이 글은 AI 딥러닝에 핵심적으로 사용되는 CUDA를 손쉽게 사용하기 위해 CuPy 와 사용법을 간략히 알아본다.
설치 방법
pip install cupy-cuda11x
pip install nvcc4jupyter
개발하기
import numpy as np
import cupy as cp
x_gpu = cp.array([1, 2, 3])
x_cpu = np.array([1, 2, 3])
l2_cpu = np.linalg.norm(x_cpu)
x_gpu = cp.array([1, 2, 3])
l2_gpu = cp.linalg.norm(x_gpu)
x_on_gpu0 = cp.array([1, 2, 3, 4, 5])
x_on_gpu0 = cp.array([1, 2, 3, 4, 5])
with cp.cuda.Device(0):
x_gpu_0 = cp.ndarray([1, 2, 3]) # create an array in GPU 0
레퍼런스
- Installation — CuPy 13.3.0 documentation
- cupy/cupy: NumPy & SciPy for GPU (github.com)
- Recommendations for free resources for learning CUDA for C/C++ : r/CUDA (reddit.com) (ref #1, ref #2, ref #3, ref #4)
- [D] Practice CUDA without an Actual NVIDIA GPU! : r/MachineLearning (reddit.com)
- notY0rick/cuda_practice: My own repository containing the codes I wrote to practice CUDA programming. (github.com)
- Pricing - Genesis Cloud
- Welcome to nvcc4jupyter’s documentation! — nvcc4jupyter 1.2.1 documentation
- Learning CUDA C++ without a GPU using Kaggle or Colab : r/CUDA (reddit.com)
- Can I use CUDA on VSCode? : r/learnmachinelearning (reddit.com)
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