问题
I was currently using google-colab and on the getting started pages, we see:
Local runtime support Colab supports connecting to a Jupyter runtime on your local machine. For more information, see our documentation.
So, when I saw the documentation I connected my colab notebook to the local runtime, after the installations,etc by using the connected tab.
And when I access the memory info:
!cat /proc/meminfo
The output is as follows:
MemTotal: 3924628 kB
MemFree: 245948 kB
MemAvailable: 1473096 kB
Buffers: 168560 kB
Cached: 1280300 kB
SwapCached: 20736 kB
Active: 2135932 kB
Inactive: 991300 kB
Active(anon): 1397156 kB
Inactive(anon): 560124 kB
Active(file): 738776 kB
Inactive(file): 431176 kB
Unevictable: 528 kB
Mlocked: 528 kB
Which is the memory info for my pc, so certainly the access from the notebook is to my pc? Then how is it any different from my local jupyter-notebook? Now, I can't use the high memory environment of 13 Gigs, nor can I have GPU access.
Would be great if someone can explain!
回答1:
The main advantages to using Colab with a local backend stem from Drive-based notebook storage: Drive commenting, ACLs, and easy link-based sharing of the finished notebook.
When using Jupyter, sharing notebooks requires sharing files. And, accessing your notebooks from a distinct machine requires installing Jupyter rather than loading a website.
回答2:
The only benefit is to keep your notebooks in Google Drive.
- you can share them easily
- you have automatic history/versioning
- people can comment on your notebooks
You also have headings with collapsible outline, and probably cleaner UI (if you prefer Colab styling).
来源：https://stackoverflow.com/questions/53514243/google-colab-local-runtime-use