jetson nano安装pycuda

Posted 2022-12-13 帅的发光发亮

jetson nano安装pycuda

• JetPack4.4版本
• • 配置cuda的环境
  • 安装pycuda-2019
• 更新JetPack4.6版本
• • 安装pycuda-2021
  • 解决方案
  • 测试dome

JetPack4.4版本

配置cuda的环境

$ sudo nano ~/.bashrc
export PATH=/usr/local/cuda-10.2/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export CUDA_HOME=$CUDA_HOME:/usr/local/cuda-10.2
$ sudo source ~/.bashrc
$ nvcc -V 检测一下是否配置成功

安装pycuda-2019

之后下载[pycuda-2019.1.2]

下载完之后解压
进入解压出来的文件

tar zxvf pycuda-2019.1.2.tar.gz    
cd pycuda-2019.1.2/  
python3 configure.py --cuda-root=/usr/local/cuda-10.2
sudo python3 setup.py install

出现这个就说明正在编译文件安装，等待一段时间后即可安装完成。

安装完出现：

就表明安装成功了。

但是使用的时候还得配置一下一些必要的东西不然会报错：😗*

FileNotFoundError: [Errno 2] No such file or directory: ‘nvcc’

将nvcc的完整路径硬编码到Pycuda的compiler.py文件中的compile_plain()
中，大约在第 73 行的位置中加入下面段代码！

nvcc = '/usr/local/cuda/bin/'+nvcc

更新JetPack4.6版本

4.6版本也是cuda10.2版本的，cuda配置环境都一样

安装pycuda-2021

源码也可下载【pycuda-2021】的
这是pycuda的github地址：https://github.com/inducer/pycuda

测试pycuda是否安装正确的时候会报错

Traceback (most recent call last):
  File "test.py", line 2, in <module>
    import pycuda.autoinit
  File "/usr/local/lib/python3.6/dist-packages/pycuda-2021.1-py3.6-linux-aarch64.egg/pycuda/autoinit.py", line 7, in <module>
    from pycuda.tools import make_default_context  # noqa: E402
  File "/usr/local/lib/python3.6/dist-packages/pycuda-2021.1-py3.6-linux-aarch64.egg/pycuda/tools.py", line 33, in <module>
    from pycuda.compyte.dtypes import (  # noqa: F401
ModuleNotFoundError: No module named 'pycuda.compyte'

解决方案

官方解决方案【链接】
不想去看的话，直接下载这个链接的源码，同下步骤进行安装即可
https://pypi.org/project/pycuda/#files

tar zxvf pycuda-2021.1.tar.gz    
cd pycuda-2021.1/  
python3 configure.py --cuda-root=/usr/local/cuda-10.2
sudo python3 setup.py install

测试dome

接下来写个矩阵运算的小demo来测试是否能真正运行：

import numpy as np
import pycuda.autoinit
import pycuda.driver as cuda
from pycuda.compiler import SourceModule


mod = SourceModule("""
#define BLOCK_SIZE 16

typedef struct 
    int width;
    int height;
    int stride; 
    int __padding;    //为了和64位的elements指针对齐
    float* elements;
 Matrix;

// 读取矩阵元素
__device__ float GetElement(const Matrix A, int row, int col)

    return A.elements[row * A.stride + col];


// 赋值矩阵元素
__device__ void SetElement(Matrix A, int row, int col, float value)

    A.elements[row * A.stride + col] = value;


// 获取 16x16 的子矩阵
 __device__ Matrix GetSubMatrix(Matrix A, int row, int col) 

    Matrix Asub;
    Asub.width    = BLOCK_SIZE;
    Asub.height   = BLOCK_SIZE;
    Asub.stride   = A.stride;
    Asub.elements = &A.elements[A.stride * BLOCK_SIZE * row + BLOCK_SIZE * col];
    return Asub;


__global__ void matrix_mul(Matrix *A, Matrix *B, Matrix *C)

    int blockRow = blockIdx.y;
    int blockCol = blockIdx.x;
    int row = threadIdx.y;
    int col = threadIdx.x;

    Matrix Csub = GetSubMatrix(*C, blockRow, blockCol);

    // 每个线程通过累加Cvalue计算Csub的一个值
    float Cvalue = 0;

    // 为了计算Csub遍历所有需要的Asub和Bsub
    for (int m = 0; m < (A->width / BLOCK_SIZE); ++m) 
    
        Matrix Asub = GetSubMatrix(*A, blockRow, m);
        Matrix Bsub = GetSubMatrix(*B, m, blockCol);
 
        __shared__ float As[BLOCK_SIZE][BLOCK_SIZE];
        __shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE];
       
        As[row][col] = GetElement(Asub, row, col);
        Bs[row][col] = GetElement(Bsub, row, col);

        __syncthreads();
        
        for (int e = 0; e < BLOCK_SIZE; ++e)
            Cvalue += As[row][e] * Bs[e][col];

        __syncthreads();
    

    SetElement(Csub, row, col, Cvalue);

""")


class MatrixStruct(object):
    def __init__(self, array):
        self._cptr = None

        self.shape, self.dtype = array.shape, array.dtype
        self.width = np.int32(self.shape[1])
        self.height = np.int32(self.shape[0])
        self.stride = self.width
        self.elements = cuda.to_device(array)                      # 分配内存并拷贝数组数据至device，返回其地址

    def send_to_gpu(self):
        self._cptr = cuda.mem_alloc(self.nbytes())                 # 分配一个C结构体所占的内存
        cuda.memcpy_htod(int(self._cptr), self.width.tobytes())    # 拷贝数据至device，下同
        cuda.memcpy_htod(int(self._cptr)+4, self.height.tobytes())
        cuda.memcpy_htod(int(self._cptr)+8, self.stride.tobytes())
        cuda.memcpy_htod(int(self._cptr)+16, np.intp(int(self.elements)).tobytes())

    def get_from_gpu(self):
        return cuda.from_device(self.elements, self.shape, self.dtype)  # 从device取回数组数据
   
    def nbytes(self):
        return self.width.nbytes * 4 + np.intp(0).nbytes


a = np.random.randn(400,400).astype(np.float32)
b = np.random.randn(400,400).astype(np.float32)
c = np.zeros_like(a)

A = MatrixStruct(a)
B = MatrixStruct(b)
C = MatrixStruct(c)
A.send_to_gpu()
B.send_to_gpu()
C.send_to_gpu()

matrix_mul = mod.get_function("matrix_mul")
matrix_mul(A._cptr, B._cptr, C._cptr, block=(16,16,1), grid=(25,25))
result = C.get_from_gpu()
print(np.dot(a,b))
print(result)

出现下面矩阵运算的结果即可说明在jetson nano上安装的pycuda成功了，之后就可以配合tensorrt使用啦！