Data Warehousing and Machine Learning

13 November 2021

Google Colab

Filed under: Machine Learning — Vincent Rainardi @ 9:05 am

I started using Google Colab about 6 months ago, as my laptop is 1 core 4 GB. I’m now on Colab Pro (£9.72 a month) which is fantastic. So in this article I’d like to share my experience doing machine learning on Colab.

The Look and Feel

I love the table of contents on the left panel! Jupyter Notebook on local install don’t have it. Well they do (link) but it’s so complicated and so manual. On Google Colab it is available out of the box, without us doing anything (it detects the headings in the markdown, i.e. #, ##, ###, etc.) We use it to jump to different sections in our notebook:

Google Colab also supports dark theme as you can see above. On the left most column (marked A) we have search, code snippet, variables and files. They are very useful when doing development. Psst, if you click the Colab icon on the top left (B), you’ll see all your notebooks in Google Drive!).

On the bottom left (C) there are 2 very useful icons. The first one is Command Pallette where every single Jupyter command and shortcut is listed! The second one is the Terminal, where you can run shell commands (only on Colab Pro).

Google Drive

When starting on Colab almost everyone asked “How do I upload files” and spent time searching.

There you go! Saves you from searching 🙂

Tip: don’t use GPU when you connect to Google Drive for the first time. You could get “403 error daily limit exceeded” (link). Turn off GPU on the Notebook Settings (see below) and you’ll be ok.

Performance

The main reason we use Colab is because it’s free and it’s fast. Yes it’s free. And yes you get GPU and TPU! In my case it’s 10-20x faster than my laptop. So why pay £9.72/month for Pro? (including VAT) Because it disconnects after 1.5 hours, that’s why. To prevent that we need to keep typing! With Pro we also get more power and RAM.

When doing neural network (RNN, CNN) or RL, Colab is an absolute godsend. I can’t do those NN or RL models on my laptop (well it’s 6 years old, no wonder 🙂 Colab is very good with anything to do with Keras. Because of its TPU. Well, with scikit learn too.

Tip: unless you are doing network layers, using TPU doesn’t mean it’s faster than GPU or None. I was using classical models (Logistic Rregression, Random Forest, XG Boost, etc) and found that TPU or GPU doesn’t make it faster. And there were moments when I could not connect using TPU, but with  None I could connect.

Also, most often I don’t need to use the High RAM setting. Only once I ran out of memory, that was when processing a lot of images (augmentation).

Executing Code

After executing code, you can tell how long each cell runs by hovering over the play button (or the green check box). Below you can see “29 seconds” under the green checkbox so why bother hovering? Because once you edit the cell the 29s is gone! But if you hover over you can still see it 🙂

A lot of times when I’m running code it fails. After fixing that particular cell, and testing it, I used to run it from the top again (using “Run before”). Now once I fixed that cell, I use “Run after” to execute the rest of the code, because if you are like me, there will be other cells failing down there 🙂

Sometimes a model runs for a long time and when you click Stop (the rectangle on the left of the cell) it doesn’t stop. In that case I use “Restart runtime” (the last blue arrow above). It’s an absolute godsend! Whatever the code is doing, it will stop. Psst, you will also see this “Restart runtime” if you keep clicking the Stop button on the left of the cell 3 times.

Settings

On the top right of the screen, next to your initial, you can see a wheel icon. This is for Settings.

The dark theme is set here, along with the font, etc. Ever wonder why the tab is 2 spaces not 4 spaces? Well you can set it here. Do you want to see line numbers? Set it here. Mind you it’s the line number within a cell, not the cell number.

Editing

When editing, the first botton (A) makes it a header, i.e. it begins with a “#”. Keep clicking that icon and it changes to level 2 and level 3. Botton B formats whatever we are highlighting as a code and botton C inserts a link. You can even insert an image (D), a line (E) or an Emoji (G). Botton F is LaTeX (it’s a type setting thing, link) and H is to put the preview below rather than on the right.

You can get the link to a particular cell (button I), and you add a comment on individual cell (J). I use button K often, it is for closing the editor (well you can also press Escape). The last button (L) is open up the cell.

File Menu

On the file menu you can open or upload a notebook, rename or save a copy, and download the notebook. I press control-S (save) often, but it’s actually not necessary because the notebook is automatically saving itself very often.

When working on a project for a few days or weeks, I download the notebook to my PC, having different file name every time (file1, file2, file3, etc.) So I can see any previous code that I’ve written and copy it back to my current code if required.

If the automatic save fails and it says something like “auto sync fails because of another copy”, click Runtime menu, Manage sessions. You’ll see two sessions open for the same notebook file. Terminate one of them and the automatic saving is fixed.

There you go I hope it was useful. Any comments, corrections or advise you can contact me on vrainardi@gmail.com.
Happy coding in machine learning!

1 Comment »

  1. […] have been using Google Colab (see my article here) and Jupyter to build and train various machine learning models including LR, KNN, PCA, SVM, […]

    Pingback by Automating Machine Learning using Azure ML | Data Warehousing and Machine Learning — 26 November 2021 @ 8:07 am | Reply


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