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.
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
- using a remote desktop or SSH
- 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
Resource Limits
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
