{"id":1010274,"date":"2024-12-27T11:19:30","date_gmt":"2024-12-27T03:19:30","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1010274.html"},"modified":"2024-12-27T11:19:32","modified_gmt":"2024-12-27T03:19:32","slug":"python%e5%a6%82%e4%bd%95%e5%88%a9%e7%94%a8%e5%a4%9a%e6%a0%b8cpu","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1010274.html","title":{"rendered":"python\u5982\u4f55\u5229\u7528\u591a\u6838cpu"},"content":{"rendered":"

\"python\u5982\u4f55\u5229\u7528\u591a\u6838cpu\"<\/p>\n

Python\u5229\u7528\u591a\u6838CPU\u7684\u65b9\u5f0f\u5305\u62ec\u591a\u7ebf\u7a0b\u3001multiprocessing\u6a21\u5757\u3001joblib\u5e93\u3001\u5e76\u884c\u8ba1\u7b97\u5e93\u3001Cython\u4f18\u5316\u6280\u672f<\/strong>\u3002\u5176\u4e2d\uff0c\u5229\u7528multiprocessing\u6a21\u5757\u662f\u8f83\u4e3a\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u56e0\u4e3aPython\u7684\u5168\u5c40\u89e3\u91ca\u5668\u9501\uff08GIL\uff09\u9650\u5236\u4e86\u591a\u7ebf\u7a0b\u7684\u6027\u80fd\u3002\u901a\u8fc7multiprocessing\u6a21\u5757\uff0c\u53ef\u4ee5\u521b\u5efa\u591a\u4e2a\u8fdb\u7a0b\uff0c\u6bcf\u4e2a\u8fdb\u7a0b\u8fd0\u884c\u5728\u72ec\u7acb\u7684Python\u89e3\u91ca\u5668\u4e2d\uff0c\u4ece\u800c\u5b9e\u73b0\u771f\u6b63\u7684\u5e76\u884c\u8ba1\u7b97\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5982\u4f55\u5229\u7528\u8fd9\u4e9b\u65b9\u6cd5\u6765\u5145\u5206\u53d1\u6325\u591a\u6838CPU\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u591a\u7ebf\u7a0b\u4e0e\u591a\u8fdb\u7a0b<\/p>\n<\/p>\n

Python\u4e2d\u6709\u4e24\u79cd\u4e3b\u8981\u7684\u5e76\u884c\u7f16\u7a0b\u65b9\u5f0f\uff1a\u591a\u7ebf\u7a0b\u548c\u591a\u8fdb\u7a0b\u3002\u4e24\u8005\u4e4b\u95f4\u7684\u533a\u522b\u5728\u4e8e\uff0c\u7ebf\u7a0b\u662f\u8f7b\u91cf\u7ea7\u7684\uff0c\u591a\u4e2a\u7ebf\u7a0b\u5171\u4eab\u540c\u4e00\u4e2a\u8fdb\u7a0b\u7684\u5185\u5b58\u7a7a\u95f4\uff0c\u800c\u8fdb\u7a0b\u5219\u662f\u72ec\u7acb\u7684\uff0c\u62e5\u6709\u81ea\u5df1\u7684\u5185\u5b58\u7a7a\u95f4\u3002<\/p>\n<\/p>\n

1.1\u3001\u591a\u7ebf\u7a0b<\/p>\n<\/p>\n

\u591a\u7ebf\u7a0b\u662f\u6307\u5728\u540c\u4e00\u4e2a\u7a0b\u5e8f\u4e2d\u540c\u65f6\u8fd0\u884c\u591a\u4e2a\u7ebf\u7a0b\u3002Python\u7684\u7ebf\u7a0b\u6a21\u5757\uff08threading\uff09\u63d0\u4f9b\u4e86\u4e00\u79cd\u7b80\u4fbf\u7684\u65b9\u6cd5\u6765\u5b9e\u73b0\u591a\u7ebf\u7a0b\u3002\u7136\u800c\uff0c\u7531\u4e8eGIL\u7684\u5b58\u5728\uff0c\u591a\u7ebf\u7a0b\u5728Python\u4e2d\u5e76\u4e0d\u80fd\u5145\u5206\u5229\u7528\u591a\u6838CPU\u7684\u4f18\u52bf\u3002GIL\u9650\u5236\u4e86\u540c\u4e00\u65f6\u95f4\u53ea\u6709\u4e00\u4e2a\u7ebf\u7a0b\u5728\u6267\u884cPython\u5b57\u8282\u7801\uff0c\u56e0\u6b64\u5728CPU\u5bc6\u96c6\u578b\u4efb\u52a1\u4e2d\uff0c\u591a\u7ebf\u7a0b\u7684\u6027\u80fd\u63d0\u5347\u6709\u9650\u3002<\/p>\n<\/p>\n

1.2\u3001\u591a\u8fdb\u7a0b<\/p>\n<\/p>\n

\u76f8\u6bd4\u591a\u7ebf\u7a0b\uff0c\u591a\u8fdb\u7a0b\u662f\u4e00\u79cd\u66f4\u597d\u7684\u5e76\u884c\u7f16\u7a0b\u65b9\u5f0f\u3002Python\u7684multiprocessing\u6a21\u5757\u5141\u8bb8\u521b\u5efa\u591a\u4e2a\u8fdb\u7a0b\uff0c\u6bcf\u4e2a\u8fdb\u7a0b\u8fd0\u884c\u5728\u72ec\u7acb\u7684Python\u89e3\u91ca\u5668\u4e2d\uff0c\u4ece\u800c\u7ed5\u8fc7\u4e86GIL\u7684\u9650\u5236\u3002\u8fd9\u4f7f\u5f97\u591a\u8fdb\u7a0b\u66f4\u9002\u5408CPU\u5bc6\u96c6\u578b\u4efb\u52a1\u3002<\/p>\n<\/p>\n

\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u591a\u8fdb\u7a0b\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

from multiprocessing import Process<\/p>\n
def worker(num):<\/p>\n
    """\u7ebf\u7a0b\u7684\u5de5\u4f5c\u51fd\u6570"""<\/p>\n
    print(f'Worker: {num}')<\/p>\n
if __name__ == '__mAI<\/a>n__':<\/p>\n
    processes = []<\/p>\n
    for i in range(4):<\/p>\n
        p = Process(target=worker, args=(i,))<\/p>\n
        processes.append(p)<\/p>\n
        p.start()<\/p>\n
    for p in processes:<\/p>\n
        p.join()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001multiprocessing\u6a21\u5757<\/p>\n<\/p>\n
multiprocessing\u6a21\u5757\u662fPython\u5185\u7f6e\u7684\u5e76\u884c\u8ba1\u7b97\u5e93\uff0c\u53ef\u4ee5\u5229\u7528\u591a\u6838CPU\u8fdb\u884c\u5e76\u884c\u8ba1\u7b97\u3002\u5b83\u63d0\u4f9b\u4e86\u591a\u79cd\u529f\u80fd\uff0c\u5305\u62ec\u8fdb\u7a0b\u6c60\uff08Pool\uff09\u3001\u961f\u5217\uff08Queue\uff09\u3001\u7ba1\u9053\uff08Pipe\uff09\u7b49\u3002<\/p>\n<\/p>\n
2.1\u3001\u8fdb\u7a0b\u6c60<\/p>\n<\/p>\n
\u8fdb\u7a0b\u6c60\u662fmultiprocessing\u6a21\u5757\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u6982\u5ff5\uff0c\u5b83\u5141\u8bb8\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u8fdb\u7a0b\u6c60\uff0c\u63a7\u5236\u5e76\u53d1\u8fdb\u7a0b\u7684\u6570\u91cf\u3002\u901a\u8fc7\u8fdb\u7a0b\u6c60\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u4efb\u52a1\u5206\u914d\u5230\u591a\u4e2a\u8fdb\u7a0b\u4e2d\u6267\u884c\u3002<\/p>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528\u8fdb\u7a0b\u6c60\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
from multiprocessing import Pool<\/p>\n
def square(x):<\/p>\n
    return x * x<\/p>\n
if __name__ == '__main__':<\/p>\n
    with Pool(4) as p:<\/p>\n
        results = p.map(square, [1, 2, 3, 4, 5])<\/p>\n
    print(results)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a\u5305\u542b4\u4e2a\u8fdb\u7a0b\u7684\u8fdb\u7a0b\u6c60\uff0c\u5e76\u4f7f\u7528map<\/code>\u51fd\u6570\u5c06square<\/code>\u51fd\u6570\u5e94\u7528\u4e8e\u7ed9\u5b9a\u5217\u8868\u4e2d\u7684\u6bcf\u4e2a\u5143\u7d20\u3002<\/p>\n<\/p>\n
2.2\u3001\u961f\u5217\u4e0e\u7ba1\u9053<\/p>\n<\/p>\n
multiprocessing\u6a21\u5757\u8fd8\u63d0\u4f9b\u4e86\u961f\u5217\uff08Queue\uff09\u548c\u7ba1\u9053\uff08Pipe\uff09\u7528\u4e8e\u8fdb\u7a0b\u95f4\u901a\u4fe1\u3002\u961f\u5217\u662f\u7ebf\u7a0b\u548c\u8fdb\u7a0b\u5b89\u5168\u7684FIFO\u6570\u636e\u7ed3\u6784\uff0c\u800c\u7ba1\u9053\u5219\u63d0\u4f9b\u4e86\u53cc\u5411\u901a\u4fe1\u673a\u5236\u3002<\/p>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528\u961f\u5217\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
from multiprocessing import Process, Queue<\/p>\n
def worker(queue, num):<\/p>\n
    queue.put(f'Worker: {num}')<\/p>\n
if __name__ == '__main__':<\/p>\n
    queue = Queue()<\/p>\n
    processes = []<\/p>\n
    for i in range(4):<\/p>\n
        p = Process(target=worker, args=(queue, i))<\/p>\n
        processes.append(p)<\/p>\n
        p.start()<\/p>\n
    for p in processes:<\/p>\n
        p.join()<\/p>\n
    while not queue.empty():<\/p>\n
        print(queue.get())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001joblib\u5e93<\/p>\n<\/p>\n
joblib\u662f\u4e00\u4e2a\u4e13\u6ce8\u4e8e\u5728Python\u4e2d\u8fdb\u884c\u5e76\u884c\u8ba1\u7b97\u7684\u7b2c\u4e09\u65b9\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e00\u79cd\u7b80\u5355\u7684\u65b9\u5f0f\u6765\u5b9e\u73b0\u4efb\u52a1\u7684\u5e76\u884c\u5316\u3002joblib\u7684\u6838\u5fc3\u529f\u80fd\u662fParallel<\/code>\u548cdelayed<\/code>\uff0c\u5b83\u4eec\u7528\u4e8e\u5b9a\u4e49\u548c\u6267\u884c\u5e76\u884c\u4efb\u52a1\u3002<\/p>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528joblib\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
from joblib import Parallel, delayed<\/p>\n
import math<\/p>\n
def compute_square_root(x):<\/p>\n
    return math.sqrt(x)<\/p>\n
results = Parallel(n_jobs=4)(delayed(compute_square_root)(i) for i in range(10))<\/p>\n
print(results)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528Parallel<\/code>\u548cdelayed<\/code>\u6765\u5e76\u884c\u8ba1\u7b97\u5e73\u65b9\u6839\u3002n_jobs<\/code>\u53c2\u6570\u6307\u5b9a\u4e86\u5e76\u884c\u6267\u884c\u7684\u4efb\u52a1\u6570\u91cf\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5e76\u884c\u8ba1\u7b97\u5e93<\/p>\n<\/p>\n
\u9664\u4e86multiprocessing\u548cjoblib\uff0cPython\u4e2d\u8fd8\u6709\u5176\u4ed6\u4e00\u4e9b\u5e76\u884c\u8ba1\u7b97\u5e93\uff0c\u5982Dask\u3001Ray\u548cPySpark\u7b49\u3002\u5b83\u4eec\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u548c\u7075\u6d3b\u7684\u5e76\u884c\u8ba1\u7b97\u529f\u80fd\uff0c\u9002\u7528\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\u548c\u5206\u5e03\u5f0f\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
4.1\u3001Dask<\/p>\n<\/p>\n
Dask\u662f\u4e00\u4e2a\u7075\u6d3b\u7684\u5e76\u884c\u8ba1\u7b97\u5e93\uff0c\u652f\u6301\u591a\u6838CPU\u548c\u5206\u5e03\u5f0f\u8ba1\u7b97\u3002\u5b83\u80fd\u591f\u5904\u7406\u6bd4\u5185\u5b58\u5927\u7684\u6570\u636e\u96c6\uff0c\u5e76\u63d0\u4f9b\u4e86\u7c7b\u4f3c\u4e8eNumPy\u548cPandas\u7684\u63a5\u53e3\u3002<\/p>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528Dask\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import dask.array as da<\/p>\n
x = da.random.random((10000, 10000), chunks=(1000, 1000))<\/p>\n
y = x.mean().compute()<\/p>\n
print(y)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.2\u3001Ray<\/p>\n<\/p>\n
Ray\u662f\u4e00\u4e2a\u7528\u4e8e\u6784\u5efa\u548c\u8fd0\u884c\u5206\u5e03\u5f0f\u5e94\u7528\u7a0b\u5e8f\u7684\u6846\u67b6\u3002\u5b83\u63d0\u4f9b\u4e86\u7b80\u5355\u7684API\u6765\u5b9e\u73b0\u5e76\u884c\u8ba1\u7b97\uff0c\u5e76\u652f\u6301\u5927\u89c4\u6a21\u96c6\u7fa4\u3002<\/p>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528Ray\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import ray<\/p>\n
ray.init()<\/p>\n
@ray.remote<\/p>\n
def compute_square(x):<\/p>\n
    return x * x<\/p>\n
futures = [compute_square.remote(i) for i in range(10)]<\/p>\n
results = ray.get(futures)<\/p>\n
print(results)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001Cython\u4f18\u5316\u6280\u672f<\/p>\n<\/p>\n
Cython\u662f\u4e00\u79cd\u5c06Python\u4ee3\u7801\u7f16\u8bd1\u4e3aC\u6269\u5c55\u6a21\u5757\u7684\u5de5\u5177\u3002\u901a\u8fc7Cython\uff0c\u53ef\u4ee5\u5c06\u8ba1\u7b97\u5bc6\u96c6\u578b\u7684Python\u4ee3\u7801\u8f6c\u6362\u4e3a\u9ad8\u6548\u7684C\u4ee3\u7801\uff0c\u4ece\u800c\u63d0\u5347\u6027\u80fd\u3002\u867d\u7136Cython\u672c\u8eab\u5e76\u4e0d\u662f\u5e76\u884c\u8ba1\u7b97\u5de5\u5177\uff0c\u4f46\u7ed3\u5408\u591a\u7ebf\u7a0b\u6216\u591a\u8fdb\u7a0b\u53ef\u4ee5\u5b9e\u73b0\u66f4\u9ad8\u6548\u7684\u5e76\u884c\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684Cython\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
def compute_square(double x):<\/p>\n
    return x * x<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8981\u4f7f\u7528Cython\u7f16\u8bd1\u8fd9\u4e2a\u51fd\u6570\uff0c\u9700\u8981\u5728setup.py\u4e2d\u6307\u5b9a\uff1a<\/p>\n<\/p>\n
from setuptools import setup<\/p>\n
from Cython.Build import cythonize<\/p>\n
setup(<\/p>\n
    ext_modules=cythonize("example.pyx"),<\/p>\n
)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\u8fd0\u884cpython setup.py build_ext --inplace<\/code>\u8fdb\u884c\u7f16\u8bd1\u3002<\/p>\n<\/p>\n
\u7efc\u4e0a\u6240\u8ff0\uff0cPython\u4e2d\u6709\u591a\u79cd\u65b9\u6cd5\u53ef\u4ee5\u5229\u7528\u591a\u6838CPU\u8fdb\u884c\u5e76\u884c\u8ba1\u7b97\uff0c\u5305\u62ec\u591a\u7ebf\u7a0b\u3001\u591a\u8fdb\u7a0b\u3001multiprocessing\u6a21\u5757\u3001joblib\u5e93\u3001\u5e76\u884c\u8ba1\u7b97\u5e93\u4ee5\u53caCython\u4f18\u5316\u6280\u672f\u3002\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53d6\u51b3\u4e8e\u5177\u4f53\u7684\u5e94\u7528\u573a\u666f\u548c\u4efb\u52a1\u9700\u6c42\u3002\u5728\u8fdb\u884c\u5e76\u884c\u8ba1\u7b97\u65f6\uff0c\u9700\u8981\u6ce8\u610f\u7ebf\u7a0b\u5b89\u5168\u3001\u8fdb\u7a0b\u95f4\u901a\u4fe1\u548c\u8d44\u6e90\u7ba1\u7406<\/a>\u7b49\u95ee\u9898\uff0c\u4ee5\u786e\u4fdd\u7a0b\u5e8f\u7684\u6b63\u786e\u6027\u548c\u6548\u7387\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u5b9e\u73b0\u591a\u6838CPU\u7684\u5e76\u884c\u5904\u7406\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u5229\u7528\u591a\u6838CPU\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5b9e\u73b0\u5e76\u884c\u5904\u7406\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528multiprocessing<\/code>\u6a21\u5757\u3001concurrent.futures<\/code>\u6a21\u5757\u4ee5\u53ca\u7b2c\u4e09\u65b9\u5e93\u5982joblib<\/code>\u548cdask<\/code>\u3002multiprocessing<\/code>\u6a21\u5757\u53ef\u4ee5\u521b\u5efa\u591a\u4e2a\u8fdb\u7a0b\uff0c\u6bcf\u4e2a\u8fdb\u7a0b\u8fd0\u884c\u5728\u72ec\u7acb\u7684\u5185\u5b58\u7a7a\u95f4\u4e2d\uff0c\u4ece\u800c\u5145\u5206\u5229\u7528\u591a\u6838CPU\u7684\u4f18\u52bf\u3002concurrent.futures<\/code>\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\uff0c\u7b80\u5316\u4e86\u591a\u7ebf\u7a0b\u548c\u591a\u8fdb\u7a0b\u7684\u4f7f\u7528\u3002<\/p>\n
\u4f7f\u7528\u591a\u6838CPU\u8fdb\u884c\u6570\u636e\u5904\u7406\u65f6\u6709\u54ea\u4e9b\u6ce8\u610f\u4e8b\u9879\uff1f<\/strong>
\u5728\u4f7f\u7528\u591a\u6838CPU\u8fdb\u884c\u6570\u636e\u5904\u7406\u65f6\uff0c\u9700\u8981\u8003\u8651\u4efb\u52a1\u7684\u5206\u914d\u65b9\u5f0f\u548c\u6570\u636e\u5171\u4eab\u95ee\u9898\u3002\u6bcf\u4e2a\u8fdb\u7a0b\u62e5\u6709\u81ea\u5df1\u7684\u5185\u5b58\u7a7a\u95f4\uff0c\u6570\u636e\u9700\u8981\u901a\u8fc7\u961f\u5217\u6216\u7ba1\u9053\u8fdb\u884c\u4f20\u9012\u3002\u6b64\u5916\uff0c\u4efb\u52a1\u7684\u7c92\u5ea6\u4e5f\u5f88\u91cd\u8981\uff0c\u8fc7\u5c0f\u7684\u4efb\u52a1\u53ef\u80fd\u5bfc\u81f4\u8fdb\u7a0b\u95f4\u7684\u8c03\u5ea6\u5f00\u9500\u589e\u52a0\uff0c\u4ece\u800c\u5f71\u54cd\u6027\u80fd\u3002\u56e0\u6b64\uff0c\u5408\u9002\u7684\u4efb\u52a1\u5927\u5c0f\u548c\u6709\u6548\u7684\u6570\u636e\u4f20\u8f93\u65b9\u5f0f\u662f\u63d0\u5347\u6027\u80fd\u7684\u5173\u952e\u3002<\/p>\n
\u5982\u4f55\u4f18\u5316Python\u4ee3\u7801\u4ee5\u66f4\u597d\u5730\u5229\u7528\u591a\u6838CPU\uff1f<\/strong>
\u4f18\u5316Python\u4ee3\u7801\u4ee5\u5145\u5206\u5229\u7528\u591a\u6838CPU\uff0c\u53ef\u4ee5\u4ece\u591a\u4e2a\u65b9\u9762\u5165\u624b\u3002\u9996\u5148\uff0c\u786e\u4fdd\u4f7f\u7528\u7684\u662f\u652f\u6301\u591a\u7ebf\u7a0b\u6216\u591a\u8fdb\u7a0b\u7684\u5e93\uff0c\u4f8b\u5982numpy<\/code>\u548cpandas<\/code>\u7b49\uff0c\u5b83\u4eec\u5185\u90e8\u5b9e\u73b0\u4e86\u5e76\u884c\u8ba1\u7b97\u3002\u5176\u6b21\uff0c\u51cf\u5c11\u5168\u5c40\u89e3\u91ca\u5668\u9501\uff08GIL\uff09\u7684\u5f71\u54cd\uff0c\u53ef\u4ee5\u9009\u62e9\u4f7f\u7528multiprocessing<\/code>\u6a21\u5757\u800c\u975e\u591a\u7ebf\u7a0b\u3002\u6b64\u5916\uff0c\u8003\u8651\u4f7f\u7528\u5f02\u6b65\u7f16\u7a0b\uff0c\u5c24\u5176\u662f\u5728\u5904\u7406IO\u5bc6\u96c6\u578b\u4efb\u52a1\u65f6\uff0c\u80fd\u591f\u6709\u6548\u63d0\u9ad8\u7a0b\u5e8f\u7684\u6267\u884c\u6548\u7387\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5229\u7528\u591a\u6838CPU\u7684\u65b9\u5f0f\u5305\u62ec\u591a\u7ebf\u7a0b\u3001multiprocessing\u6a21\u5757\u3001joblib\u5e93\u3001\u5e76\u884c\u8ba1\u7b97\u5e93\u3001C […]","protected":false},"author":3,"featured_media":1010284,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1010274"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1010274"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1010274\/revisions"}],"predecessor-version":[{"id":1010286,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1010274\/revisions\/1010286"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1010284"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1010274"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1010274"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1010274"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}