Easy Python Parallelism using Ray

In this talk, we explore the Ray library and how it can be used to easily parallelize Python code. We cover the basics of using Ray Core, including installation, spinning up a local Ray cluster, and running remote actors and tasks. We also discuss some real-world examples of using Ray Core in practice, including optimizing the performance of pi estimation and using multiple web scrapers in parallel. Additionally, we touch on some advanced topics such as distributed computing on AWS and explore other offerings by the Ray package. By the end of this talk, you will have a solid understanding of how to use Ray for efficient parallel processing in your own Python applications. Presenter: Dean Wetherby Dean Wetherby is a Machine Learning Engineer with 20 years of experience building and scaling machine learning applications. He has a strong background in computer vision and information technology, and has worked on various projects related to face/speaker recognition, optical character recognition (OCR), and object detection and tracking. Dean has been programming in Python for over a decade.