Google Colab got here out as a boon for machine studying practitioners — not solely to resolve the storage issues of working with a big dataset but in addition monetary constraints of affording a system that meets information science work necessities. The Jupyter pocket book setting operating on the cloud with no requirement for a separate setup was designed to equip ML fans to study, run, and share their coding with only a click on. Its free entry to python libraries, 50 GB arduous drive house, 12 GB RAM, and a free GPU makes it an ideal guess for ML practitioners.
Despite all these benefits, in actuality, Google Colab comes with a number of disadvantages and limitations, proscribing a machine studying practitioners’ coding functionality to run with none pace bumps. Let’s have a look at these options of Google Colab that may spoil machine studying experiences.
Also Read: The Beginner’s Guide To Using Google Colab
Drawbacks Of Google Colab
Closed-Environment: Anyone can use Google Colab to jot down and run arbitrary Python code within the browser. However, it’s nonetheless a comparatively closed setting, as machine studying practitioners can solely run the python bundle already pre-added on the Colab. There is not any means that one can add their very own python bundle and begin operating the code. Hence, the platform can present widespread instruments however will not be appropriate for specialisation.
Repetitive Tasks: Imagine one has to repeat the identical set of actions repeatedly to execute a process — not solely will it’s exhausting, however it is going to additionally eat a whole lot of time. Similarly, for each new session within the Google Colab, a programmer should set up the entire particular libraries that aren’t included with the usual Python bundle.
No Live-Editing: Writing a code and sharing the identical together with your associate or a crew means that you can collaborate. However, the choice for stay modifying is totally lacking in Google Colab, which restricts two folks to jot down, or edit codes on the similar time. Hence, it additional results in a whole lot of forwards and backwards re-sharing. Additionally, this function is offered by its different rivals, together with CoCalc.
Saving & Storage Problems: Uploaded recordsdata are eliminated when the session is restarted as a result of Google Colab doesn’t present a persistent storage facility. So, if the system is turned off, the information can get misplaced, which generally is a nightmare for a lot of. Moreover, as one makes use of the present session in Google Storage, a downloaded file that’s required for use later must be saved earlier than the session’s expiration. In addition to that, one should at all times be logged in to their Google account, contemplating all Colaboratory notebooks are saved in Google Drive.
Limited Space & Time: The Google Colab platform shops recordsdata in Google Drive with a free house of 15GB; nonetheless, engaged on greater datasets requires extra space, making it tough to execute. This, in flip, can maintain a lot of the advanced capabilities to execute.
Google Colab permits customers to run their notebooks for at most 12 hours a day, however so as to work for an extended time period, customers must entry the paid model, i.e. Colab Pro, which permits programmers to remain related for 24 hours. Finally, the much less talked about downside of the platform is its incapacity to execute codes or run correctly on a cellular system.
Google Colab entered the market with a pure focus to offer machine studying practitioners with a platform and instruments to advance their machine studying capabilities. However, over time, the quantity, depth, and high quality of information modified, and so did ML practitioners’ necessities to seek out options to advanced issues. Coming out with a paid version is simple, however for the bigger good, it must be upgraded and freely accessible to anybody for all the machine studying ecosystem to develop.
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