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We have used it to set up a small ML project. We decided to move away from it and to go back to Jupyter. We have found that file I/O is proprietary to Colab (e.

We have used it to set up a small ML project. We decided to move away from it and to go back to Jupyter. We have found that file I/O is proprietary to Colab (e.g., the Google Drive interface). We had to rewrite that part of the notebook when going back to Jupyter. Our main reason for moving away from it was the fact that it is difficult to run long jobs on Colab. It was good to start working on the project, but any real ML task took too long to finish, if at all. I see it as a teaching tool for people who do not have admin access to install a full python environment or that are interested in trying out basic things before investing time and effort in setting up Jupyter.

throwaway287391 8y ago
> Our main reason for moving away from it was the fact that it is difficult to run long jobs on Colab. It was good to start working on the project, but any real ML task took too long to finish, if at all. I like Jupyter notebooks (whether they're running in Colab or not) for data analysis and post-hoc model analysis, but I'd never recommend using them to actually train models in the real world, unless your real-world models are extremely fast to train (like, < 5 minutes). YMMV, but I constantly have to reconnect, restart the kernel etc. -- I consider them completely unreliable in terms of retaining anything I actually care about (i.e., any interesting model or result that can't be recomputed in <5 minutes). Of course, you can save snapshots along the way and resume from them, but to me the notebook interface has never really encouraged this sort of workflow -- IMO if you can't quickly shift+enter through every cell of the notebook when you first start it up and see all the same outputs you saw the first time in a couple minutes, it's probably not the right tool. (I brush up against or cross this line constantly myself, and it's always a painful experience.) Maybe they'll get better and work for this kind of thing in the future, but for now I wouldn't recommend them only for anything beyond analysis.

throwaway287391 8y ago

> Our main reason for moving away from it was the fact that it is difficult to run long jobs on Colab. It was good to start working on the project, but any real ML task took too long to finish, if at all. I like Jupyter notebooks (whether they're running in Colab or not) for data analysis and post-hoc model analysis, but I'd never recommend using them to actually train models in the real world, unless your real-world models are extremely fast to train (like, < 5 minutes). YMMV, but I constantly have to reconnect, restart the kernel etc. -- I consider them completely unreliable in terms of retaining anything I actually care about (i.e., any interesting model or result that can't be recomputed in <5 minutes). Of course, you can save snapshots along the way and resume from them, but to me the notebook interface has never really encouraged this sort of workflow -- IMO if you can't quickly shift+enter through every cell of the notebook when you first start it up and see all the same outputs you saw the first time in a couple minutes, it's probably not the right tool. (I brush up against or cross this line constantly myself, and it's always a painful experience.) Maybe they'll get better and work for this kind of thing in the future, but for now I wouldn't recommend them only for anything beyond analysis.