Python's GIL (Global Interpreter Lock), while promising thread safety within applications, can be a limiting factor when considering the need for truly parallel CPU bound tasks without the overhead of multiprocessing or only the I/O concurrency which is provided by asyncio.
In this talk we will discuss more about how we can bypass the GIL to improve performance and writing custom routines with Cython and the powerful nogil directive to identify and resolve performance bottlenecks.
As the culmination of this talk, we will explore how we can use Cython and nogil to implement an SGD (Stochastic Gradient Descent, an algorithm crucial to Deep Learning models) and benchmark it aganist pure Python implementation and see the performance gains.