![]() ![]() Imagine waiting hours for your model to train, just to come back and see that your instance was shut down or imagine having to keep your laptop open for 12 hours, afraid that it will go into sleep mode and disconnect you. This means that you can lose your work and any training progress – also if you happen to close your tab, or log out by accident. Perhaps the biggest complaint of Colab users is that instances can be shut down (“preempted”) in the middle of a session, and disconnect if you're not actively connected to your notebook. Just a few of the drawbacks to Google Colab include: That then begs the question: Why Shouldn’t I Use Google Colab?ĭespite being a popular choice, Colab faces several issues that are deal breakers for many users. With free GPUs and storage linked to Google Drive, many users in the ML and data science communities find it a natural extension of their Google-centric web existence. In recent years, Google Colab has become a popular choice for cloud-backed notebooks. By taking care of all of the hardware and backend configuration, cloud-hosted environments also enable users to focus on their work, without any messy installation, configuration, or hardware purchases. In addition to powerful compute resources that might be difficult to get locally (or which would break the bank if you tried), cloud-hosted Jupyter environments come with features like cloud storage, model training and deployment capabilities, version control, and more. There's now a huge selection of options to choose from when it comes to cloud-hosted notebook services, so we decided to put together a list of the best available options today. ![]() Jupyter notebooks have become the go-to standard for exploring machine learning libraries and algorithms. ![]()
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