![]() It will cause the code to pause when it reaches this line and open the debugger view. You can inspect what's going on or even run the code step by step to see what each line of code is doing.Īdding a breakpoint is as simple as adding t_trace(). If you have a complicated piece of code which isn't working as expected for certain inputs, you can add breakpoints in the code so when you execute the code, it will pause at that breakpoint. If you want to turn off this then just use %pdb off. This is helpful when you don't know why an exception is occuring so you can pause at that point, inspect values and see what went wrong. In this example, we raised an exception and as you see that the debugger automatically starts when you hit an exception and I was able to inspect the value of i in that state. You can use the follow magic command in your Notebook to turn on debugging, so that whenever your code hits into an Exception, the code will pause and the debugger view will be opened for you to explore what caused the exception %pdb on Turning on debbuging on Exception ipdb in Google ColabĪs ipdb is not packaged with Python itself so you need to install the package and import it. Lets get into how to use ipdb in Google Colab. This debugger doesn't have any visual aspect to it as IDE do, but they allow you to perform similar capabilites such as "Continue", "Step into functions", evaluate expressions, jump to line etc. Python already has pdb which is a command line based debugger but for Jupyter there is ipdb which is similar to pdb but for Jupyter notebooks. Gladly there is a way to debug code within Notebook albeit not as convenient as IDE but still good enough. In those cases debuggers are super handy. Yes, you can always use print() as a quick substitute for debugger but sometimes you need to explore the problem more in the state it occurred, learn how the program is being executed etc. While I start to use Notebooks, one thing I've missed from the regular dev environment in an IDE is a debugger. Google Colab allows you to directly create a Notebook and run algorithms on GPU (if you are doing Machine Learning) instead of having to deal with Jupyter or driver installations etc. ![]() Jupyter notebooks in general have been really great for literate programming and exploratory learning. ![]()
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