{"id":986577,"date":"2024-12-27T07:46:09","date_gmt":"2024-12-26T23:46:09","guid":{"rendered":""},"modified":"2024-12-27T07:46:11","modified_gmt":"2024-12-26T23:46:11","slug":"python%e5%a6%82%e4%bd%95%e8%b0%83%e7%94%a8%e6%89%80%e6%9c%89%e6%a0%b8%e5%bf%83","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/986577.html","title":{"rendered":"python\u5982\u4f55\u8c03\u7528\u6240\u6709\u6838\u5fc3"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25063110\/b2a33019-2c54-4d0e-ab3e-807affa65ff4.webp\" alt=\"python\u5982\u4f55\u8c03\u7528\u6240\u6709\u6838\u5fc3\" \/><\/p>\n<p><p> <strong>Python\u8c03\u7528\u6240\u6709\u6838\u5fc3\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528\u591a\u7ebf\u7a0b\u3001\u591a\u8fdb\u7a0b\u548c\u5e76\u884c\u8ba1\u7b97\u5e93\u3002\u591a\u8fdb\u7a0b\u662f\u6700\u5e38\u7528\u7684\u65b9\u5f0f\uff0c\u56e0\u4e3aPython\u7684GIL\uff08\u5168\u5c40\u89e3\u91ca\u5668\u9501\uff09\u9650\u5236\u4e86\u591a\u7ebf\u7a0b\u7684\u6548\u7387\u3002\u901a\u8fc7\u4f7f\u7528\u591a\u8fdb\u7a0b\uff0cPython\u53ef\u4ee5\u5728\u591a\u4e2a\u6838\u5fc3\u4e0a\u8fd0\u884c\u72ec\u7acb\u7684\u8fdb\u7a0b\uff0c\u4ece\u800c\u63d0\u9ad8\u7a0b\u5e8f\u7684\u6267\u884c\u901f\u5ea6\u3002\u5e76\u884c\u8ba1\u7b97\u5e93\u5982NumPy\u548cDask\u4e5f\u53ef\u4ee5\u7528\u4e8e\u5206\u914d\u4efb\u52a1\u5230\u591a\u4e2a\u6838\u5fc3\u3002<\/strong>\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5176\u4e2d\u7684\u591a\u8fdb\u7a0b\u65b9\u6cd5\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001\u591a\u7ebf\u7a0b\u4e0e\u591a\u8fdb\u7a0b<\/p>\n<\/p>\n<p><p>\u5728\u5904\u7406\u5e76\u53d1\u4efb\u52a1\u65f6\uff0c\u591a\u7ebf\u7a0b\u548c\u591a\u8fdb\u7a0b\u662f\u4e24\u79cd\u5e38\u7528\u7684\u65b9\u6cd5\u3002\u591a\u7ebf\u7a0b\u9002\u5408I\/O\u5bc6\u96c6\u578b\u4efb\u52a1\uff0c\u800c\u591a\u8fdb\u7a0b\u5219\u66f4\u9002\u5408CPU\u5bc6\u96c6\u578b\u4efb\u52a1\u3002\u7531\u4e8ePython\u7684GIL\u9650\u5236\uff0c\u591a\u7ebf\u7a0b\u5728Python\u4e2d\u65e0\u6cd5\u5b9e\u73b0\u771f\u6b63\u7684\u5e76\u884c\uff0c\u56e0\u6b64\u5bf9\u4e8e\u9700\u8981\u5360\u7528\u591a\u4e2aCPU\u6838\u5fc3\u7684\u4efb\u52a1\uff0c\u4f7f\u7528\u591a\u8fdb\u7a0b\u662f\u66f4\u597d\u7684\u9009\u62e9\u3002<\/p>\n<\/p>\n<ol>\n<li>\u591a\u7ebf\u7a0b<\/li>\n<\/ol>\n<p><p>Python\u7684<code>threading<\/code>\u6a21\u5757\u53ef\u4ee5\u5b9e\u73b0\u591a\u7ebf\u7a0b\uff0c\u4f46\u7531\u4e8eGIL\u7684\u9650\u5236\uff0c\u7ebf\u7a0b\u4e4b\u95f4\u4e0d\u80fd\u771f\u6b63\u5e76\u884c\u8fd0\u884c\u3002\u8fd9\u610f\u5473\u7740\u5728CPU\u5bc6\u96c6\u578b\u4efb\u52a1\u4e2d\uff0c\u591a\u7ebf\u7a0b\u5e76\u4e0d\u80fd\u6709\u6548\u5730\u63d0\u9ad8\u6027\u80fd\u3002\u7136\u800c\uff0c\u5bf9\u4e8eI\/O\u5bc6\u96c6\u578b\u4efb\u52a1\uff0c\u591a\u7ebf\u7a0b\u4ecd\u7136\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u9009\u62e9\uff0c\u56e0\u4e3a\u5b83\u53ef\u4ee5\u5728\u7b49\u5f85I\/O\u64cd\u4f5c\u5b8c\u6210\u65f6\u6267\u884c\u5176\u4ed6\u4efb\u52a1\u3002<\/p>\n<\/p>\n<ol start=\"2\">\n<li>\u591a\u8fdb\u7a0b<\/li>\n<\/ol>\n<p><p>Python\u7684<code>multiprocessing<\/code>\u6a21\u5757\u5141\u8bb8\u521b\u5efa\u591a\u4e2a\u8fdb\u7a0b\uff0c\u6bcf\u4e2a\u8fdb\u7a0b\u90fd\u6709\u81ea\u5df1\u7684Python\u89e3\u91ca\u5668\u548c\u5185\u5b58\u7a7a\u95f4\uff0c\u8fd9\u6837\u5c31\u53ef\u4ee5\u7ed5\u8fc7GIL\u9650\u5236\uff0c\u5b9e\u73b0\u771f\u6b63\u7684\u5e76\u884c\u8ba1\u7b97\u3002\u901a\u8fc7\u4f7f\u7528\u591a\u8fdb\u7a0b\uff0c\u6211\u4eec\u53ef\u4ee5\u5728\u591a\u4e2aCPU\u6838\u5fc3\u4e0a\u540c\u65f6\u8fd0\u884c\u591a\u4e2a\u4efb\u52a1\uff0c\u4ece\u800c\u63d0\u9ad8\u7a0b\u5e8f\u7684\u6267\u884c\u6548\u7387\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import multiprocessing<\/p>\n<p>def worker(num):<\/p>\n<p>    print(f&#39;Worker: {num}&#39;)<\/p>\n<p>if __name__ == &#39;__m<a href=\"https:\/\/docs.pingcode.com\/blog\/59162.html\" target=\"_blank\">AI<\/a>n__&#39;:<\/p>\n<p>    jobs = []<\/p>\n<p>    for i in range(multiprocessing.cpu_count()):<\/p>\n<p>        p = multiprocessing.Process(target=worker, args=(i,))<\/p>\n<p>        jobs.append(p)<\/p>\n<p>        p.start()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4ee5\u4e0a\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528<code>multiprocessing<\/code>\u6a21\u5757\u521b\u5efa\u591a\u4e2a\u8fdb\u7a0b\uff0c\u5e76\u5728\u6bcf\u4e2a\u6838\u5fc3\u4e0a\u8fd0\u884c\u4e00\u4e2a\u7b80\u5355\u7684\u4efb\u52a1\u3002<\/p>\n<\/p>\n<p><p>\u4e8c\u3001\u5e76\u884c\u8ba1\u7b97\u5e93<\/p>\n<\/p>\n<p><p>Python\u4e2d\u6709\u8bb8\u591a\u5e93\u53ef\u4ee5\u5e2e\u52a9\u5b9e\u73b0\u5e76\u884c\u8ba1\u7b97\uff0c\u9664\u4e86<code>multiprocessing<\/code>\u5916\uff0c\u8fd8\u6709\u4e00\u4e9b\u4e13\u95e8\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u7684\u5e93\uff0c\u5982NumPy\u3001Dask\u7b49\u3002<\/p>\n<\/p>\n<ol>\n<li>NumPy<\/li>\n<\/ol>\n<p><p>NumPy\u662fPython\u4e2d\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u5b83\u652f\u6301\u591a\u79cd\u5e76\u884c\u5316\u9009\u9879\u3002\u867d\u7136NumPy\u672c\u8eab\u5e76\u4e0d\u76f4\u63a5\u63d0\u4f9b\u591a\u6838\u5fc3\u5e76\u884c\u5316\u7684\u529f\u80fd\uff0c\u4f46\u5b83\u80cc\u540e\u7684\u5e95\u5c42\u5e93\uff08\u5982BLAS\u548cLAPACK\uff09\u901a\u5e38\u4f1a\u81ea\u52a8\u4f7f\u7528\u591a\u4e2a\u6838\u5fc3\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u4f7f\u7528NumPy\u8fdb\u884c\u77e9\u9635\u4e58\u6cd5<\/strong><\/h2>\n<p>a = np.random.rand(1000, 1000)<\/p>\n<p>b = np.random.rand(1000, 1000)<\/p>\n<p>c = np.dot(a, b)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u6267\u884c\u77e9\u9635\u4e58\u6cd5\u65f6\uff0cNumPy\u4f1a\u5229\u7528\u5e95\u5c42\u5e93\u7684\u5e76\u884c\u5316\u80fd\u529b\uff0c\u81ea\u52a8\u4f7f\u7528\u591a\u4e2a\u6838\u5fc3\u6765\u52a0\u901f\u8ba1\u7b97\u3002<\/p>\n<\/p>\n<ol start=\"2\">\n<li>Dask<\/li>\n<\/ol>\n<p><p>Dask\u662f\u4e00\u4e2a\u7075\u6d3b\u7684\u5e76\u884c\u8ba1\u7b97\u5e93\uff0c\u4e13\u4e3a\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u548c\u8ba1\u7b97\u4efb\u52a1\u800c\u8bbe\u8ba1\u3002\u4e0eNumPy\u4e0d\u540c\uff0cDask\u63d0\u4f9b\u4e86\u66f4\u9ad8\u5c42\u6b21\u7684\u5e76\u884c\u5316\u63a5\u53e3\uff0c\u4f7f\u5f97\u7528\u6237\u53ef\u4ee5\u66f4\u65b9\u4fbf\u5730\u5229\u7528\u591a\u6838\u5fc3\u8fdb\u884c\u8ba1\u7b97\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import dask.array as da<\/p>\n<h2><strong>\u521b\u5efa\u4e00\u4e2a\u5927\u7684Dask\u6570\u7ec4<\/strong><\/h2>\n<p>x = da.random.random((10000, 10000), chunks=(1000, 1000))<\/p>\n<p>y = x + x.T<\/p>\n<p>z = y.mean(axis=0)<\/p>\n<h2><strong>\u6267\u884c\u8ba1\u7b97<\/strong><\/h2>\n<p>z.compute()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>Dask\u4f1a\u81ea\u52a8\u5c06\u8ba1\u7b97\u4efb\u52a1\u5206\u914d\u5230\u591a\u4e2a\u6838\u5fc3\uff0c\u5e76\u5728\u540e\u53f0\u8fdb\u884c\u8c03\u5ea6\uff0c\u4ece\u800c\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\u3002<\/p>\n<\/p>\n<p><p>\u4e09\u3001\u4f7f\u7528GPU\u8fdb\u884c\u5e76\u884c\u8ba1\u7b97<\/p>\n<\/p>\n<p><p>\u9664\u4e86\u4f7f\u7528CPU\u6838\u5fc3\u5916\uff0cPython\u8fd8\u53ef\u4ee5\u5229\u7528GPU\u8fdb\u884c\u5e76\u884c\u8ba1\u7b97\u3002GPU\u62e5\u6709\u5927\u91cf\u7684\u5904\u7406\u6838\u5fc3\uff0c\u80fd\u591f\u5728\u67d0\u4e9b\u4efb\u52a1\u4e0a\u663e\u8457\u63d0\u9ad8\u8ba1\u7b97\u901f\u5ea6\u3002\u5e38\u7528\u7684GPU\u8ba1\u7b97\u5e93\u5305\u62ecCuPy\u548cPyCUDA\u3002<\/p>\n<\/p>\n<ol>\n<li>CuPy<\/li>\n<\/ol>\n<p><p>CuPy\u662f\u4e00\u4e2a\u7528\u4e8eGPU\u52a0\u901f\u7684Python\u5e93\uff0c\u4e0eNumPy\u7684\u63a5\u53e3\u975e\u5e38\u76f8\u4f3c\u3002\u901a\u8fc7\u5c06\u6570\u636e\u548c\u8ba1\u7b97\u8f6c\u79fb\u5230GPU\u4e0a\uff0cCuPy\u53ef\u4ee5\u5728\u8bb8\u591a\u6570\u503c\u8ba1\u7b97\u4efb\u52a1\u4e0a\u5927\u5e45\u63d0\u9ad8\u6027\u80fd\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import cupy as cp<\/p>\n<h2><strong>\u5728GPU\u4e0a\u521b\u5efa\u6570\u7ec4\u5e76\u8fdb\u884c\u8ba1\u7b97<\/strong><\/h2>\n<p>a = cp.random.rand(1000, 1000)<\/p>\n<p>b = cp.random.rand(1000, 1000)<\/p>\n<p>c = cp.dot(a, b)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"2\">\n<li>PyCUDA<\/li>\n<\/ol>\n<p><p>PyCUDA\u662f\u4e00\u4e2a\u5e95\u5c42\u7684GPU\u8ba1\u7b97\u5e93\uff0c\u5141\u8bb8\u7528\u6237\u76f4\u63a5\u4f7f\u7528CUDA\u7f16\u5199\u548c\u6267\u884cGPU\u5185\u6838\u3002\u867d\u7136PyCUDA\u63d0\u4f9b\u4e86\u66f4\u5927\u7684\u7075\u6d3b\u6027\uff0c\u4f46\u4e5f\u9700\u8981\u7528\u6237\u5bf9CUDA\u7f16\u7a0b\u6709\u4e00\u5b9a\u7684\u4e86\u89e3\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pycuda.driver as cuda<\/p>\n<p>import pycuda.autoinit<\/p>\n<p>from pycuda.compiler import SourceModule<\/p>\n<p>import numpy as np<\/p>\n<p>mod = SourceModule(&quot;&quot;&quot;<\/p>\n<p>__global__ void multiply(float *a, float *b, float *c, int N)<\/p>\n<p>{<\/p>\n<p>  int idx = blockIdx.x * blockDim.x + threadIdx.x;<\/p>\n<p>  if (idx &lt; N)<\/p>\n<p>  {<\/p>\n<p>    c[idx] = a[idx] * b[idx];<\/p>\n<p>  }<\/p>\n<p>}<\/p>\n<p>&quot;&quot;&quot;)<\/p>\n<p>multiply = mod.get_function(&quot;multiply&quot;)<\/p>\n<p>N = 1000<\/p>\n<p>a = np.random.rand(N).astype(np.float32)<\/p>\n<p>b = np.random.rand(N).astype(np.float32)<\/p>\n<p>c = np.zeros_like(a)<\/p>\n<p>multiply(cuda.In(a), cuda.In(b), cuda.Out(c), np.int32(N), block=(256, 1, 1), grid=(int(N\/256), 1))<\/p>\n<p>print(c)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u56db\u3001\u5e76\u884c\u5316\u7684\u6ce8\u610f\u4e8b\u9879<\/p>\n<\/p>\n<p><p>\u5728\u4f7f\u7528\u591a\u6838\u5fc3\u8fdb\u884c\u5e76\u884c\u5316\u65f6\uff0c\u6211\u4eec\u9700\u8981\u6ce8\u610f\u4ee5\u4e0b\u51e0\u70b9\uff1a<\/p>\n<\/p>\n<ol>\n<li>\u6570\u636e\u4f20\u8f93\u5f00\u9500<\/li>\n<\/ol>\n<p><p>\u5728\u591a\u8fdb\u7a0b\u548cGPU\u8ba1\u7b97\u4e2d\uff0c\u6570\u636e\u7684\u4f20\u8f93\u5f00\u9500\u53ef\u80fd\u4f1a\u5f71\u54cd\u6027\u80fd\u3002\u4e3a\u4e86\u6700\u5927\u5316\u5e76\u884c\u5316\u7684\u6548\u7387\uff0c\u6211\u4eec\u5e94\u8be5\u5c3d\u91cf\u51cf\u5c11\u6570\u636e\u5728\u4e0d\u540c\u8fdb\u7a0b\u6216\u8bbe\u5907\u4e4b\u95f4\u7684\u4f20\u8f93\u3002<\/p>\n<\/p>\n<ol start=\"2\">\n<li>\u4efb\u52a1\u7684\u7c92\u5ea6<\/li>\n<\/ol>\n<p><p>\u5e76\u884c\u5316\u7684\u4efb\u52a1\u5e94\u8be5\u8db3\u591f\u5927\uff0c\u4ee5\u62b5\u6d88\u4efb\u52a1\u521b\u5efa\u548c\u8c03\u5ea6\u7684\u5f00\u9500\u3002\u5982\u679c\u4efb\u52a1\u8fc7\u5c0f\uff0c\u8c03\u5ea6\u5f00\u9500\u53ef\u80fd\u4f1a\u5927\u4e8e\u5e76\u884c\u5316\u5e26\u6765\u7684\u6027\u80fd\u63d0\u5347\u3002<\/p>\n<\/p>\n<ol start=\"3\">\n<li>\u7ebf\u7a0b\u5b89\u5168<\/li>\n<\/ol>\n<p><p>\u5728\u591a\u7ebf\u7a0b\u7f16\u7a0b\u4e2d\uff0c\u6211\u4eec\u9700\u8981\u6ce8\u610f\u7ebf\u7a0b\u5b89\u5168\u95ee\u9898\u3002\u5171\u4eab\u8d44\u6e90\u7684\u8bbf\u95ee\u5e94\u8be5\u901a\u8fc7\u9501\u7b49\u673a\u5236\u8fdb\u884c\u4fdd\u62a4\uff0c\u4ee5\u907f\u514d\u6570\u636e\u7ade\u4e89\u548c\u4e0d\u4e00\u81f4\u6027\u3002<\/p>\n<\/p>\n<p><p>\u901a\u8fc7\u5408\u7406\u5730\u4f7f\u7528Python\u7684\u591a\u8fdb\u7a0b\u3001\u591a\u7ebf\u7a0b\u548c\u5e76\u884c\u8ba1\u7b97\u5e93\uff0c\u6211\u4eec\u53ef\u4ee5\u5145\u5206\u5229\u7528\u8ba1\u7b97\u673a\u7684\u591a\u6838\u5fc3\uff0c\u63d0\u9ad8\u7a0b\u5e8f\u7684\u6267\u884c\u6548\u7387\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6839\u636e\u4efb\u52a1\u7684\u7279\u70b9\u9009\u62e9\u5408\u9002\u7684\u5e76\u884c\u5316\u7b56\u7565\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u63d0\u5347\u8ba1\u7b97\u6027\u80fd\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> 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