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Top Google Colab Alternatives

author-profile-picEli Mernit
November 24 2024
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Google Colab is a popular online notebook platform, giving free (or inexpensive) access to GPUs through an online notebook IDE. Today, there are a number of alternatives to Colab which we’ll explore in detail. All the platforms in this article offer local or online notebooks, and some offer additional features like model deployment, file storage, and autoscaling.

Beam

Beam is a Python-focused cloud platform that makes the experience of working on the cloud feel just like working on your laptop. You can add FastAPI-style decorators to your code, and Beam will run your code on powerful servers in the cloud.

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This means you can work in a local Jupyter Notebook, while outsourcing certain cells to servers in the cloud.

Paperspace

Paperspace has been around since 2015 and is a classic player in the space. Paperspace offers GPU-backed notebooks, and also includes templates for popular use-cases. Paperspace offers a variety of hardware options, including the H100, A100-80, and A6000. You can also run CPU workloads on Paperspace notebooks, with pricing billed by the hour.

In addition to notebooks, Paperspace has a separate product called Deployments which offers hosted model serving. Their deployment product includes version control, storage, metrics, and integration into Git.

Deepnote

Deepnote is a modern notebook platform that goes beyond GPU-backed Jupyter. Unlike the other products in this article, Deepnote offers an entire analytics suite in addition to notebooks. Data teams can connect their data warehouse, run SQL queries directly on their data, or even write R instead of Python in the notebook.

Runpod

Runpod provides GPU-backed notebooks on a variety of hardware. Users can access hardware from A6000s to H100s, and connect it to a traditional notebook IDE. Runpod also offers file storage, deployment, and cloud resource monitoring. They also offer hourly pricing and a variety of templates for popular ML use-cases.

Lambda Labs

Lambda Labs is another GPU cloud, with a variety of hardware including H200s and H100s. Like Runpod and Paperspace, users can attach their notebooks and work in a traditional notebook IDE. Lambda offers a broader range of hardware, including bare-metal servers. For users looking to customize their hardware or run a large cluster, Lambda is a good choice.

Which Should You Choose? Key Factors

As you evaluate online notebook platforms, here are a few of the main factors to consider:

  • Access to hardware. If you’re experimenting with data, you’ll want access to a variety of hardware, including GPUs and CPUs. Workloads aren’t always the same, so you’ll want small CPU or GPU machines for lightweight tasks, and GPUs with a lot of VRAM for running state-of-the-art LLMs or training your own neural networks from scratch.
  • Reliability. One of the biggest peeves of using Colab is the occasional crashes that happen during a long-running job. With any online notebook, you’ll want to ensure that the environment is robust enough to stay connected during long-running jobs. It’s incredibly frustrating to be 8 hours into a training job, only for the notebook to suddenly crash and lose your work.
  • Distributed File Storage. When working in a notebook, you’ll often access large files or datasets. In order to keep your workflow iteration speed fast, you’ll want to store these files in distributed volumes geographically close to the notebook server. Ensure that the tool you choose has access to cloud storage, ideally at the edge.
  • Integration into Local Development Workflow. Most online notebooks are completely isolated from your local development workflow, but powerful things are possible when you combine them. For example, connecting a local notebook to a cloud GPU allows you to get access to cloud hardware without the instability of a cloud notebook.

There are many good choices for developing on cloud notebooks. Today, cloud notebooks often include powerful features like file storage, SQL support, various GPU options, and even tools to deploy your notebook code as persistent API endpoints. We hope this guide has given you a framework for deciding which notebook to use for your data science needs. As a starting point, we invite you to signup for Beam, and if you’d like to chat about any of the tools in this article, feel free to message us in our Slack community!

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