Autoreload of modules in IPython
Update your live Python code in IPython on-the-fly using %autoreload. You only need to set it up once:
Now, any changes in the imported modules you make are automatically reloaded prior to execution flow, thereby streamlining your workflow and eliminating the need for manual reloads.
Improve coding productivity significantly by ensuring that your code revisions are instantly active during your exploratory programming sessions in IPython.
When to reload specific modules only
While %autoreload 2 refreshes all imported modules, there could be times when you only want to reload one specific module to save time:
Once you run this, only the selected module_name will be reloaded before the next execution. This control over reloading selective modules can save execution time and minimize redundancy.
Always start with autoreload
To ensure that autoreload is always active when you start your IPython session, add it to your ipython_config.py:
If ipython_config.py doesn’t exist, create it in your IPython profile directory, and it would become part of your default IPython environment.
Careful with relading compiled modules
Keep in mind while the autoreload is an effective tool, use it judiciously. Remember that reloading large and complex compiled modules can have performance implications. It’s best to understand the impact of using autoreload with various types of modules.
Understanding autoreload options
Serial exploratory programming can make remembering all the available options of autoreload difficult. In such scenarios, use the command %autoreload?. It provides in-situ documentation, handy for on-the-fly reference.
If you are developing on thin modules or constantly iterating over the same one, you can optimize loading times by specifying the modules to reload expressly:
As a result, precious runtime isn't wasted on unused modules which are usually also reloaded by default.
For data scientists and researchers
As a data scientist or researcher, you sometimes make frequent small changes to your modules, especially when working with fast-evolving fields like SciPy or Machine Learning. The autoreload feature ensures that you always have the most current data and functionality at your disposal, offering an uninterrupted coding experience.
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