computing, JupyterHub is one option in that ecosystem. Google Cloud also offers Colab Enterprise as a managed collaborative environment with security and compliance capabilities; see the Colab Enterprise overview.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the limitations of Google Colab?
Colab’s compute resources are dynamic and not guaranteed. The hardware offered and the behavior of limits can change with availability and usage patterns, and runtime sessions may end. Check the current Colab FAQ for service details rather than planning around a particular resource always being available.
Selecting a GPU or TPU does not by itself make code use that accelerator. Google’s FAQ cautions: “Executing code in a GPU or TPU runtime does not automatically mean that the GPU or TPU is being utilized.” Use an accelerator only if your framework and workload can use it, and verify that the workload is actually running on it.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Colab’s session durations and resource behavior are plan- and availability-dependent. Avoid treating a stated maximum duration as a guaranteed uninterrupted session; save important work and make notebooks able to restore dependencies and data after a runtime ends.
Can I connect Colab to a local runtime?
Yes. Colab’s frontend can send notebook execution to a Jupyter server running on your computer, so the notebook uses your local resources rather than a hosted Colab runtime. Follow Google’s local-runtime connection instructions to configure the connection.
This option combines Colab’s browser interface with local execution, but it does not remove the work or responsibility of running Jupyter yourself. You must install and configure the local environment, and you should understand Jupyter’s server security model before connecting. Do not expose a local server or grant access without understanding who or what can reach it.
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