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Colab VS Jupyter Notebooks
Notebooks · 23 Apr 2024, 08:42 · 6
Which notebook does the work better?
My preference has always been colab, especially with the ability to configure the processor to GPU, and TPU when handling large data and models. I also love using Colab for the ease of sharing my work on GitHub and finally, my favorite is Colab AI, as much as it's not always accurate, I enjoy fixing overlooked errors through it.
With low resources available locally, surely Google colab would be preferable for most tasks.. so many pre installed libraries. No worries of having a weak PC, gpu ready to use, Google drive storage available, an Ai assistant.
.......
Unfortunate bit is can't store non preinstalled libraries, has session limits of 12hrs, only provides quite small storage 😥 (about 32gb) as per now which can't handle most image related tasks with heavy datasets, also with poor Internet connection your certainly off.
Fortunately most of these weaknesses are the strengths of using jupyter. Though also there are much more comparisons and many alternatives to both.. Guess discussion for another time 😉🙃
Totally on board with Colab. Its GPU/TPU options are a lifesaver for heavy lifting. And don't forget auto-save—no fear of losing progress! It's the ultimate package for smooth notebook work.
Google Colab is better for quick prototyping, leveraging GPU/TPU resources, and also easy collaboration. On the other hand, Jupyter Notebooks offer more customization options, a wider ecosystem of tools and extensions, and flexibility in terms of environment setup.
And also I have used Google Collab which makes it easier for me to integrate with Google Drive, making it easy to share notebooks with collaborators. Multiple users can work on the same notebook simultaneously, facilitating real-time collaboration.
The choice between the two depends on your specific requirements.
I concur with this. Can we also talk about running Miniconda with Jupyter Notebook Integration on VSCode. My current Miniconda base environment has almost all the ML libraries I need be it Computer Vision, NLP etc
A combination of these that is Miniconda, Jupyter Notebook Integration in VSCode, and a well-configured environment with ML libraries also offers a robust platform for developing and deploying AI models efficiently.
The choice between Jupyter Notebooks and Google Colab primarily comes down to the needs of your particular project, the scope of collaboration required, the computational requirements, and data security issues. Google Colab is a great option if accessibility, cooperation, and open computing resources are important to you. Jupyter Notebooks are probably more ideal if you need to maintain control over your computing environment, work with sensitive data, or need a reliable, long-term solution without runtime restrictions.