Python on NVDIA CUDA | GPU Acceleration Basics

Python can compile and run NVIDIA CUDA accelerated applications. In this tutorial series learn to use CUDA on Python with cupy and numba. Accelerate your applications by leveraging the parallel processing of a GPU.
Playlist: • Acceleration Basics wi...
Python Beginners Tutorial Playlist: • Python Beginners Tutorial
Numba CUDA: numba.readthedocs.io/en/stabl...
#python #cuda #cudaprogramming #gpu #advancedpython

[@mintoo2cool]

good stuff. thanks for the video

[@rajatpaul3728]

Nice teaching

[@rajatpaul3728]

It's a long and elaborate explanation,easy to learn 😊

[@rounakpaul001]

Thank you

[@akashtriz]

Tough topic, well explained.

[@rounakpaul001]

Glad to be of help

[@naveenchilakapati5578]

I have one doubt regarding activation of GPU in vs code , can I contact you via mail or WhatsApp bro

[@rounakpaul001]

I am on Discord, if you like to send messages/photos regarding this topic send friend request to:
dukerkp

[@urvikpatel1903]

Is it possible can you give tutorial regarding macOS for their own Gpu acceleration

[@rounakpaul001]

Mac machines historically has been difficult to get things done on. GPU functionality of numba and cupy modules don't extend to the MacOS. A c++ approach is required for the Metal API, either direct shader codes for Metal or via Vulkan translation layer.
You can still use the jitted functions of numba on MacOS for some performance gain in Python. Optionally PyTorch has some of its methods using the Metal API, so if PyTorch is enough for your application you can use it for acceleration in Python.