CUDA_Getting_Started_2.2_Windows

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unexpected error from cudagetdevicecount

unexpected error from cudagetdevicecount摘要：1.概述：CUDA 设备计数错误2.错误原因：设备驱动程序版本不兼容3.解决方案：更新设备驱动程序4.结论：及时更新设备驱动程序以避免类似错误正文：近日，一些使用CUDA（Compute Unified Device Architecture，统一设备架构）编程的程序员在运行程序时遇到了“unexpected error from cudagetdevicecount”的错误提示。

本文将针对这一问题进行分析，并提供相应的解决方案。

首先，我们需要了解这个错误的含义。

这个错误提示主要是指在CUDA 编程过程中，从CUDA 设备获取设备数量时出现了意外错误。

而导致这一问题的原因可能有很多，如设备驱动程序版本不兼容、系统设置不当等。

经过分析，我们发现，导致“unexpected error from cudagetdevicecount”错误的主要原因是设备驱动程序版本不兼容。

可能是由于程序员在安装或升级CUDA 时，没有正确安装或升级相应的设备驱动程序，导致CUDA 设备驱动程序版本与系统中其他组件的不兼容，从而引发错误。

针对这一问题，我们可以采取以下解决方案：1.确认设备驱动程序版本：首先，程序员需要确认自己的系统中安装的CUDA 版本以及相应的设备驱动程序版本。

可以通过在NVIDIA 官网上下载最新的驱动程序，并查看驱动程序版本是否与CUDA 版本匹配。

2.更新设备驱动程序：如果发现设备驱动程序版本不兼容，程序员需要及时更新设备驱动程序。

可以通过NVIDIA 官网下载最新版本的驱动程序，并按照官方说明进行安装。

3.重新安装CUDA：如果更新设备驱动程序后仍然出现错误，可以尝试重新安装CUDA。

在重新安装过程中，请确保正确安装相应的设备驱动程序。

通过以上解决方案，程序员可以有效避免在CUDA 编程过程中出现“unexpected error from cudagetdevicecount”的错误提示。

cudaerrorcontextisdestory

cudaerrorcontextisdestory摘要：1.CUDA 错误背景2.CUDA 错误原因3.解决方案4.总结正文：一、CUDA 错误背景CUDA（Compute Unified Device Architecture）是NVIDIA 推出的一种通用并行计算架构，旨在充分利用GPU 的强大计算能力。

在使用CUDA 进行编程时，可能会遇到各种各样的错误，其中之一就是“cudaErrorContextIsDestroyed”。

二、CUDA 错误原因“cudaErrorContextIsDestroyed”错误通常表示CUDA 上下文（context）已被销毁，而相关资源尚未释放。

这种情况通常发生在程序试图访问已释放的内存或执行未初始化的操作时。

以下是一些可能引发该错误的原因：1.CUDA 上下文销毁：在执行某些操作之前，CUDA 上下文可能被意外销毁。

例如，使用CUDA API 时，调用顺序不当可能导致上下文在执行某些操作之前被销毁。

2.内存访问不当：在CUDA 程序中，对内存的访问需要遵循一定的规则。

如果程序试图访问已被释放的内存，就可能导致该错误。

3.并行不当：CUDA 允许开发者利用多线程并行执行计算。

如果在并行执行过程中出现错误，如线程同步问题，可能导致该错误。

三、解决方案要解决“cudaErrorContextIsDestroyed”错误，需要对程序进行仔细检查，确保CUDA 上下文、内存访问和并行操作都正确无误。

以下是一些建议：1.检查CUDA API 调用顺序，确保在执行相关操作之前上下文已正确初始化。

2.审查内存分配和释放代码，确保内存在使用完毕后正确释放，避免对已被释放的内存进行访问。

3.检查并行操作，确保线程同步和互斥操作正确。

可以使用CUDA 提供的同步原语（如cudaMemcpyAsync 和cudaMemcpy）来确保内存操作的同步。

4.调试程序，查找并修复潜在的错误。

pytorch（cuda）的安装

pytorch（cuda）的安装安装好之后可以进去虚拟环境，然后进去python，接着导⼊模块import torch，然后torch.cuda.get_device_capability()可以知道⾃⼰的显卡的算⼒我的cuda算⼒是3.5,⽽⼤神说pytorch1.3需要满⾜>=3.7算⼒，所以就不⽤想了！我的显卡是GeForce920m。

需要注意的是每种包（或框架）对应的python版本，对应的其它包的版本是⼀⼀对应的，需要搞清楚再来安装。

1.安装了anaconda之后先创建⼀个虚拟环境：创建虚拟环境：conda create -n env_name list of packagename，如：conda create -n pytorch python=3.72.然后⽤nvcc -V（如果没有反应就先安装sudo apt install nvidia-cuda-toolkit）查看Linux系统的cuda是什么版本，我的cuda版本是9.1.85。

3.接着去pytorch官⽹找安装pytorch的安装命令conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=9.2 -c pytorch4.进⼊虚拟环境，安装：conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=9.2 -c pytorch注意：可以直接覆盖，不需要先卸载再安装。

1.⼀开始我是⽤conda install pytorch torchvision cudatoolkit=10.2 -c pytorch来安装，没有指定pytorch的版本，所以它会⾃动安装最新的版本（1.5.1）。

但是运算的时候出现了错误RuntimeError: CUDA error: no kernel image is available for execution on the device（运⾏时错误：CUDA 错误：没有可在设备上执⾏的内核映像），可能是安装的cuda版本和系统的cuda版本不⼀样的原因。

02.CUDA_Toolkit_Release_Notes_Windows

--------------------------------------------------------------------------------
Supported NVIDIA Hardware
--------------------------------------------------------------------------------
Documentation (located in the doc directory)
--------------------------------------------------------------------------------
o CUDA_Toolkit_Release_Notes_Windows.txt
o CUDA_VideoDecoder_Library.pdf
- Documentation for the NVIDIA CUDA Video Decoder library.
o CUDA_VideoEncoder_df
- Documentation for the NVIDIA CUDA Video Encoder library.
o CURAND_Library.pdf
- User manual for the CUDA accelerated Random Number Generation library.
o CUSPARSE_Library.pdf
- User manual for the CUDA accelerated Sparse Matrix library.
include/
cuda.h CUDA driver API header

unexpected error from cudagetdevicecount

unexpected error from cudagetdevicecount怎么解决"unexpected error from cuGetDeviceCount" 是与NVIDIA 的CUDA（Compute Unified Device Architecture）相关的错误信息。

这个错误通常表明在尝试获取可用的CUDA 设备数量时发生了意外的问题。

以下是一些建议，希望能帮助你解决这个问题：1. 检查CUDA驱动和Toolkit的安装：-确保已正确安装与你的GPU兼容的CUDA驱动和Toolkit版本。

你可以在NVIDIA的官方网站上找到相应的CUDA版本和GPU驱动版本兼容性表。

2. 更新GPU驱动：-尝试更新你的GPU驱动程序到最新版本。

最新版本的驱动程序通常修复了已知的问题并提供更好的兼容性。

3. 确认GPU支持CUDA：-确保你的GPU确实支持CUDA。

一些较老的或低端的GPU可能不支持最新版本的CUDA。

4. 检查CUDA环境变量：-确保设置了正确的CUDA环境变量。

在环境变量中，`CUDA_HOME` 应该指向你的CUDA Toolkit的安装路径。

5. 查看系统日志和错误信息：-在尝试运行CUDA程序时，查看系统日志和错误信息。

这些信息可能提供更多的线索，帮助你找到问题所在。

6. 检查系统兼容性：-确保你的操作系统和硬件符合CUDA Toolkit的系统要求。

7. 尝试重新安装CUDA Toolkit：-如果可能，尝试重新安装CUDA Toolkit。

确保按照官方文档的说明进行操作。

8. 检查其他程序的冲突：-有时，其他正在运行的程序可能会干扰CUDA的正常运行。

尝试关闭其他可能与CUDA 冲突的程序。

9. 查看CUDA示例程序：-尝试运行CUDA Toolkit自带的示例程序，以确保CUDA环境设置正确且没有其他问题。

如果你仍然无法解决问题，建议查阅相关CUDA文档、用户论坛或与NVIDIA支持团队联系，以获取更详细的帮助。

unexpected error from cudagetdevicecount

unexpected error from cudagetdevicecount摘要：I.错误现象及原因A.错误信息B.CUDA概念介绍C.错误原因分析II.解决方法A.检查GPU是否支持CUDAB.确保正确安装了cuda-python库C.检查CUDA版本是否与Python和conda环境中的库兼容D.尝试在不同的Python环境中安装cuda-python库E.卸载并重新安装NVIDIA驱动III.总结正文：最近，我在使用Python和CUDA进行并行计算时遇到了一个意外的错误。

错误信息是“cudagetdevicecount() failed”。

经过一番搜索和尝试，我终于找到了问题的根源和解决方法。

本文将详细介绍这个错误的原因以及如何解决它。

首先，让我们了解一下CUDA（Compute Unified Device Architecture）的基本概念。

CUDA是一种由NVIDIA开发的并行计算架构，它允许开发人员利用NVIDIA GPU进行高性能计算。

在Python中，我们可以使用conda包管理器安装cuda-python库，从而利用CUDA进行计算。

然后，我们来看一下错误的具体原因。

根据错误信息，问题出在cudagetdevicecount()函数上。

这个函数用于获取支持CUDA的设备数量。

当这个函数返回一个错误时，通常意味着CUDA设备初始化失败或者GPU不支持CUDA。

为了解决这个问题，我们可以采取以下几个步骤：1.检查GPU是否支持CUDA。

我们可以通过查询NVIDIA官网或者在终端中使用nvidia-smi命令来查看GPU的详细信息。

2.确保正确安装了cuda-python库。

我们可以使用conda list命令来查看已安装的库，或者尝试重新安装cuda-python。

3.检查CUDA版本是否与Python和conda环境中的库兼容。

如果CUDA版本过低或者过高，可能会导致与Python库的兼容性问题。

torch.cuda常用函数

torch.cuda常用函数在深度学习领域中，使用图形处理单元（GPU）来加速计算是非常常见的。

PyTorch是一种非常流行的深度学习框架，也支持在GPU上进行计算。

而torch.cuda模块提供了一些常用的函数，用于管理GPU设备和在GPU 上执行操作。

在本文中，我们将逐步解释torch.cuda常用函数的使用方法，并讨论它们在加速深度学习训练和推断过程中的重要性。

第一步，为了使用torch.cuda模块，我们首先需要安装并配置正确的GPU 驱动程序。

GPU驱动程序负责与GPU硬件进行通信，并将计算任务分配给CUDA核心进行执行。

在安装GPU驱动程序的同时，还需要安装与之兼容的CUDA工具包。

这个工具包包含了一些必要的库和头文件，以及用于编译和链接CUDA代码的编译器。

安装完成之后，我们可以在PyTorch 中使用CUDA来加速计算。

第二步，一旦GPU驱动程序和CUDA工具包安装完成，我们就可以使用torch.cuda.is_available()函数来检查系统是否有可用的GPU。

“torch.cuda.is_available()”函数返回一个布尔值，表示是否可以使用CUDA进行计算。

如果返回True，则说明系统中有可用的GPU，并且支持CUDA计算。

这个函数通常在程序的开始部分被调用，以决定是否使用GPU加速。

第三步，如果系统中有可用的GPU，我们可以使用torch.cuda.device_count()函数来获取当前系统中的GPU设备数量。

“torch.cuda.device_count()”函数返回一个整数，表示可用的GPU设备数量。

这个函数对于设置多GPU训练非常有用。

我们可以使用torch.cuda.device(index)函数将当前设备指定为指定索引的设备。

这意味着我们可以在使用多个GPU进行并行计算时，将不同的计算任务分配给不同的GPU。

第四步，一旦我们确定了要使用的GPU设备，我们可以使用torch.cuda.set_device(device)函数将当前设备设置为指定的设备。

【2022新教程】Ubuntuserver20.04如何安装nvidia驱动和cuda-解。。。

【2022新教程】Ubuntuserver20.04如何安装nvidia驱动和cuda-解。

0 背景最近实验室的gpu服务器⽼是出问题，需要重新装显卡驱动。

⽹上教程⾮常⽼旧，很多说的都不知所云。

作者曾经尝试的安装⽅法和结果：官⽹下载driver——安装失败直接⽤cuda toolkit，⼀⼝⽓安装驱动和cuda——安装失败所以作者采⽤了本⽂中的⽅法。

本教程旨在记录本⼈亲⾃安装成功的⼀次经历。

在相同的系统环境下可安装成功，不同系统环境仅供参考，不保证成功。

本⽂旨在简洁明了、可直接复制命令执⾏、可复现、可读性好。

本⽂环境：ubuntu server 20.04，其他系统仅供参考！注意，本⽂仅适⽤于ubuntu server，不需要图形界⾯，没有对图形界⾯进⾏特殊考虑和验证！依赖图形操作界⾯的读者慎⽤！注意，本⽂仅适⽤于ubuntu server，不需要图形界⾯，没有对图形界⾯进⾏特殊考虑和验证！依赖图形操作界⾯的读者慎⽤！注意，本⽂仅适⽤于ubuntu server，不需要图形界⾯，没有对图形界⾯进⾏特殊考虑和验证！依赖图形操作界⾯的读者慎⽤！1 安装nvidia驱动1.1 查看是否安装了gccgcc -v若没有安装，则输⼊下⾯的命令，直接把包括gcc在内很多开发⼯具包⼀同安装sudo apt-get install build-essential1.2 禁⽤nouveau驱动编辑 /etc/modprobe.d/blacklist-nouveau.conf ⽂件，添加以下内容：blacklist nouveaublacklist lbm-nouveauoptions nouveau modeset=0alias nouveau offalias lbm-nouveau off关闭nouveau：echo options nouveau modeset=0 | sudo tee -a /etc/modprobe.d/nouveau-kms.conf注意，本⽂仅适⽤于ubuntu server，不需要图形界⾯，没有对图形界⾯进⾏特殊考虑和验证！依赖图形操作界⾯的读者慎⽤！注意，本⽂仅适⽤于ubuntu server，不需要图形界⾯，没有对图形界⾯进⾏特殊考虑和验证！依赖图形操作界⾯的读者慎⽤！注意，本⽂仅适⽤于ubuntu server，不需要图形界⾯，没有对图形界⾯进⾏特殊考虑和验证！依赖图形操作界⾯的读者慎⽤！完成后，重新⽣成内核并重启：sudo update-initramfs -usudo reboot重启后，执⾏：lsmod | grep nouveau。

cannot initialize cuda without aten_cuda library

无法初始化CUDA without aten_cuda库在深度学习中，GPU通常用于加速计算过程。

在使用CUDA进行GPU加速时，经常会遇到“cannot initialize CUDA without aten_cuda库”（无法初始化CUDA without aten_cuda库）的错误。

本文将介绍这个错误的原因和解决方法。

错误原因“cannot initialize CUDA without aten_cuda库”错误通常是由于缺少或无法加载aten_cuda库引起的。

aten_cuda库是PyTorch库的一部分，用于处理GPU加速计算相关的函数。

如果缺少aten_cuda库，就会导致无法正确初始化CUDA。

解决方法下面是一些可能的解决方法：1. 检查CUDA是否正确安装首先，需要确保正确安装了适用于您的GPU的CUDA版本。

可以通过查看NVIDIA官方网站上的CUDA下载页面来获取正确的CUDA版本，并确保按照官方指导进行安装。

2. 检查PyTorch版本PyTorch是GPU加速计算的主要框架之一，aten_cuda库是PyTorch的一部分。

因此，如果遇到“cannot initialize CUDA without aten_cuda库”错误，可以尝试升级或降级PyTorch版本。

可以通过运行以下命令来升级或降级PyTorch：pip install torch==<desired_version>将<desired_version>替换为所需的PyTorch版本号。

注意，升级或降级PyTorch可能需要升级或降级其他依赖项。

在更改版本之前，请务必查阅PyTorch官方文档，并确保与其他库兼容。

3. 升级GPU驱动程序确保安装了最新的GPU驱动程序，以便与最新版本的CUDA和PyTorch兼容。

可以通过访问NVIDIA官方网站并找到适用于您的GPU型号的最新驱动程序来升级驱动程序。

在windows下安装cuda(附VNC远程调用CUDA说明)

在windows下安装cuda安装环境在windows下，目前cuda只支持在 Visual Studio 7.x 系列、Visual Studio 8以及免费的 Visual Studio C++ 2005 Express。

所以需要预先安装以上软件中的任意一种。

下面我们以Visual Studio 2005 为例演示cuda的安装。

1、cuda安装包cuda是免费使用的，各种操作系统下的cuda安装包均可以在/object/cuda_get_cn.html上免费下载。

Cuda提供3个安装包，分别是：SDK, Toolkit和Display。

SDK包括许多例子程序和函数库。

Toolkit包括cuda的基本工具。

Display包括了NV显卡的驱动程序。

Toolkit是核心。

2、安装cuda2.1 安装cuda toolkit双击NVIDIA_CUDA_toolkit_2.0_win32.exe安装，安装完成后在安装目录下出现6个文件夹，分别是：Bin ：工具程序和动态链接库Doc ：相关文档Include ： header头文件包Lib ：程序库Open64 ：基于open64的cuda compilerSrc ：部分原始代码安装过程中toolkit自动设定了3个环境变量：CUDA_BIN_PATH、CUDA_INC_PATH和CUDA_LIB_PATH分别对应工具程序库、头文件库和程序库，预设路径为当前安装文件夹下的bin、include 和lib三个文件夹。

2.2 安装CUDA SDKSDK可以根据需要选择安装(推荐安装，因为SDK中的许多例子程序和函数库非常有用)。

2.3 安装 CUDA Display对于没有安装NV显卡的计算机，不需要安装Display安装包，程序也可以在模拟模式下运行。

3、在Visual Studio中使用cudaCUDA的主要工具是nvcc，它会执行所需要的程序，将CUDA程序编译并执行。

cuda samples用法 -回复

cuda samples用法-回复CUDA（Compute Unified Device Architecture）是NVIDIA推出的一种并行计算平台和编程模型，可为GPU（图形处理器）提供并行计算能力。

CUDA使开发人员能够利用GPU的强大性能来加速各种计算密集型应用程序。

在CUDA中，有许多示例代码可供开发人员使用。

这些示例代码可以帮助开发人员了解CUDA编程模型的基本概念和技术，并为他们提供一个快速入门的方式。

本文将详细介绍CUDA示例的使用方法和一些常见示例的功能。

首先，要使用CUDA示例，我们需要确保已安装了适当的CUDA开发环境。

这包括安装NVIDIA显卡驱动程序、CUDA Toolkit和在计算机上配置适当的编译环境。

一旦安装完CUDA开发环境，我们可以开始浏览和运行CUDA示例。

CUDA示例通常由一组源代码文件组成，每个文件都包含一个示例的实现。

示例的实现代码通常采用C或C++编写，并使用CUDA编程模型中的函数和语法扩展。

要编译和运行示例，我们可以使用CUDA Toolkit提供的命令行工具nvcc。

例如，假设我们下载并解压了CUDA示例源代码，现在我们可以使用终端或命令提示符打开示例目录。

从示例目录中，我们可以使用以下命令编译和运行示例：nvcc -o example example.cu./example上述命令将使用nvcc编译示例源文件example.cu，并生成可执行文件example。

然后，我们可以运行生成的可执行文件以查看示例的输出。

CUDA示例涵盖了许多不同的主题和技术，包括向量加法、矩阵乘法、并行排序、并行规约等等。

每个示例都具有相应的实现代码和文档说明，可以帮助开发人员理解示例背后的算法和实现细节。

以向量加法为例，CUDA示例提供了一个简单的矢量加法实现。

示例代码首先分配和初始化两个输入向量，然后将这两个向量作为参数传递给CUDA函数。

在CUDA函数中，每个线程负责处理一个元素，并将其与另一个向量的对应元素相加，然后将结果存储在输出向量中。

CUDA环境配置总结

CUDA环境配置总结一、操作系统：（1）windows操作系统：1.windows xp：(1)win32_xp_ CUDA3.0_VS2010_配置指南-DOC(2)xp下vs2008 +cuda4.0配置(3)win32_xp_ CUDA3.0_VS2008_开发环境配置(4)xp_vs2008_MFC环境中CUDA2.3程序的配置与调试-PDF(5)win32_XP_CUDA_ vs2005_SDK&ToolKit2.3配置说明(6)VS2008+CUDA3.2在台式WINXP下的安装超详细步骤2.win7(1)Win7_x64_VS2010下配置CUDA 4.0(2)Win7_Visual Studio2010配置CUDA 4.0(3)Win7（x32）下配置CUDA3.2+VS2010(4)Win7_VS2008配置CUDA3.2手册-DOC(5)Win7_CUDA3.2_on_VS2008(6)Win7_x64_vs2008 _cuda2.3配置指南-DOC(7)Win7_32_基于Visual_Studio 2005_的CUDA3.2开发平台搭建-pdf(8)CUDA4.0 X32 + Windows7 32bit + Visual Studio2008+ Visual Assist安装指南(9)Win7_64_+Visual_Studio 2010_CUDA4.0+V AssistX 安装经验(10)win7_32_基于Visual Studio 2010 的CUDA4.0 环境配置(11)环境搭建：CUDA4.0+VS2010+Win7_32（2）linux操作系统：(1)CUDA 在linux系统上安装指南-PDF(2)CUDA_C_Getting_Started_Linux-pdf(3)linux集群安装与并行计算-pdf(4)Linux操作系统关于集群的安装与并行计算-txt(5)Linux_并行计算环境使用-pdf(6)Linux环境CUDA4.0入门：安装前的准备二、Cuda版本：Cuda4.0:(1)xp下vs2008 +cuda4.0配置(2)Win7_x64_VS2010下配置CUDA 4.0(3)Win7_Visual Studio2010配置CUDA 4.0(4)Win7_64_+Visual_Studio 2010_CUDA4.0+V AssistX 安装经验Cuda3.2:(1)Win7（x32）下配置CUDA3.2+VS2010(2)Win7_VS2008配置CUDA3.2手册-DOC(3)Win7_CUDA3.2_on_VS2008(4)Win7_32_基于Visual_Studio 2005_的CUDA3.2开发平台搭建-pdf(5)VS2008+CUDA3.2在台式WINXP下的安装超详细步骤Cuda3.0:(1)Win32_xp_ CUDA3.0_VS2010_配置指南-DOC(2)win32_xp_ CUDA3.0_VS2008_开发环境配置Cuda2.3:(1)xp_vs2008_MFC环境中CUDA2.3程序的配置与调试-PDF(2)win32_XP_CUDA_ vs2005_SDK&ToolKit2.3配置说明(3)Win7_x64_vs2008 _cuda2.3配置指南-DOC三、开发平台：Vs2010：(1)VS2010下配置CUDA 4.0（win7 x64）(2)Visual Studio2010配置CUDA 4.0(3)window7（x32）下配置CUDA3.2+VS2010(4)VS2010_CUDA3.0_xp x32配置指南-DOC(5)Win7_64_+Visual_Studio 2010_CUDA4.0+V AssistX 安装经验Vs2008：(1)VS2008配置CUDA3.2手册-DOC(2)XP下vs2008 +cuda4.0配置(3)CUDA3.2_on_VS2008(4)CUDA_VS2008_开发环境配置(5)vs2008_x64_cuda配置指南-DOC(6)xp_vs2008_MFC环境中CUDA程序的配置与调试-PDF(7)VS2008+CUDA3.2在台式WINXP下的安装超详细步骤Vs2005：(1)vs2005 cuda环境配置(2)基于Visual_Studio 2005_的CUDA3.2开发平台搭建-pdf（win7 x32）(3)CUDA_win32_XP_vs2005_SDK&ToolKit2.3配置说明(4)CUDA+VS2005环境配置及编译(5)CUDA3.1 X32 + Windows 7 32bit + Visual Studio 2005 + Visual assist安装指南CUDA软硬件环境简介-PDFCUDA开发环境配置。

2024年风辰的CUDA培训教程(含多款)

风辰的CUDA培训教程(含多款)风辰的CUDA培训教程一、引言二、CUDA编程基础1.GPU架构在介绍CUDA编程之前，需要了解GPU的架构。

GPU由成百上千个核心组成，每个核心都可以执行相同的指令，因此GPU具有极高的并行计算能力。

CUDA编程模型允许开发者将计算任务分配给GPU 上的多个核心，从而实现高效的并行计算。

2.CUDA编程模型（1）主机（Host）：指CPU及其内存，用于执行串行代码和CUDA代码的调度。

（2）设备（Device）：指GPU及其内存，用于执行并行计算任务。

（3）内核（Kernel）：指在设备上执行的并行函数，用于执行具体的计算任务。

（4）线程层次结构：CUDA中的线程被组织成三维的线程块（threadblock）和一维的网格（grid）。

线程块内的线程可以协作，而不同线程块之间的线程相互独立。

3.CUDA程序结构（1）主机端：分配主机和设备内存，将数据从主机传输到设备。

（2）设备端：编写内核函数，定义并行计算任务。

（3）主机端：调用内核函数，启动GPU上的并行计算。

（4）主机端：从设备内存中读取计算结果，释放主机和设备内存。

三、CUDA编程实践1.环境搭建在进行CUDA编程之前，需要搭建相应的开发环境。

具体步骤如下：（1）安装NVIDIAGPU驱动程序。

（2）安装CUDAToolkit，包含CUDA开发工具和运行时库。

（3）配置CUDA开发环境，如VisualStudio、Eclipse等。

2.编写第一个CUDA程序在本节中，我们将编写一个简单的CUDA程序，实现向量加法。

具体步骤如下：（1）在主机端分配内存，初始化输入向量。

（2）将输入向量传输到设备内存。

（3）编写向量加法的内核函数。

（4）在主机端调用内核函数，启动GPU上的并行计算。

（5）从设备内存中读取计算结果，并验证正确性。

（6）释放主机和设备内存。

3.性能优化（1）合理设置线程块大小和网格大小，以充分利用GPU资源。

深度学习环境配置指南！（Windows、Mac、Ubuntu全讲解）

深度学习环境配置指南！（Windows、Mac、Ubuntu全讲解）第⼀时间获取价值内容⼊门深度学习，很多⼈经历了从⼊门到放弃的⼼酸历程，且千军万马倒在了⼊门第⼀道关卡：环境配置问题。

俗话说，环境配不对，学习两⾏泪。

如果你正在⾯临配置环境的痛苦，不管你是Windows⽤户、Ubuntu⽤户还是苹果死忠粉，这篇⽂章都是为你量⾝定制的。

接下来就依次讲下Windows、Mac和Ubuntu的深度学习环境配置问题。

⼀、Windows系统深度学习环境配置系统：Win10 64位操作系统安装组合：Anaconda+PyTorch(GPU版)+GTX1060开源贡献：伍天⾈，内蒙古农业⼤学1.1 打开Anaconda Prompt1、conda create -n pytorch python=3.7.0：创建名为pytorch的虚拟环境，并为该环境安装python=3.7。

2、activate pytorch：激活名为pytorch的环境1.2 确定硬件⽀持的CUDA版本NVIDIA控制⾯板-帮助-系统信息-组件2020年5⽉19⽇16:46:31，我更新了显卡驱动，看到我的cuda⽀持11以内的1.3 确定pytorch版本，torchvision版本因为官⽅源太慢了，这⾥使⽤清华源下载1.4 镜像中下载对应的安装包清华镜像：https:///anaconda/cloud/pytorch/win-64/pytorch：torchvision：1.5 本地安装接着第⼀步，在pytorch环境下进⾏安装，依次输⼊如下指令。

然后回到虚拟环境所在⽬录，⽤conda install anaconda安装环境所需的基础包1.6 测试代码1：from future import print_functionimport torchx = torch.rand(5, 3)print(x)输出类似于以下的张量：代码2：import torchtorch.cuda.is_available()输出：True如果以上两段代码输出⽆异常，表明环境搭建成功。

linux或windows环境下pytorch的安装与检查验证（解决runtimeerror问题）

linux或windows环境下pytorch的安装与检查验证（解决
runtimeerror问题）
1、pytorch官⽹下载对应安装⽂件
在getstarted处选择对应版本
⽅法⼀：直接使⽤对应的安装指令进⾏安装
但是其实这样⽐较容易断或者出现runtimeout的error。

解决办法：使⽤pip先进⾏源的查找，在开始下载后停掉，然后找到屏幕上打印出来的链接，⼿动下载whl 然后⼿动安装。

⽅法⼆：选择相应版本的whl进⾏离线下载
进⾏whl⽂件的下载然后安装
pytorch从链接安装指定版本
pip3 install whl
pip3 install torchvision
2、验证
import torch
import torchvision
print(torch.cuda.is_available())#输出为true即可
3、pytorch版本查看
import torch
print(torch.__version__)
总结
以上所述是⼩编给⼤家介绍的linux或windows环境下pytorch的安装与检查验证(解决runtimeerror问题)，希望对⼤家有所帮助，如果⼤家有任何疑问请给我留⾔，⼩编会及时回复⼤家的。

在此也⾮常感谢⼤家对⽹站的⽀持！
如果你觉得本⽂对你有帮助，欢迎转载，烦请注明出处，谢谢！。

morphoj安装教程

morphoj安装教程最近人工智能等多门课需要复现论文，近两年的论文很多都是Pytorch环境，所以，这里总结一下Pytorch的安装教程，做好最快、最简单、最好地完成安装。

本机环境Win10+1050Ti+Python3.71、查看本机的CUDA版本cmd命令行输入nvidia-smi，在第一行最右边可以看到CUDA 的版本号，我的版本是11.12、安装Pytroch1、点击进入Pytorch官网然后选择Get Started，就是如下界面2、这里进行Pytorch版本的选择，首先我选择的是Stable稳定版，然后OS是Windows系统，Package包就使用Conda，Language肯定选Python，最后的Compute Platform就根据大家的需求来定了。

大家如果想在自己电脑（具有NVIDIA显卡）上跑通代码，就选CUDA，如果不需要在自己电脑上跑（在服务器上跑）或者没有独立显卡，就选CPU。

我们在第一步已经看过自己的NAVIDA的CUDA版本了，这里我们一定要选择比自己版本低的CUDA。

比如，像我的版本是11.1，那么就只能选择10.2，因为11.3有点高了。

同样道理，基本上大家都能选择10.2。

3、安装下anaconda深度学习神器，没有安装的同学，请按照这篇教程安装好anaconda，并创建一个python环境：anaconda安装配置教程，python的版本根据你的需求而定，这里我根据复现算法的需求环境，安装的是python3.7。

4、打开anaconda命令行，先激活需要安装Pytorch的python环境（这里我将python环境命名为pytorch，环境名称随意），复制第二步最下面那段命令行语句，然后回车执行：conda install pytorch torchvision torchaudio cudatoolkit=10.2-c pytorch5、可以看到将要安装的包里面有pytorch和cudatoolkit，我们输入y确认，然后回车6、下面就等待这些package安装成功，可以看到Pytorch还是比较大的，所以耐心等待一下。

pyodbc文档之Gettingstarted(开始使用)

pyodbc文档之Gettingstarted（开始使用）PYODBC是一个用于Python的ODBC数据库接口模块，它让开发者能够通过Python编程语言来连接和操作ODBC兼容的数据库。

在开始使用PYODBC之前，我们需要进行一些准备工作。

首先，我们需要确保已经安装了Python和pyodbc模块。

可以使用以下命令来安装pyodbc模块：```pip install pyodbc```接下来，我们需要准备一个ODBC数据源来进行测试。

可以在Windows操作系统的控制面板中找到ODBC数据源管理员，然后创建一个新的系统DSN数据源。

此外，还需要确保该数据源能够连接到一个可用的数据库。

接下来，我们可以编写Python代码来连接到数据库并执行一些操作。

以下是一个简单的示例代码：``` pythonimport pyodbc#连接到数据库conn =pyodbc.connect('DSN=testdb;UID=testuser;PWD=testpassword') #创建一个游标对象cursor = conn.cursor#执行SQL查询语句cursor.execute("SELECT * FROM customers")#获取查询结果集rows = cursor.fetchall#打印查询结果for row in rows:print(row)#关闭游标和数据库连接cursor.closeconn.close```在以上代码中，我们首先使用pyodbc.connect(函数连接到数据库，需要传入一个包含连接信息的字符串。

在这个例子中，我们使用的是一个名为"testdb"的ODBC数据源，用户名为"testuser"，密码为"testpassword"。

接下来，我们使用conn.cursor(方法创建一个游标对象，可以通过游标对象来执行SQL查询语句。

Nsight Eclipse Plugins Edition DG-06450-001 _v9.1

Getting Started GuideTABLE OF CONTENTS Chapter 1. Introduction (1)1.1. About Nsight Eclipse Plugins Edition (1)Chapter 2. Using Nsight Eclipse Edition (2)2.1. Installing Nsight Eclipse Edition (2)2.1.1. Installing CUDA T oolkit (2)2.1.2. Configure CUDA T oolkit Path (2)2.2. Nsight Eclipse Main Window (4)2.3. Creating a New Project (5)2.4. Importing CUDA Samples (6)2.4.1. cuHook Sample (6)2.5. Configure Build Settings (7)2.6. Debugging CUDA Applications (8)2.7. Remote development of CUDA Applications (10)2.8. Debugging Remote CUDA Applications (12)2.9. Profiling CUDA applications (18)2.10. Importing Nsight Eclipse Projects (19)2.11. More Information (21)LIST OF FIGURESFigure 1 Nsight main window after creating a new project (5)Figure 2 Debugging CUDA application (9)Figure 3 Debugging CUDA application (10)Figure 4 Debugging remote CUDA application (18)Figure 5 Profiling CUDA Application (19)This guide introduces Nsight Eclipse Plugins Edition and provides instructions necessary to start using this tool. Nsight Eclipse is based on Eclipse CDT project. Fora detailed description of Eclipse CDT features consult the integrated help "C/C++ Development User Guide" available from inside Nsight (through Help->Help Contents menu).1.1. About Nsight Eclipse Plugins EditionNVIDIA® Nsight™ Eclipse Edition is a unified CPU plus GPU integrated development environment (IDE) for developing CUDA® applications on Linux and Mac OS X for the x86, POWER and ARM platforms. It is designed to help developers on all stages of the software development process. Nsight Eclipse Plugins can be installed on vanilla Eclipse 4.4 or later using the standard Help->Install New Software.. Menu. The principal features are as follows:‣Edit, build, debug and profile CUDA-C applications‣CUDA aware source code editor – syntax highlighting, code completion and inline help‣Graphical user interface for debugging heterogeneous applications‣Profiler integration – Launch visual profiler as an external application with the CUDA application built in this IDE to easily identify performance bottlenecksFor more information about Eclipse Platform, visit 2.1. Installing Nsight Eclipse EditionNsight Eclipse Plugins archive is part of the CUDA Toolkit. Nsight Eclipse Plugins archive can be installed using the Help -> Install New Software... Menu on Eclipse 4.4 or later2.1.1. Installing CUDA T oolkitTo install CUDA Toolkit:1.Visit the NVIDIA CUDA Zone download page:/object/cuda_get.html2.Select appropriate operating system. Nsight Eclipse Edition is available in Mac OS X and Linux toolkit packages.3.Download and install the CUDA Driver.4.Download and install the CUDA Toolkit.5.Follow instructions to configure CUDA Driver and Toolkit on your system.2.1.2. Configure CUDA T oolkit PathTo get started, CUDA Toolkit path must be configured in Eclipse with Nsight Plugins:1.Open the Preferences page, Window > Preferences.2.Go to CUDA toolkit section.3.Select the CUDA toolkit path to be used by Nsight. CUDA tookits that are installed in the default location will automatically appear.4.CUDA toolkit path can be also specified in the project properties page in order to use different toolkit for a project.5.For QNX: When QNX is selected as Target OS, a dialog will be displayed to set theQNX_HOST and QNX_TARGET environment variables if they were not already set.QNX_HOST environment variable identifies the directory that holds the host-related components:QNX_TARGET environment variable identifies the directory that holds the target-related components:2.2. Nsight Eclipse Main WindowOn the first run Eclipse will ask to pick a workspace location. The workspace is a folder where Nsight will store its settings, local files history and caches. An empty folder should be selected to avoid overwriting existing files.The main Nsight window will open after the workspace location is selected. The main window is divided into the following areas:‣Editor - displays source files that are opened for editing.‣Project Explorer - displays project files‣Outline - displays structure of the source file in the current editor.‣Problems - displays errors and warnings detected by static code analysis in IDE or bya compiler during the build.‣Console - displays make output during the build or output from the running application.2.3. Creating a New Project1.From the main menu, open the new project wizard - File > New... > C/C++ Project2.Specify the project name and project files location.3.Specify the project type like executable project.4.Specify the CUDA toolchain from the list of toolchains.5.Specify the project configurations on the next wizard page.plete the wizard.The project will be shown in the Project Explorer view and source editor will be opened.7.Build the project by clicking on the hammer button on the main toolbar.Figure 1 Nsight main window after creating a new project2.4. Importing CUDA SamplesThe CUDA samples are an optional component of the CUDA Toolkit installation. Nsight provides a mechanism to import these samples and work with them easily: Samples that use the CUDA driver API (suffixed with "Drv") are not supported byNsight.1.From the main menu, open the new project wizard - File > New... > C/C++ Project2.Specify the project name and project files location.3.Select Import CUDA Sample under Executable in the Project type tree.4.Select CUDA toolchain from the Toolchains option. location.5.On the next wizard page select project sample you want to import. Also select the target CPU architecture. Press Next...6.Specify the project parameters on the next wizard page.plete the wizard.The project will be shown in the Project Explorer view and source editor will be opened.8.Build the project by clicking on the hammer button on the main toolbar.2.4.1. cuHook SamplecuHook sample builds both the library and the executable. cuHook sample should be imported as the "makefile" project using the following steps.1.From the main menu, open the new project wizard - File > New... > C/C++ Project2.Select project type "Makefile project" and choose "Empty Project"3.Specify the project name and project files location.plete the wizard.The project will be shown in the Project Explorer view.5.Right click on the project - Import... > General > File System6.On the next wizard page, select the location of cuHooksample(Samples/7_CUDALibraries/cuHook)7.Select all the source files and makefile and Finish the wizard8.Build the project by clicking on the hammer button on the main toolbar.9.To run the sample, from the main menu - Run > Run Configurations... > Select the executable > Go to Environment tab > New... > enter Name=LD_PRELOAD, Value=./ libcuhook.so.1 > Run will execute the sample2.5. Configure Build SettingsTo define build settings: In the C/C++ Projects view, right-click your project, and select Properties. Select C/C++ Build, Settings from the list.The following are the categories of Nvcc linker settings that can be configured for the selected project.All options field in the main page is not editable and it's the collection of options setin the child categories.‣Libraries - Configure library search path(-L) and to include linker libraries(-l). When you are cross compiling for different target os, the library search path should point to the appropriate location where the target os libraries are present.‣Miscellaneous - Set additional linker options and option to link with OpenGL libraries.‣Shared Library Settings - Set option to build a shared library.The following are the categories of Nvcc Compiler settings that can be configured for the selected project.All options field in the main page is not editable and it's the collection of options set‣Dialect - Select the language standard and dialect options.‣Preprocessor - Add the defined and undefined symbols for the preprocessor.‣Includes - Set include paths and include files for the compiler.‣Optimization - Set the compiler optimization level.‣Debugging - Set the options to generate debug information.‣Warnings - Set inhibit all warning messages.‣CUDA - Generate code for different real architectures with the PTX for the same vitrual architectures.2.6. Debugging CUDA ApplicationsNsight must be running and at least one project must exist.1.In the Project Explorer view, select project you want to debug. Make sure the project executable is compiled and no error markers are shown on the project.2.Right click on the project and go to Debug As > NVIDIA CUDA GDB Debugger menu.3.You will be offered to switch perspective when you run debugger for the first time. Click "Yes".Perspective is a window layout preset specifically designed for a particular task.4.Application will suspend in the main function. At this point there is no GPU code running.5.Add a breakpoint in the device code. Resume the application.Debugger will break when application reaches the breakpoint. You can now explore your CUDA device state, step through your GPU code or resume the application.Figure 2 Debugging CUDA applicationAdditional debugger options can be set in the debug configuration dialog through Run > Debug Configurations .. menu..Figure 3 Debugging CUDA application2.7. Remote development of CUDA ApplicationsNsight Eclipse Edition also supports remote development of CUDA application starting with CUDA Toolkit 6.0. The picture below shows how Nsight Eclipse Edition can beused for local as well as remote development:For remote development you do not need any NVIDIA GPU on your host system. The remote target system can be a Linux x86 or POWER system with an NVIDIA GPU or an Tegra-based ARM system. Nsight IDE and UI tools can only be hosted on x86 and POWER systems.Nsight Eclipse Plugins supports the cross compilation mode for remote devices.In the cross compilation mode the project resides on the host system and the cross compilation is also done on the host system. The cross compilation mode is only supported on an Ubuntu x86 host system.To cross compile select the target cross compile architecture in CPU architecture dropdown in the project properties page:2.8. Debugging Remote CUDA Applications Remote debugging is available starting with CUDA Toolkit 5.5. A dedicated GPU is not required to use Nsight remote debugging UI. A dedicated GPU is still required on the debug target. Only Linux targets are supported. Debug host and target may run different operating systems or have different CPU architectures. The remote machine must be accessible via SSH and CUDA Toolkit must be installed on both machines.If there is a firewall between the host and the target, it must be set up to let RSP1.Select the project and right click then go to Debug As...>NVIDIA CUDA GDBDebugger(Remote) menu item.2.Type the full path to a local executable or select one using the Local file... button.3.Select a remote connection from a drop-down list or press the Add connection... button to create a new one.4.If you are creating a new remote connection, select the SSH Only connection type, press Next, and type the host name(or IP address) as well as the connection name anddescription (both are optional) and then press Finish.5.For Android devices: To configure the remote connection using Android debug bridge, select the Android debug bridge from the Remote Connection drop-downlist, Android device must be connected to the host system using USB port.Press Manage button, and enter or select the path to adb utility. You need to install Android SDK platform tools to use Android debug bridge. press Detect button tofind the android device available through ADB.6.Optional: Press Connect to verify the selected remote connection.7.Press the Next button.8.Type the full path to cuda-gdbserver on the remote system or select one using theBrowse... button.9.Click on "Add new path" or on the Browse... button to specify the path to the shared libraries the remote application depends on.10.C lick on the Finish button to finish the new debug configuration wizard and start debugging the application.11.Y ou will be offered to switch perspective when you run the debugger for the first time. Click Yes.Perspective is a window layout preset specifically designed for a particular task. The debugger will stop at the application main routine. You can now set breakpoints, orresume the application.Figure 4 Debugging remote CUDA application2.9. Profiling CUDA applicationsNsight must be running and at least one project must exist. Profiler cannot be used when debugging session is in progress.Nsight Eclipse Edition profiling features are based on the NVIDIA Visual Profiler ( nvvp ) code. Nsight Eclipse Plugins Edition will launch the Visual Profiler as an external tool with the executable and other information from the selected project.1.In the Project Explorer view, select project you want to profile. Make sure the project executable is compiled and no error markers are shown on the project.2.Select the project and right click and go to Profile As>NVIDIA Visual Profiler menu.Nsight Eclipse will launch the Visual Profiler to specify extra profiler options with the executable information already passed from the selected project.Figure 5 Profiling CUDA Application2.10. Importing Nsight Eclipse ProjectsThe projects that are created with Nsight Eclipse Edition can be imported into the Eclipse workbench with Nsight Eclipse plugins.1.Open Nsight Eclipse edition and select the project that needs to be exported.2.Right click on the Nsight Eclipse project and go to - Export > C/C++ > C/C++ Project Settings > Next menu.3.Select the project and settings to export.4.Specify the "Export to file" location.5.Settings will be stored in the given XML file.6.Go to Eclipse workbench where the project settings needs to be imported.7.Create a C/C++ Project from the main menu File > New > C/C++ Project8.Specify the project name and choose Empty project type with CUDA toolchains.9.Right click on the project to import the source files. Import > General > File System >(From directory) or copy the source files from the existing project.10.I mport the project settings like include paths and symbols using the following right click menu Import > C/C++ > C/C++ Project Settings >Next...11.S elect the location of the project settigns file and select the project and configurationon the next wizard page.12.C omplete the wizard.The project settings will be imported from the file exported from Nsight Eclipse Edition.13.B uild the project by clicking on the hammer button on the main toolbar.2.11. More InformationMore information about the Eclipse CDT features and other topics is available in the Help contents. To access Help contents select Help->Help Contents from the Nsight main menu.More information about CUDA, CUDA Toolkit and other tools is available on CUDAweb page at /cudaNoticeALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATEL Y, "MATERIALS") ARE BEING PROVIDED "AS IS." NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSL Y DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE.Information furnished is believed to be accurate and reliable. However, NVIDIA Corporation assumes no responsibility for the consequences of use of such information or for any infringement of patents or other rights of third parties that may result from its use. No license is granted by implication of otherwise under any patent rights of NVIDIA Corporation. Specifications mentioned in this publication are subject to change without notice. This publication supersedes and replaces all other information previously supplied. NVIDIA Corporation products are not authorized as critical components in life support devices or systems without express written approval of NVIDIA Corporation.TrademarksNVIDIA and the NVIDIA logo are trademarks or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Copyright© 2007-2018 NVIDIA Corporation. All rights reserved.。

CUDA_Getting_Started_2.1_Windows

Getting StartedNVIDIA CUDA 2.1 Installation and Verification on Microsoft Windows XP and Windows VistaNovember 2008Getting Started with CUDA ii November 2008Table of ContentsChapter 1. Introduction (1)CUDA—Supercomputing on Desktop Systems (1)System Requirements (2)About This Document (2)Chapter 2. Installing CUDA (3)Verify CUDA-Capable GPU (3)Download CUDA Software (4)Install the CUDA Driver for Microsoft Windows XP or Windows Vista (4)Multiple-GPU Configurations (5)CUDA Software (5)Installing the Software (5)Verify the Installation (7)Chapter 3. Compiling a CUDA Program (10)Compiling Sample Projects (10)Sample Projects (10)What’s Next? (11)November 2008 iiiGetting Started with CUDA iv November 2008November 2008 1 Chapter 1.IntroductionCUDA—Supercomputing onDesktop SystemsNVIDIA ® CUDA TM is a general purpose parallel computing architecture introduced by NVIDIA. It includes the CUDA Instruction Set Architecture (ISA) and the parallel compute engine in the GPU. To program to the CUDA architecture,developers can, today, use C, one of the most widely used high-level programming languages, which can then be run at great performance on a CUDA enabled processor.The CUDA architecture and its associated software were developed with several design goals in mind:Provide a small set of extensions to standard programming languages, like C, that enable a straightforward implementation of parallel algorithms. With CUDA and C for CUDA, programmers can focus on the task of parallelization of thealgorithms rather than spending time on their implementation.Support heterogeneous computation where applications use both the CPU and GPU. Serial portions of applications are run on the CPU, and parallel portions are offloaded to the GPU. As such, CUDA can be incrementally applied to existing applications. The CPU and GPU are treated as separate devices that have their own memory spaces. This configuration also allows simultaneous computation on both the CPU and GPU without contention for memory resources. CUDA-capable GPUs have hundreds of cores that can collectively run thousands of computing threads. Each core has shared resources, including registers and memory. The on-chip shared memory allows parallel tasks running on these cores to share data without sending it over the system memory bus.This guide will show you how to install and check the correct operation of C for CUDA and CUDA runtimes.Getting Started with CUDA 2 November 2008System RequirementsTo use CUDA on your system, you will need the following installed:CUDA-enabled GPUDevice driverCUDA software (available at no cost from /cuda ) Microsoft Visual Studio 2005 or 2008, or the corresponding versions of Microsoft Visual C++ ExpressAbout This DocumentThis document is intended for readers familiar with Microsoft Windows XP orMicrosoft Windows Vista and the Microsoft Visual Studio environment. You do not need previous experience with CUDA or experience with parallel computation.November 2008 3Chapter 2.Installing CUDAThe installation of CUDA on a system running Microsoft Windows consists of four simple steps:Verify the system has a CUDA-capable GPUDownload the CUDA softwareInstall the driver for Windows XP or Windows Vista (if necessary) Install the CUDA softwareTest your installation by compiling and running one of the sample programs in the CUDA software to validate that the hardware and software are running correctly and communicating with each other.Verify CUDA-Capable GPUMany of the NVIDIA products today contain CUDA-enabled GPUs. These include:NVIDIA GeForce ® 8, 9, and 200 series GPUsNVIDIA Tesla™ computing solutions Many of the NVIDIA Quadro ® products An up-to-date list of CUDA-enabled GPUs can be found on the NVIDIA CUDA Web site at /object/cuda_learn_products.html . The Release Notes for the CUDA Toolkit also contain a list of supported products.To verify which video adapter your Windows system uses, open the Control Panel (Start Control Panel) and double click on System . In the System Propeties window that opens, click the Hardware tab, then Device Manager . Expand the Display adapters entry. There you will find the vendor name and model of your graphics card. Note: It is possible to develop CUDA software in the absence of a CUDA-enabled GPU.You can test the software in an emulation mode described later in this document. Naturally, performance on this platform is far less than on the CUDA-enabledprocessor, so the emulated hardware should not be used for release versions and performance tuning.Getting Started with CUDA 4 November 2008Download CUDA Software The CUDA driver is integrated in the NVIDIA graphics driver and is available from the main CUDA download site at /object/cuda_get.html. Choose the platform you are using, click Search , and download the driver, the CUDA Toolkit, and the CUDA SDK. The following sections describe the installation and configuration of version 2.1 of these packages.Install the CUDA Driver forMicrosoft Windows XP orWindows VistaAs mentioned in the previous section, the CUDA driver is integrated in the NVIDIA graphics driver. To use CUDA 2.1, you must have at least version 181.20 of the NVIDIA ForceWare ® graphics driver for Windows XP and Windows Vista. In most cases, if you are running a recent NVIDIA graphics adapter that has support for CUDA, you already have installed the CUDA driver. Verify that your system is running at least version 181.20.To identify the version of your NVIDIA driver, look in the NVIDIA Control Panel. Open the NVIDIA Control Panel by right clicking on the desktop and selecting by NVIDIA Control Panel . Click the System Information button in the lower leftcorner of the main panel to display a dialog box similar to that shown in Figure 1.Figure 1. System Information Dialog Box The ForceWare driver versionnumber must be 181.20 or higherIf you need to update your driver go to/object/cuda_get.html.Installing CUDANovember 2008 5 Note: New versions of CUDA can require updates of the driver, so always verify that youare running the right release of the driver for the version of CUDA you are using.Multiple-GPU ConfigurationsIf you have a multiple-GPU configuration, including -GX2 cards, for best CUDA performance you should disable SLI. On Windows Vista, each GPU needs its own NVIDIA desktop, which you can set up in the NVIDIA Control Panel. Alternatively some newer versions of the Forceware drivers offer the option to select secondary GPU as a dedicated for NVIDIA PhysX – which also enables it for use for applications using CUDA.CUDA SoftwareTo run CUDA programs, you will need the following CUDA software:The CUDA Toolkit The CUDA SDKThe CUDA Toolkit contains the tools needed to compile and build a CUDA application in conjunction with Microsoft Visual Studio. It includes tools, libraries, header files, and other resources.The CUDA SDK (software development kit) includes sample projects that have all the necessary project configuration and build files to perform one-click builds using Microsoft Visual Studio.Both software packages are available for 32-bit Windows XP and Windows Vista (called x86 on the download site) and 64-bit Windows XP and Windows Vista (called x86-64 on the download site). Download instructions appear in an earlier section of this chapter.Before installing these packages, you should read the Release Notes bundled with each, as these notes provide details on installation and software functionality. Installing the SoftwareFollow these few steps for a successful installation:1. Download the NVIDIA CUDA software from/object/cuda_get.html and save the installer to yourdesktop.2.Uninstall previous versions of the NVIDIA CUDA Toolkit and NVIDIA CUDA SDK if they have previously been installed. You can uninstall the NVIDIA CUDA Toolkit through the Start menu:Start All Programs NVIDIA Corporation CUDA Toolkit Uninstall CUDA .Uninstalling the CUDA SDK uses the same sequence.Getting Started with CUDA6 November 2008 3.Install version 2.1 of the NVIDIA CUDA Toolkit by runningNVIDIA_CUDA_Toolkit_2.1_Win32.exe (or Win64.exe, if you are using a 64-bit version of Windows). The CUDA Toolkit is installed by default underC:\CUDA. Several environment variables are defined with the toolkit installation: CUDA_BIN_PATH (defaults to C:\CUDA\bin) contains the compilerexecutables and runtime libraries.CUDA_INC_PATH (defaults to C:\CUDA\include) contains the include filesneeded to compile CUDA programs.CUDA_LIB_PATH (defaults to C:\CUDA\lib) contains the libraries needed for linking CUDA codes.In addition to these directories, the CUDA Toolkit installation also includes a documentation directory (C:\CUDA\doc) containing the CUDA Programming Guide, compiler guide, and guides for the CUDA implementation of the BLAS and FFT libraries.4.Install version 2.1 of the NVIDIA CUDA SDK by runningNVIDIA_CUDA_SDK_2.1_Win32.exe (or Win64.exe, if you are running the 64-bit version of Microsoft Windows). The CUDA SDK is installed inC:\Documents and Settings\All Users\Application Data\NVIDIACorporation\NVIDIA CUDA SDK and contains source code for many example problems and templates for Microsoft Visual Studio.Installing CUDAVerify the InstallationThe version of the CUDA Toolkit can be checked by running “nvcc –V” in aCommand Prompt window. A Command Prompt window can be obtained throughthe Start menu as follows: Start All Programs Accessories Command Prompt.In the CUDA SDK, NVIDIA includes sample programs that come in both sourceand compiled form. To verify a correct configuration of the hardware and software,it is highly recommended that you run the bandwidthTest program located inC:\Documents and Settings\All Users\Application Data\NVIDIACorporation\NVIDIA CUDA SDK\bin\win32\Release, presuming that you usedthe default installation directory structure. (On 64-bit versions of Windows, thedirectory name ends with \win64\Release.)If CUDA is installed and configured correctly, the output should look similar toFigure 2.Figure 2. Valid Results from Sample CUDAbandwidthTest ProgramNote that your device name (second line) and the bandwidth numbers will varyfrom system to system. The important items are the second line, which confirms aCUDA device was found, and the second-to-last line, which confirms that allnecessary tests passed.Should the tests not pass, make sure you do have an NVIDIA GPU on your systemthat supports CUDA and make sure it is properly installed.Getting Started with CUDATo see a graphical representation of what CUDA can do, run the sample Particlesexecutable in NVIDIA CUDA SDK\bin\win32\Release (or …\win64\Release on64-bit Windows).Installing CUDAChapter 3.Compiling a CUDA ProgramThe project files in the CUDA SDK have been designed to provide simple, one-click builds of the programs, which include all source code. To build the 32-bit or64-bit Windows projects (for release, debug, or emulated release and debug—calledemurelease and emudebug, respectively), use the provided *.sln solution files forMicrosoft Visual Studio 2005 and *_vc90.sln for Microsoft Visual Studio 2008.(Likewise for the corresponding versions of Microsoft Visual C++ ExpressEdition.) You can use either the solution files located in each of the examplesdirectories in NVIDIA CUDA SDK\projects or the global solution filesRelease.sln or Release_vc90.sln located in NVIDIA CUDA SDK\projects. Compiling Sample ProjectsThe bandwidthTest project, first mentioned on page 10, is a good sample projectto build and run. It is located in the C:\Documents and Settings\AllUsers\Application Data\NVIDIA Corporation\NVIDIA CUDASDK\projects\bandwidthTest directory. The output is placed inNVIDIA CUDA SDK\bin\win32\Debug. (As mentioned previously, the \win32segment of this address will be \win64on 64-bit versions of Windows.) Thislocation presumes that you used the default installation directory structure.Build the program using the appropriate solution file and run the executable. If allworks correctly, the output should be similar to Figure 2.Sample ProjectsThe sample projects come in four configurations: debug and release (where releasecontains no debugging information), and emulated versions of both. The emulatedversions are for developing and running CUDA software in the absence of a CUDAGPU.A few of the example projects require some additional setup. The simpleD3Dexample requires the system to have a Direct3D SDK installed and the Visual C++directory paths (located in Tools Options...) properly configured. Consult theDirect3D documentation for additional details.Most samples link to a utility library called cutil whose source code is in NVIDIACUDA SDK\common. The release and emurelease versions of these samples link tocutil32.lib (or cutil64.lib) and dynamically load cutil32.dll (orcutil64.dll). The debug and emudebug versions of these samples link toCompiling a CUDA Programcutil32D.lib and dynamically load cutil32D (or their 64-bit equivalents on 64-bitversions of Windows).To build the Win32 release and/or debug configurations of the cutil library, usethe solution files located in NVIDIA CUDA SDK\common. The output of thecompilation process should be placed in NVIDIA CUDA SDK\common\lib:cutil32.lib and cutil32D.lib (or cutil64.lib and cutil64D.lib) are the release and debug import libraries.cutil32.dll and cutil32D.dll (or cutil64.dll and cutil64D.dll) are the release and debug dynamic-link libraries, which also are copied to NVIDIA CUDASDK\bin\win32\[release|emurelease] and NVIDIA CUDASDK\bin\win32\[debug|emudebug] respectively. (Substitute \win64 for\win32 on 64-bit Windows.)What’s Next?Now that you have CUDA-capable hardware and the software installed, you canexamine and enjoy the numerous included programs. To begin using CUDA toaccelerate the performance of your own applications, consult the CUDAProgramming Guide, located in the directory where you installed the CUDA Toolkit(by default, C:\CUDA\doc).For tech support on programming questions, consult and participate in the bulletinboard and mailing list at /index.php?showforum=71.NoticeALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATELY, “MATERIALS”) ARE BEING PROVIDED “AS IS.” NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE.Information furnished is believed to be accurate and reliable. However, NVIDIA Corporation assumes no responsibility for the consequences of use of such information or for any infringement of patents or other rights of third parties that may result from its use. No license is granted by implication or otherwise under any patent or patent rights of NVIDIA Corporation. Specifications mentioned in this publication are subject to change without notice. This publication supersedes and replaces all information previously supplied. NVIDIA Corporation products are not authorized for use as critical components in life support devices or systems without express written approval of NVIDIA Corporation.TrademarksNVIDIA, the NVIDIA logo, CUDA, Forceware, GeForce, and Quadro are trademarks or registered trademarks of NVIDIA Corporation. Other company and product names may be trademarks of the respective companies with which they are associated.Copyright© 2008 NVIDIA Corporation. All rights reserved.。

unexpected error from cudagetdevicecount

unexpected error from cudagetdevicecount（原创版）目录1.概述：CUDA 设备数量获取错误2.原因分析：可能的原因3.解决方案：如何解决这个问题4.总结：对问题的总结和建议正文一、概述：CUDA 设备数量获取错误CUDA 是 NVIDIA 推出的通用并行计算架构，它能够让开发者利用NVIDIA 的 GPU 进行高性能计算。

然而，在使用 CUDA 进行编程时，可能会遇到从 cudagetdevicecount 获取设备数量时出现意外错误的情况。

本文将分析可能的原因，并提供相应的解决方案。

二、原因分析：可能的原因1.驱动程序问题：错误的设备数量可能是由于显卡驱动程序不正确安装或版本不兼容导致的。

请确保您的系统上安装了最新、正确的 NVIDIA 驱动程序。

2.CUDA 版本问题：错误的设备数量可能是由于 CUDA 工具包版本不兼容导致的。

请确保您的系统上安装了与驱动程序兼容的 CUDA 版本。

3.其他软件冲突：某些第三方软件可能会影响 CUDA 设备数量的获取。

请检查系统中是否存在与 CUDA 冲突的软件，如有，请尝试卸载或更新。

三、解决方案：如何解决这个问题1.更新驱动程序：请访问 NVIDIA 官方网站，下载并安装最新版的显卡驱动程序。

2.更新 CUDA 版本：请访问 NVIDIA 官方网站，下载并安装与驱动程序兼容的 CUDA 版本。

3.检查第三方软件：请检查系统中是否存在与 CUDA 冲突的软件，如有，请尝试卸载或更新。

4.重启计算机：有时候，重启计算机可以解决一些奇怪的问题。

请尝试重启计算机，然后再次运行程序。

四、总结：对问题的总结和建议遇到 CUDA 设备数量获取错误时，需要耐心排查可能的原因。

从驱动程序、CUDA 版本和第三方软件等方面进行分析，找到问题所在，并进行相应的解决。