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

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

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.

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Numpy and Tensor has similar data structure, but CuPy has completely different data structure as the pool it uses is from the GPU, not the system RAM. But you can still do conversions and have a hybrid code running in CPU and GPU: import tensorflow as tf import cupy as cp # Create a CuPy array cupy_array = cp.array([1, 2, 3]) # Convert CuPy array to NumPy array numpy_array = cp.asnumpy(cupy_array) # Use NumPy array as input to TensorFlow tensor = tf.constant(numpy_array) # Perform TensorFlow operations on the tensor result = tf.math.square(tensor) print(result)

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no, thank you for choosing to watch my video :)

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I will try, kindly explain the problem you are facing.

@@rounakpaul001 i am working on somthing where i use yolo mediapipe and tensorflow i code enough to do my work and code working properly but the issue is i cant able to use gpu so when a video i pass throw my function the one frame take 300 ms and its two slow i have nivdea gpu 4060 install 12 version i always have issue in version what should i do

The methods are currently limited to NVIDIA GPUs. AMD has launched their new CUDA equivalent toolkits recently, I will learn it when I can get my hand on an AMD GUP system.

In short, mostly no. The script will always be run on the CPU, but your computation tasks you can do on GPU. The architecture of the CPU and GPU are different. Using python modules to run programs on the gpu may look similar to a normal python program, but underneath it's different. With some modifications you can probably run your functions on the gpu.

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By CuPy devs: "We cannot guarantee that CuPy works on other environments like MacOs, even if it looks like it is working" On mac, your best acceleration options are: Metal API or Vulkan Compute pipeline For learning and prototyping algorithms you can use google colab's TGPU.

If using Colab, no need to install CuPy on your M1 computer. The colab does the setup on the cloud VM by itself.

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Glad you liked the series, more software contents are coming soon. I will branch out to other languages also, like C++ is next in the list.

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Thank you for watching. This is introduction video for CUDA. I will make following videos in depth 😊

Wating for the next

Check out the latest one about CUDA CuPy th-cam.com/video/EqTt94s6WpU/w-d-xo.htmlsi=PXuEUamXn6brLcuZ

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More on the way

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