Segmind vs Google Colab
Google Colab allows simple access to compute for running your Jupyter notebooks. It comes with a modified Jupyter interface and internally uses GCP to schedule workers.
Stopping the session saves the environment (in the docker container). So you can get back to where you were, once you restart your notebook. You are going to save tons of time not reinstalling packages. On Colab, you need to install all specific libraries which do not come with a standard environment and repeat this for every session.
Start with any of the leading machine learning and deep learning frameworks including PyTorch, Tensorflow and more, configured for each kind of hardware environment.
Choose anything from a single CPU machine to a powerful multi-GPU machine. Changing your machine takes just 2 minutes.
It is difficult to work with large datasets as you have to download and store them on Google drive with 15 GB of free space. Moreover, latency issues creep in as the Drive storage cannot handle throughput-intensive workloads that require low latency.
Work on pure Jupyter user interface, no modifications. Advance JupyterLab interface includes features found in traditional IDEs such as text editors, terminal along with the traditional Jupyter notebook. You can also choose VS Code IDE to work on your code.
Complete control over the lifecycle of a VM running your notebook. No timeouts, run your code as long as it takes.