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.
I wished i had knew about this before. Numpy uses a lot of ram and cpu. I was looking for a way to shift some of the data preprocessing workload onto the GPU. Does tensorflow recognise cupy arrays as valid input data types like numpy arrays? Great video.
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)
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.
@@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
