怎么用gpu跑python程序（用GPU来运行Python代码）

时间2025-05-04 19:33:20分类IT科技浏览5590

导读：简介前几天捣鼓了一下Ubuntu，正是想用一下我旧电脑上的N卡，可以用GPU来跑代码，体验一下多核的快乐。...

简介

前几天捣鼓了一下Ubuntu ，正是想用一下我旧电脑上的N卡，可以用GPU来跑代码，体验一下多核的快乐。

还好我这破电脑也是支持Cuda的：

$ sudo lshw -C display *-display description: 3D controller product: GK208M [GeForce GT 740M] vendor: NVIDIA Corporation physical id: 0 bus info: pci@0000:01:00.0 version: a1 width: 64 bits clock: 33MHz capabilities: pm msi pciexpress bus_master cap_list rom configuration: driver=nouveau latency=0 resources: irq:35 memory:f0000000-f0ffffff memory:c0000000-cfffffff memory:d0000000-d1ffffff ioport:6000(size=128)

安装相关工具

首先安装一下Cuda的开发工具，命令如下：

$ sudo apt install nvidia-cuda-toolkit

查看一下相关信息：

$ nvcc --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2021 NVIDIA Corporation Built on Thu_Nov_18_09:45:30_PST_2021 Cuda compilation tools, release 11.5, V11.5.119 Build cuda_11.5.r11.5/compiler.30672275_0

通过Conda安装相关的依赖包：

conda install numba & conda install cudatoolkit

通过pip安装也可以，一样的。

测试与驱动安装

简单测试了一下，发觉报错了：

$ /home/larry/anaconda3/bin/python /home/larry/code/pkslow-samples/python/src/main/python/cuda/test1.py Traceback (most recent call last): File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/cudadrv/driver.py", line 246, in ensure_initialized self.cuInit(0) File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/cudadrv/driver.py", line 319, in safe_cuda_api_call self._check_ctypes_error(fname, retcode) File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/cudadrv/driver.py", line 387, in _check_ctypes_error raise CudaAPIError(retcode, msg) numba.cuda.cudadrv.driver.CudaAPIError: [100] Call to cuInit results in CUDA_ERROR_NO_DEVICE During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/larry/code/pkslow-samples/python/src/main/python/cuda/test1.py", line 15, in <module> gpu_print[1, 2]() File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/compiler.py", line 862, in __getitem__ return self.configure(*args) File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/compiler.py", line 857, in configure return _KernelConfiguration(self, griddim, blockdim, stream, sharedmem) File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/compiler.py", line 718, in __init__ ctx = get_context() File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/cudadrv/devices.py", line 220, in get_context return _runtime.get_or_create_context(devnum) File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/cudadrv/devices.py", line 138, in get_or_create_context return self._get_or_create_context_uncached(devnum) File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/cudadrv/devices.py", line 153, in _get_or_create_context_uncached with driver.get_active_context() as ac: File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/cudadrv/driver.py", line 487, in __enter__ driver.cuCtxGetCurrent(byref(hctx)) File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/cudadrv/driver.py", line 284, in __getattr__ self.ensure_initialized() File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/cudadrv/driver.py", line 250, in ensure_initialized raise CudaSupportError(f"Error at driver init: {description}") numba.cuda.cudadrv.error.CudaSupportError: Error at driver init: Call to cuInit results in CUDA_ERROR_NO_DEVICE (100)

网上搜了一下，发现是驱动问题。通过Ubuntu自带的工具安装显卡驱动：

还是失败：

$ nvidia-smi NVIDIA-SMI has failed because it couldnt communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.

最后，通过命令行安装驱动，成功解决这个问题：

$ sudo apt install nvidia-driver-470

检查后发现正常了：

测试代码也可以跑了。

测试Python代码

打印ID

准备以下代码：

from numba import cuda import os def cpu_print(): print(cpu print) @cuda.jit def gpu_print(): dataIndex = cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x print(gpu print , cuda.threadIdx.x, cuda.blockIdx.x, cuda.blockDim.x, dataIndex) if __name__ == __main__: gpu_print[4, 4]() cuda.synchronize() cpu_print()

这个代码主要有两个函数，一个是用CPU执行，一个是用GPU执行，执行打印操作。关键在于@cuda.jit这个注解，让代码在GPU上执行。运行结果如下：

$ /home/larry/anaconda3/bin/python /home/larry/code/pkslow-samples/python/src/main/python/cuda/print_test.py gpu print 0 3 4 12 gpu print 1 3 4 13 gpu print 2 3 4 14 gpu print 3 3 4 15 gpu print 0 2 4 8 gpu print 1 2 4 9 gpu print 2 2 4 10 gpu print 3 2 4 11 gpu print 0 1 4 4 gpu print 1 1 4 5 gpu print 2 1 4 6 gpu print 3 1 4 7 gpu print 0 0 4 0 gpu print 1 0 4 1 gpu print 2 0 4 2 gpu print 3 0 4 3 cpu print

可以看到GPU总共打印了16次，使用了不同的Thread来执行。这次每次打印的结果都可能不同，因为提交GPU是异步执行的，无法确保哪个单元先执行。同时也需要调用同步函数cuda.synchronize() ，确保GPU执行完再继续往下跑。

查看时间

我们通过这个函数来看GPU并行的力量：

from numba import jit, cuda import numpy as np # to measure exec time from timeit import default_timer as timer # normal function to run on cpu def func(a): for i in range(10000000): a[i] += 1 # function optimized to run on gpu @jit(target_backend=cuda) def func2(a): for i in range(10000000): a[i] += 1 if __name__ == "__main__": n = 10000000 a = np.ones(n, dtype=np.float64) start = timer() func(a) print("without GPU:", timer() - start) start = timer() func2(a) print("with GPU:", timer() - start)

结果如下：

$ /home/larry/anaconda3/bin/python /home/larry/code/pkslow-samples/python/src/main/python/cuda/time_test.py without GPU: 3.7136273959999926 with GPU: 0.4040513340000871

可以看到使用CPU需要3.7秒，而GPU则只要0.4秒，还是能快不少的。当然这里不是说GPU一定比CPU快，具体要看任务的类型。

代码

代码请看GitHub: https://github.com/LarryDpk/pkslow-samples