Ray is a distributed open-source platform from the RISELab in U.C. Berkeley. It quickly scales python applications from a laptop to a cluster, which is now being adapted for production deployments. Ray has useful problem solving features such as rapid distribution, scheduling, and execution of tasks. This video demonstrates when one can use Ray, how it can be useful in projects, and how Ray can be used in several ML Libraries.
Pycon is a must for Python developers
Whether you are a beginner in using or developing Python Programming language or an expert aiming to expand your coding proficiency, attending the annual Python Conference is definitely a must! The recently concluded PyCon 2020 in Pittsburgh was a success despite the panic of the global pandemic. We have learned a lot from various educational talks from different speakers. In this video, we will be introduced to the so-called Ray, an open-source distributed framework from RISElab.
Why you need to know about Ray for Python and why
This Learn Python video is presented by Dean Wampler and he will demonstrate how Ray can easily scale Python applications from a laptop to a cluster. The video will highlight the motivations for Ray and how it actually works. Wampler will also discuss how Ray works behind the scenes and also its notable useful features like rapid distribution, scheduling, execution of tasks, and managing distributed stateful serverless computing. To learn more about Ray and when and how to use it on your Python projects, feel free to watch the video presentation below.