TensorFlow-object-detection-tutorial VS cupy

I am trying to accomplish something similar to this with yolo5. https://github.com/pythonlessons/TensorFlow-object-detection-tutorial

The plethora of packages, including DSLs for compute and MLIR.

https://developer.nvidia.com/how-to-cuda-python

https://cupy.dev/

No.

You may want to check out cupy

https://cupy.dev/

I think I would agree with you. In my opinion, that already exists and is decently mature. CuPy [0] for Python and CUDA.jl [1] for Julia are both excellent ways to interface with GPU that don't require you to get into the nitty gritty of CUDA. Both do their best to keep you at the Array-level abstraction until you actually need to start writing kernels yourself and even then, it's pretty simple. They took a complete GPU novice like me and let me to write pretty performant kernels without having to ever touch raw CUDA.

[0] https://cupy.dev/

I did not expect anything interesting, but this is actually cool.

> A full implementation of the NumPy API. Not something "NumPy-like" — just literally the NumPy API, with the same functions and the same arguments.

I suppose it's like https://cupy.dev/

What was the difference in runtime performance, and did you try CuPy?

https://github.com/cupy/cupy :

> CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. CuPy acts as a drop-in replacement to run existing NumPy/SciPy code on NVIDIA CUDA or AMD ROCm platforms.

Projects using CuPy:

It's more effort though. You will likely have to format your data in specific ways for the GPU to efficiently process it. I've done this kind of thing with PyTorch tensors, but there are also math-specific libraries like CuPy. If you only have millions, Numpy should be fine.

I'd also recommend checking out CuPy which aims to fully re-implement the Numpy api for CUDA GPUs, while taking advantage of Nvidia's specialized libraries like cuBLAS, cuRAND, cuSOLVER etc. The tradeoff being that it only works with Nvidia GPUs.