Getting Started with Colab for Python and Data Science

In this video I review some tricks and tips for CoLab. Transferring files, installing libraries, using the pro terminal mode, and dealing with timeouts.

1:02 GitHub Badge for Google Colab
1:43 Choose an Instance
1:54 Installing Libraries
2:06 Terminal (pro)
2:51 Running Pip Install
3:45 Running UNIX Commands
3:52 Colab File Space
6:10 Uploading and Downloading Files
7:13 Accessing GDrive from Google Colab
8:48 Handling Google Colab Timeouts

Code from this video:

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[zicoabhidey]

Colab has been a blessing. Using for ML to generic python projects.

[StefanBrock_PL]

Very interesting. Seems a suitable platform for running a basic ML course

[bthkn]

Thx for the video. I'm only using colab for a university course

[SaschaRobitzki]

Great, I was just wondering how to get the Colab badge to show up in my notebooks.

[D.]

I love your videos. I recently found you. And want to take all of your classes. Question, if you have time; Is our code in the cloud private??? Is there any chance of it being copied if it’s not all running locally? I mean yes. But what are the chances? Using google cloud or collab?

[charlesrongione]

Hey thank you this was very informative. Do you know if you can get the colab code completer locally? Just the code completer, not the whole environment. I love it

[patricecarbonneau]

Colab is nice for teaching and as s backup. But it won't replace my workhorse GPUs which started as a 1080, then a 2080 and now an A5000. Even Colab Pro only has enough ram for smaller ideas. But it's still hugely useful.