{"id":1151925,"date":"2025-01-13T17:19:17","date_gmt":"2025-01-13T09:19:17","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1151925.html"},"modified":"2025-01-13T17:19:20","modified_gmt":"2025-01-13T09:19:20","slug":"python%e5%a6%82%e4%bd%95%e8%b0%83%e7%94%a8gpu%e8%bf%90%e7%ae%97","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1151925.html","title":{"rendered":"python\u5982\u4f55\u8c03\u7528gpu\u8fd0\u7b97"},"content":{"rendered":"

\"python\u5982\u4f55\u8c03\u7528gpu\u8fd0\u7b97\"<\/p>\n

Python\u8c03\u7528GPU\u8fd0\u7b97\u4e3b\u8981\u901a\u8fc7\u4ee5\u4e0b\u51e0\u79cd\u65b9\u5f0f\u5b9e\u73b0\uff1a\u4f7f\u7528CUDA\u3001\u4f7f\u7528OpenCL\u3001\u4f7f\u7528\u4e13\u95e8\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u5982TensorFlow\u548cPyTorch<\/strong>\u3002\u5176\u4e2d\uff0cCUDA<\/strong>\u662f\u4e00\u79cd\u7531NVIDIA\u5f00\u53d1\u7684\u5e76\u884c\u8ba1\u7b97\u5e73\u53f0\u548c\u7f16\u7a0b\u6a21\u578b\uff0c\u5b83\u4f7f\u5f97GPU\u53ef\u4ee5\u9ad8\u6548\u5730\u5904\u7406\u8ba1\u7b97\u5bc6\u96c6\u578b\u4efb\u52a1\u3002TensorFlow<\/strong>\u548cPyTorch<\/strong>\u662f\u76ee\u524d\u6700\u6d41\u884c\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff0c\u5b83\u4eec\u90fd\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684\u63a5\u53e3\u6765\u8c03\u7528GPU\u8fdb\u884c\u8ba1\u7b97\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528CUDA\u548c\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u8fdb\u884cGPU\u8fd0\u7b97\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528CUDA<\/h3>\n<\/p>\n

1\u3001\u5b89\u88c5CUDA Toolkit<\/h4>\n<\/p>\n

\u8981\u4f7f\u7528CUDA\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5CUDA Toolkit\u3002CUDA Toolkit\u5305\u542b\u4e86\u5f00\u53d1GPU\u52a0\u901f\u5e94\u7528\u7a0b\u5e8f\u6240\u9700\u7684\u6240\u6709\u5de5\u5177\u548c\u5e93\u3002\u53ef\u4ee5\u4eceNVIDIA\u7684\u5b98\u65b9\u7f51\u7ad9\u4e0b\u8f7d\u5e76\u5b89\u88c5\u3002<\/p>\n<\/p>\n

2\u3001\u5b89\u88c5cuDNN<\/h4>\n<\/p>\n

cuDNN\u662fNVIDIA\u63d0\u4f9b\u7684\u7528\u4e8e\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u7684GPU\u52a0\u901f\u5e93\u3002\u5b83\u63d0\u4f9b\u4e86\u5e38\u89c1\u7684\u6df1\u5ea6\u5b66\u4e60\u64cd\u4f5c\uff08\u5982\u5377\u79ef\u3001\u6c60\u5316\u7b49\uff09\u7684\u9ad8\u5ea6\u4f18\u5316\u5b9e\u73b0\u3002\u53ef\u4ee5\u4eceNVIDIA\u7684\u5b98\u65b9\u7f51\u7ad9\u4e0b\u8f7d\u5e76\u5b89\u88c5\u3002<\/p>\n<\/p>\n

3\u3001\u7f16\u5199CUDA\u4ee3\u7801<\/h4>\n<\/p>\n

\u53ef\u4ee5\u4f7f\u7528CUDA C\/C++\u7f16\u5199GPU\u52a0\u901f\u4ee3\u7801\uff0c\u7136\u540e\u901a\u8fc7Python\u8c03\u7528\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684CUDA\u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

#include <cuda_runtime.h><\/p>\n
#include <iostream><\/p>\n
__global__ void add(int n, float *x, float *y) {<\/p>\n
    int index = blockIdx.x * blockDim.x + threadIdx.x;<\/p>\n
    int stride = blockDim.x * gridDim.x;<\/p>\n
    for (int i = index; i < n; i += stride)<\/p>\n
        y[i] = x[i] + y[i];<\/p>\n
}<\/p>\n
int mAI<\/a>n(void) {<\/p>\n
    int N = 1<<20;<\/p>\n
    float *x, *y;<\/p>\n
    cudaMallocManaged(&x, N*sizeof(float));<\/p>\n
    cudaMallocManaged(&y, N*sizeof(float));<\/p>\n
    for (int i = 0; i < N; i++) {<\/p>\n
        x[i] = 1.0f;<\/p>\n
        y[i] = 2.0f;<\/p>\n
    }<\/p>\n
    add<<<1, 256>>>(N, x, y);<\/p>\n
    cudaDeviceSynchronize();<\/p>\n
    std::cout << "y[0] = " << y[0] << std::endl;<\/p>\n
    std::cout << "y[N-1] = " << y[N-1] << std::endl;<\/p>\n
    cudaFree(x);<\/p>\n
    cudaFree(y);<\/p>\n
    return 0;<\/p>\n
}<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u901a\u8fc7PyCUDA\u8c03\u7528CUDA\u4ee3\u7801<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528PyCUDA\u5e93\u5728Python\u4e2d\u8c03\u7528CUDA\u4ee3\u7801\u3002PyCUDA\u662f\u4e00\u4e2aPython\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u8c03\u7528CUDA API\u7684\u63a5\u53e3\u3002\u9996\u5148\u9700\u8981\u5b89\u88c5PyCUDA\uff1a<\/p>\n<\/p>\n
pip install pycuda<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\u53ef\u4ee5\u5728Python\u4e2d\u8c03\u7528CUDA\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pycuda.autoinit<\/p>\n
import pycuda.driver as drv<\/p>\n
import numpy as np<\/p>\n
from pycuda.compiler import SourceModule<\/p>\n
mod = SourceModule("""<\/p>\n
__global__ void add(int n, float *x, float *y) {<\/p>\n
    int index = blockIdx.x * blockDim.x + threadIdx.x;<\/p>\n
    int stride = blockDim.x * gridDim.x;<\/p>\n
    for (int i = index; i < n; i += stride)<\/p>\n
        y[i] = x[i] + y[i];<\/p>\n
}<\/p>\n
""")<\/p>\n
add = mod.get_function("add")<\/p>\n
n = np.int32(1024)<\/p>\n
x = np.random.randn(1024).astype(np.float32)<\/p>\n
y = np.random.randn(1024).astype(np.float32)<\/p>\n
add(n, drv.InOut(x), drv.InOut(y), block=(256, 1, 1), grid=(4, 1))<\/p>\n
print(y)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528TensorFlow<\/h3>\n<\/p>\n
TensorFlow\u662f\u4e00\u4e2a\u6d41\u884c\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff0c\u5b83\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684\u63a5\u53e3\u6765\u8c03\u7528GPU\u8fdb\u884c\u8ba1\u7b97\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528TensorFlow\u8fdb\u884cGPU\u8fd0\u7b97\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5TensorFlow<\/h4>\n<\/p>\n
\u9996\u5148\u9700\u8981\u5b89\u88c5TensorFlow\u3002\u53ef\u4ee5\u4f7f\u7528pip\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install tensorflow<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u68c0\u67e5TensorFlow\u662f\u5426\u68c0\u6d4b\u5230GPU<\/h4>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u8fd0\u884c\u4ee5\u4e0b\u4ee3\u7801\u68c0\u67e5TensorFlow\u662f\u5426\u68c0\u6d4b\u5230GPU\uff1a<\/p>\n<\/p>\n
import tensorflow as tf<\/p>\n
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4f7f\u7528TensorFlow\u8fdb\u884cGPU\u8fd0\u7b97<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528TensorFlow\u7684API\u8fdb\u884cGPU\u8fd0\u7b97\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import tensorflow as tf<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u5e38\u91cf\u5f20\u91cf<\/strong><\/h2>\n
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0], dtype=tf.float32)<\/p>\n
b = tf.constant([5.0, 4.0, 3.0, 2.0, 1.0], dtype=tf.float32)<\/p>\n
\u5728GPU\u4e0a\u8fdb\u884c\u52a0\u6cd5\u8fd0\u7b97<\/strong><\/h2>\n
with tf.device('\/GPU:0'):<\/p>\n
    c = a + b<\/p>\n
print(c)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528PyTorch<\/h3>\n<\/p>\n
PyTorch\u662f\u53e6\u4e00\u4e2a\u6d41\u884c\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff0c\u5b83\u4e5f\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684\u63a5\u53e3\u6765\u8c03\u7528GPU\u8fdb\u884c\u8ba1\u7b97\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528PyTorch\u8fdb\u884cGPU\u8fd0\u7b97\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5PyTorch<\/h4>\n<\/p>\n
\u9996\u5148\u9700\u8981\u5b89\u88c5PyTorch\u3002\u53ef\u4ee5\u4ecePyTorch\u7684\u5b98\u65b9\u7f51\u7ad9\u4e0b\u8f7d\u5e76\u5b89\u88c5\u3002<\/p>\n<\/p>\n
2\u3001\u68c0\u67e5PyTorch\u662f\u5426\u68c0\u6d4b\u5230GPU<\/h4>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u8fd0\u884c\u4ee5\u4e0b\u4ee3\u7801\u68c0\u67e5PyTorch\u662f\u5426\u68c0\u6d4b\u5230GPU\uff1a<\/p>\n<\/p>\n
import torch<\/p>\n
print("Is GPU available: ", torch.cuda.is_available())<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4f7f\u7528PyTorch\u8fdb\u884cGPU\u8fd0\u7b97<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528PyTorch\u7684API\u8fdb\u884cGPU\u8fd0\u7b97\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import torch<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u5f20\u91cf<\/strong><\/h2>\n
a = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0], dtype=torch.float32)<\/p>\n
b = torch.tensor([5.0, 4.0, 3.0, 2.0, 1.0], dtype=torch.float32)<\/p>\n
\u5c06\u5f20\u91cf\u79fb\u52a8\u5230GPU<\/strong><\/h2>\n
a = a.cuda()<\/p>\n
b = b.cuda()<\/p>\n
\u5728GPU\u4e0a\u8fdb\u884c\u52a0\u6cd5\u8fd0\u7b97<\/strong><\/h2>\n
c = a + b<\/p>\n
print(c)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4f7f\u7528OpenCL<\/h3>\n<\/p>\n
OpenCL\u662f\u4e00\u79cd\u7528\u4e8e\u7f16\u5199\u8de8\u5e73\u53f0\u5e76\u884c\u7a0b\u5e8f\u7684\u6846\u67b6\uff0c\u652f\u6301\u5305\u62ecCPU\u3001GPU\u548c\u5176\u4ed6\u52a0\u901f\u5668\u5728\u5185\u7684\u591a\u79cd\u8ba1\u7b97\u8bbe\u5907\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528OpenCL\u8fdb\u884cGPU\u8fd0\u7b97\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5PyOpenCL<\/h4>\n<\/p>\n
\u9996\u5148\u9700\u8981\u5b89\u88c5PyOpenCL\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528pip\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install pyopencl<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7f16\u5199OpenCL\u5185\u6838\u4ee3\u7801<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528OpenCL C\u7f16\u5199\u5185\u6838\u4ee3\u7801\uff0c\u7136\u540e\u901a\u8fc7Python\u8c03\u7528\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684OpenCL\u5185\u6838\u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
__kernel void add(__global float *x, __global float *y, __global float *z) {<\/p>\n
    int i = get_global_id(0);<\/p>\n
    z[i] = x[i] + y[i];<\/p>\n
}<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u901a\u8fc7PyOpenCL\u8c03\u7528OpenCL\u4ee3\u7801<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528PyOpenCL\u5e93\u5728Python\u4e2d\u8c03\u7528OpenCL\u4ee3\u7801\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import pyopencl as cl<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efaOpenCL\u4e0a\u4e0b\u6587\u548c\u961f\u5217<\/strong><\/h2>\n
context = cl.create_some_context()<\/p>\n
queue = cl.CommandQueue(context)<\/p>\n
\u521b\u5efaOpenCL\u5185\u6838<\/strong><\/h2>\n
kernel_code = """<\/p>\n
__kernel void add(__global float *x, __global float *y, __global float *z) {<\/p>\n
    int i = get_global_id(0);<\/p>\n
    z[i] = x[i] + y[i];<\/p>\n
}<\/p>\n
"""<\/p>\n
program = cl.Program(context, kernel_code).build()<\/p>\n
\u521b\u5efa\u8f93\u5165\u548c\u8f93\u51fa\u7f13\u51b2\u533a<\/strong><\/h2>\n
n = 1024<\/p>\n
x = np.random.randn(n).astype(np.float32)<\/p>\n
y = np.random.randn(n).astype(np.float32)<\/p>\n
z = np.empty_like(x)<\/p>\n
x_buf = cl.Buffer(context, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR, hostbuf=x)<\/p>\n
y_buf = cl.Buffer(context, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR, hostbuf=y)<\/p>\n
z_buf = cl.Buffer(context, cl.mem_flags.WRITE_ONLY, z.nbytes)<\/p>\n
\u6267\u884c\u5185\u6838<\/strong><\/h2>\n
program.add(queue, (n,), None, x_buf, y_buf, z_buf)<\/p>\n
\u8bfb\u53d6\u7ed3\u679c<\/strong><\/h2>\n
cl.enqueue_copy(queue, z, z_buf).wait()<\/p>\n
print(z)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u5185\u5bb9\uff0c\u6211\u4eec\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u4f7f\u7528Python\u8c03\u7528GPU\u8fdb\u884c\u8fd0\u7b97\u7684\u51e0\u79cd\u5e38\u89c1\u65b9\u5f0f\uff0c\u5305\u62ec\u4f7f\u7528CUDA\u3001\u4f7f\u7528TensorFlow\u3001\u4f7f\u7528PyTorch\u548c\u4f7f\u7528OpenCL<\/strong>\u3002\u6bcf\u79cd\u65b9\u5f0f\u90fd\u6709\u5176\u9002\u7528\u7684\u573a\u666f\u548c\u4f18\u7f3a\u70b9\uff0c\u7528\u6237\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u5f0f\u8fdb\u884cGPU\u8fd0\u7b97\u3002CUDA<\/strong>\u9002\u5408\u9ad8\u6027\u80fd\u8ba1\u7b97\u548c\u5bf9\u786c\u4ef6\u6709\u6df1\u5165\u4e86\u89e3\u7684\u7528\u6237\uff0cTensorFlow\u548cPyTorch<\/strong>\u5219\u66f4\u9002\u5408\u6df1\u5ea6\u5b66\u4e60\u548c\u4eba\u5de5\u667a\u80fd<\/a>\u9886\u57df\u7684\u7528\u6237\uff0cOpenCL<\/strong>\u9002\u7528\u4e8e\u9700\u8981\u8de8\u5e73\u53f0\u652f\u6301\u7684\u5e76\u884c\u8ba1\u7b97\u4efb\u52a1\u3002\u65e0\u8bba\u9009\u62e9\u54ea\u79cd\u65b9\u5f0f\uff0c\u90fd\u53ef\u4ee5\u5927\u5927\u63d0\u5347\u8ba1\u7b97\u6548\u7387\uff0c\u5145\u5206\u5229\u7528GPU\u7684\u5f3a\u5927\u6027\u80fd\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u68c0\u67e5\u6211\u7684\u7cfb\u7edf\u662f\u5426\u652f\u6301GPU\u8fd0\u7b97\uff1f<\/strong>
\u8981\u68c0\u67e5\u7cfb\u7edf\u662f\u5426\u652f\u6301GPU\u8fd0\u7b97\uff0c\u53ef\u4ee5\u5b89\u88c5NVIDIA\u7684CUDA Toolkit\u6216AMD\u7684ROCm\uff0c\u5e76\u4f7f\u7528\u76f8\u5173\u547d\u4ee4\u9a8c\u8bc1GPU\u7684\u53ef\u7528\u6027\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u5b89\u88c5tensorflow<\/code>\u6216torch<\/code>\u7b49\u5e93\uff0c\u5e76\u4f7f\u7528tf.config.list_physical_devices('GPU')<\/code>\u6216torch.cuda.is_available()<\/code>\u6765\u786e\u8ba4\u662f\u5426\u80fd\u8bc6\u522bGPU\u3002<\/p>\n
\u5728Python\u4e2d\u4f7f\u7528GPU\u8fd0\u7b97\u9700\u8981\u54ea\u4e9b\u5e93\u6216\u6846\u67b6\uff1f<\/strong>
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