Hugo Herter

While iterating rapidly on Python code, we want to see the result of our changes rapidly. In this talk, we will review the different techniques available to reload Python code. We will see how they work and when each is the best fit.

Please note that this talk replaces one titled "The future of Python's asyncio" that was due to have been given by Jonathan Slenders, who has sent his apologies but is now unable to attend as he has fallen ill. We wish him a speedy recovery.

The talk will cover both cold and hot reload techniques:

Cold reload techniques reset the application state between each reload. Examples include Django and Flask's autoreload tools.

Hot reload techniques keep the application state despite the code changing. These include Jupyter kernels and 'reloadr' [1], an open-source module developed by the speaker to allow stateful hot code reloading.

AMENDMENT: Please note that this talk replaces one titled "The future of Python's asyncio" that was due to have been given by Jonathan Slenders, who has sent his apologies but is now unable to attend as he has fallen ill. We wish him a speedy recovery.

[1] https://github.com/hoh/reloadr

While iterating rapidly on Python code, we want to see the result of our changes rapidly. In this talk, we will review the different techniques available to reload Python code. We will see how they work and when each is the best fit.

The talk will cover both cold and hot reload techniques:

Cold reload techniques reset the application state between each reload. Examples include Django and Flask's autoreload tools.

Hot reload techniques keep the application state despite the code changing. These include Jupyter kernels and 'reloadr' [1], an open-source module developed by the speaker to allow stateful hot code reloading.

[1] https://github.com/hoh/reloadr