Google Colab (2024)

What is Colaboratory?

Colaboratory, or “Colab” for short, is a product from Google Research. Colab allows anybody to write and execute arbitrary python code through the browser, and is especially well suited to machine learning, data analysis and education. More technically, Colab is a hosted Jupyter notebook service that requires no setup to use, while providing access free of charge to computing resources including GPUs.

Is it really free of charge to use?

Yes. Colab is free of charge to use.

Seems too good to be true. What are the limitations?

Colab resources are not guaranteed and not unlimited, and the usage limits sometimes fluctuate. This is necessary for Colab to be able to provide resources free of charge. For more details, see Resource Limits

Users who are interested in more reliable access to better resources may be interested in Colab Pro.

Resources in Colab are prioritized for interactive use cases. We prohibit actions associated with bulk compute, actions that negatively impact others, as well as actions associated with bypassing our policies. The following are disallowed from Colab runtimes:

• file hosting, media serving, or other web service offerings not related to interactive compute with Colab
• downloading torrents or engaging in peer-to-peer file-sharing
• remote control such as SSH shells, remote desktops, remote UIs
• connecting to remote proxies
• mining cryptocurrency
• running denial-of-service attacks
• password cracking
• using multiple accounts to work around access or resource usage restrictions
• creating deepfakes

Additional restrictions exist for paid users here.

What is the difference between Jupyter and Colab?

Jupyter is the open source project on which Colab is based. Colab allows you to use and share Jupyter notebooks with others without having to download, install, or run anything.

Where are my notebooks stored, and can I share them?

Colab notebooks are stored in Google Drive, or can be loaded from GitHub. Colab notebooks can be shared just as you would with Google Docs or Sheets. Simply click the Share button at the top right of any Colab notebook, or follow these Google Drive file sharing instructions.

If I share my notebook, what will be shared?

If you choose to share a notebook, the full contents of your notebook (text, code, output, and comments) will be shared. You can omit code cell output from being saved or shared by using Edit > Notebook settings > Omit code cell output when saving this notebook. The virtual machine you’re using, including any custom files and libraries that you’ve setup, will not be shared. So it’s a good idea to include cells which install and load any custom libraries or files that your notebook needs.

Can I import an existing Jupyter/IPython notebook into Colab?

Yes. Choose "Upload notebook" from the File menu.

How can I search Colab notebooks?

You can search Colab notebooks using Google Drive. Clicking on the Colab logo at the top left of the notebook view will show all notebooks in Drive. You can also search for notebooks that you have opened recently using File > Open notebook.

Where is my code executed? What happens to my execution state if I close the browser window?

Code is executed in a virtual machine private to your account. Virtual machines are deleted when idle for a while, and have a maximum lifetime enforced by the Colab service.

How can I get my data out?

You can download any Colab notebook that you’ve created from Google Drive following these instructions, or from within Colab’s File menu. All Colab notebooks are stored in the open source Jupyter notebook format ( .ipynb).

How can I reset the virtual machine(s) my code runs on, and why is this sometimes unavailable?

Selecting Runtime > Disconnect and delete runtime to return all managed virtual machines assigned to you to their original state. This can be helpful in cases where a virtual machine has become unhealthy e.g. due to accidental overwrite of system files, or installation of incompatible software. Colab limits how often this can be done to prevent undue resource consumption. If an attempt fails, please try again later.

Why does drive.mount() sometimes fail saying "timed out", and why do I/O operations in drive.mount()-mounted folders sometimes fail?

Google Drive operations can time out when the number of files or subfolders in a folder grows too large. If thousands of items are directly contained in the top-level "My Drive" folder then mounting the drive will likely time out. Repeated attempts may eventually succeed as failed attempts cache partial state locally before timing out. If you encounter this problem, try moving files and folders directly contained in "My Drive" into sub-folders. A similar problem can occur when reading from other folders after a successful drive.mount(). Accessing items in any folder containing many items can cause errors like OSError: [Errno 5] Input/output error. Again, you can fix this problem by moving directly contained items into sub-folders.
Note that "deleting" files or subfolders by moving them to the Trash may not be enough; if that doesn't seem to help, make sure to also Empty your Trash.

Why does "Mount Drive" sometimes insert code into the notebook?

Mounting Google Drive on Colab allows any code in your notebook to access any files in your Google Drive. We usually require that users manually grant this access every time they connect to a new runtime by adding a code cell to the notebook. This ensures that the user fully understands the permissions being granted to the notebook.
In some cases, we only require Google Drive authorization once, and automatically re-mount Google Drive during future sessions. To protect your files, we only allow this when a notebook passes multiple checks. For example, any notebooks which have been edited by another user do not automatically mount Google Drive.

Why do Drive operations sometimes fail due to quota?

Google Drive enforces various limits, including per-user and per-file operation count and bandwidth quotas. Exceeding these limits will trigger Input/output error as above, and show a notification in the Colab UI. A typical cause is accessing a popular shared file, or accessing too many distinct files too quickly. Workarounds include:

• Copy the file using drive.google.com and don't share it widely so that other users don't use up its limits.
• Avoid making many small I/O reads, instead opting to copy data from Drive to the Colab VM in an archive format (e.g. .zip or.tar.gz files) and unarchive the data locally on the VM instead of in the mounted Drive directory.
• Wait a day for quota limits to reset.

Why do Drive operations sometimes fail due to storage quota?

Google Drive imposes a limit on how much data can be stored in it by each user. If Drive operations are failing withInput/output error and a notification says storage quota has been exceeded, delete some files using drive.google.com and Empty your Trash to reclaim the space. It might take a little while for the reclaimed space to be available in Colab.

If you'd like to purchase more Drive space, visit Google Drive. Note that purchasing more space on Drive will not increase the amount of disk available on Colab VMs. Subscribing to Colab Pro will.

Why aren’t resources guaranteed in Colab?

In order to dynamically offer powerful GPUs at scale for a low price, Colab needs to maintain the flexibility to adjust usage limits and hardware availability dynamically.

In the version of Colab that is free of charge, access to expensive resources like GPUs is heavily restricted. For the paid version of Colab, we target giving our users high value per their spend.

You can purchase guaranteed resources via GCP Marketplace to use with Colab.

What are the usage limits of Colab?

Colab is able to provide resources free of charge in part by having dynamic usage limits that sometimes fluctuate, and by not providing guaranteed or unlimited resources. This means that overall usage limits as well as idle timeout periods, maximum VM lifetime, GPU types available, and other factors vary over time. Colab does not publish these limits, in part because they can (and sometimes do) vary quickly.

You can relax Colab's usage limits by purchasing one of our paid plans here. These plans have similar dynamics in that resource availability may change over time.

You can purchase guaranteed resources via GCP Marketplace to use with Colab.

What types of GPUs are available in Colab?

The types of GPUs that are available in Colab vary over time. This is necessary for Colab to be able to provide access to these resources free of charge.

You can access premium GPUs subject to availability by purchasing one of our paid plans here.

If you would like access to specific dedicated hardware, explore using GCP Marketplace Colab.

How long can notebooks run in Colab?

Colab prioritizes interactive compute. Runtimes will time out if you are idle.

In the version of Colab that is free of charge notebooks can run for at most 12 hours, depending on availability and your usage patterns. Colab Pro, Pro+, and Pay As You Go offer you increased compute availability based on your compute unit balance.

In general, notebooks can run for at most 12 hours, depending on availability and your usage patterns. You can expect to experience backend termination if you exhaust your available compute units on a Pro, Pro+, or Pay As You Go plan.

Colab Pro+ supports continuous code execution for up to 24 hours if you have sufficient compute units. Idle timeouts only apply if code execution terminates.

You can fully relax any runtime limits and idle timeouts by purchasing a dedicated VM at GCP Marketplace.

How much memory is available in Colab?

In the version of Colab that is free of charge you are able to access VMs with a standard system memory profile.

In paid versions of Colab you are able to access machines with a high memory system profile subject to availability and your compute unit balance.

Note that memory refers to system memory. All GPU chips have the same memory profile.

How can I get the most out of Colab?

Consider closing your Colab tabs when you are done with your work, and avoid opting for GPUs or extra memory when it is not needed for your work. This will make it less likely that you will run into usage limits within Colab. You can always purchase more compute via Pay As You Go should you hit limits.

For more information on getting the most out of the paid version of Colab, see Making the Most of your Colab Subscription.

I saw a message saying my GPU is not being utilized. What should I do?

Colab offers optional accelerated compute environments, including GPU and TPU. Executing code in a GPU or TPU runtime does not automatically mean that the GPU or TPU is being utilized. To avoid hitting your GPU usage limits, we recommend switching to a standard runtime if you are not utilizing the GPU. Choose Runtime > Change Runtime Type and set Hardware Accelerator to None.

For examples of how to utilize GPU and TPU runtimes in Colab, see the Tensorflow With GPU and TPUs In Colab example notebooks.

What browsers are supported?

Colab works with most major browsers, and is most thoroughly tested with the latest versions of Chrome, Firefox and Safari.

How is this related to colaboratory.jupyter.org?

In 2014 we worked with the Jupyter development team to release an early version of the tool. Since then Colab has continued to evolve, guided by internal usage.

What about other programming languages?

Colab focuses on supporting Python and its ecosystem of third-party tools. We're aware that users are interested in support for other Jupyter kernels (eg R or Scala). We would like to support these, but don't yet have any ETA.

I found a bug or have a question, who do I contact?

Open any Colab notebook. Then go to the Help menu and select ”Send feedback...”.

Why prompt to enable third-party cookies?

Colab uses HTML iframes and service workers hosted on separate origins in order to display rich outputs securely. Browsers require enabling third-party cookies to use the service workers within iframes. An alternative to enabling third-party cookies for all sites is to allow the following hostname in your browser settings: googleusercontent.com.

How do I change the editor font?

Colab uses a generic monospace font for the editor. You can configure what font family is used for monospace in most modern browsers. Here's a few common ones:

• In Firefox, follow the steps provided in the Firefox support documents to configure the "Monospace" font.
• In Chrome, navigate to "chrome://settings/fonts" and modify the section labeled "Fixed-width font".

Does Colab support Python 2?

Python 2 is no longer supported in Colab. For information on migrating your code from Python 2 to Python 3, see Porting Python 2 Code to Python 3.

Where can I learn more about the paid versions of Colab?

There is an FAQ on the sign-up page.

How does billing work for the paid versions of Colab?

Information for Colab Pro, Pro+, and Pay As You Go, including pricing and how upgrades are handled, can be found at the sign-up page.

How do I access Colab with a Workspace account?

Access to Colab for Workspace users is controlled by the Workspace on/off control accessible to your organization's administrator.

Workspace for Education organizations are required to obtain parental consent for students' (under the age of 18) use of Additional Services with their Google Workspace for Education account. This can be achieved with this notice template. Please be sure to include Colab in the list of additional services.

For more information, please read our Help Center article “Communicating with Parents and Guardians about Google Workspace for Education”. Note that Google accounts for children under the age of 13 are not supported for Colab use at this time.