C:\Users\CD>nvcc -V nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2020 NVIDIA Corporation Built on Tue_Sep_15_19:12:04_Pacific_Daylight_Time_2020 Cuda compilation tools, release 11.1, V11.1.74 Build cuda_11.1.relgpu_drvr455TC455_06.29069683_0

时间: 2025-04-03 12:17:23 AIGC 浏览: 59
<think>好的,我需要帮助用户检查他们的CUDA编译器驱动版本,特别是11.1版本的配置。首先,根据用户提供的引用,他们提到在不同操作系统下如何检查CUDA版本。 用户的问题是关于如何验证CUDA 11.1的环境配置。根据引用[1]和[2],在Windows和Linux系统中,使用`nvcc -V`或`nvcc --version`命令可以查看版本信息。我需要确认这些命令是否适用于CUDA 11.1。另外,用户提供的引用[3]和[4]涉及到Ubuntu系统的安装和环境变量配置,这可能与路径设置有关,如果命令不生效,可能需要检查环境变量是否正确设置。 接下来,我需要组织回答的结构。首先给出检查版本的命令,然后针对不同系统可能出现的问题提供解决方案,比如环境变量配置。同时,用户可能遇到版本显示不一致的情况,比如驱动版本和运行时版本不同,需要解释如何通过其他命令如`nvidia-smi`来查看驱动版本,并说明两者的区别。 还需要考虑用户是否正确安装了CUDA 11.1,可能存在的常见问题,比如安装包是否正确下载,路径是否添加等。例如,引用[4]中提到的导出PATH和LD_LIBRARY_PATH,如果用户没有正确设置这些变量,可能导致nvcc命令无法找到,需要指导他们如何修改配置文件。 最后,生成相关问题时要覆盖安装、验证、配置以及常见错误处理等方面,帮助用户进一步解决问题。需要确保所有步骤清晰,语言简洁,符合用户的实际操作流程。</think>要检查CUDA编译器驱动版本并验证CUDA 11.1的环境配置,请按以下步骤操作: ### 1. 检查NVCC编译器版本 在命令行中执行: ```bash nvcc --version ``` 或 ```bash nvcc -V ``` 若环境配置正确,将显示类似以下信息: ``` nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2020 NVIDIA Corporation Built on Mon_Oct_12_20:54:10_PDT_2020 Cuda compilation tools, release 11.1, V11.1.105 ``` *若显示`nvcc: command not found`,说明环境变量未正确配置[^4]* --- ### 2. CUDA 11.1环境配置要点 #### Windows系统 - 安装时选择"自定义安装",确保勾选`CUDA Development Tools` - 验证安装后需重启系统[^2] #### Linux系统(含WSL) 1. 下载对应系统的`runfile`安装包[^3] 2. 安装完成后需添加环境变量: ```bash # 编辑配置文件(如~/.bashrc) export PATH=/usr/local/cuda-11.1/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda-11.1/lib64:$LD_LIBRARY_PATH ``` 3. 执行`source ~/.bashrc`使配置生效[^4] --- ### 3. 版本一致性验证 同时执行以下命令检查驱动兼容性: ```bash nvidia-smi ``` 输出中的`CUDA Version`表示驱动支持的最高CUDA版本,应≥11.1。例如: ``` +-----------------------------------------------------------------------------+ | NVIDIA-SMI 450.142.00 Driver Version: 450.142.00 CUDA Version: 11.0 | |-------------------------------+----------------------+----------------------+ ``` *注意:此处显示的是驱动支持的CUDA最高版本,非实际安装的CUDA版本* ---
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PowerShell 7 环境已加载 (版本: 7.5.2) PS C:\Users\Administrator\Desktop> # 查找 NVCC 编译器路径 PS C:\Users\Administrator\Desktop> $possiblePaths = @( >> "E:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0\bin\nvcc.exe", >> "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\*\bin\nvcc.exe" >> ) PS C:\Users\Administrator\Desktop> PS C:\Users\Administrator\Desktop> Write-Host "=== NVCC 编译器位置 ===" -ForegroundColor Cyan === NVCC 编译器位置 === PS C:\Users\Administrator\Desktop> foreach ($path in $possiblePaths) { >> $found = Get-Item $path -ErrorAction SilentlyContinue >> if ($found) { >> Write-Host "✅ 找到 NVCC: $($found.FullName)" -ForegroundColor Green >> # 测试 NVCC 版本 >> & $found.FullName --version | Select-Object -First 2 >> break >> } >> } ✅ 找到 NVCC: E:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0\bin\nvcc.exe nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2025 NVIDIA Corporation PS C:\Users\Administrator\Desktop> PS C:\Users\Administrator\Desktop> if (-not $found) { >> Write-Host "❌ 未找到 NVCC 编译器" -ForegroundColor Red >> Write-Host "请检查 CUDA Toolkit 是否安装正确" -ForegroundColor Yellow >> } PS C:\Users\Administrator\Desktop> # 验证 Visual Studio 编译器 PS C:\Users\Administrator\Desktop> cl.exe /? cl.exe: The term 'cl.exe' is not recognized as a name of a cmdlet, function, script file, or executable program. Check the spelling of the name, or if a path was included, verify that the path is correct and try again. PS C:\Users\Administrator\Desktop> # 应显示 Microsoft C/C++ 编译器信息 PS C:\Users\Administrator\Desktop> PS C:\Users\Administrator\Desktop> # 验证 CUDA 环境 PS C:\Users\Administrator\Desktop> nvcc.exe --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2025 NVIDIA Corporation Built on Wed_Jul_16_20:06:48_Pacific_Daylight_Time_2025 Cuda compilation tools, release 13.0, V13.0.48 Build cuda_13.0.r13.0/compiler.36260728_0 PS C:\Users\Administrator\Desktop> # 应显示 CUDA 编译器版本信息 PS C:\Users\Administrator\Desktop> PS C:\Users\Administrator\Desktop> # 验证环境变量 PS C:\Users\Administrator\Desktop> Write-Host "关键环境变量:" 关键环境变量: PS C:\Users\Administrator\Desktop> Write-Host "PATH: $env:PATH" PATH: C:\Program Files\PowerShell\7;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\libnvvp;C:\Miniconda3\condabin;C:\Miniconda3\Scripts;E:\PyTorch_Build\pytorch\pytorch_env\Scripts;C:\Program Files\PowerShell\7;E:\PyTorch_Build\pytorch\pytorch_env\Scripts;C:\Program Files\PowerShell\7;E:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0\bin\x64;E:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0\bin;E:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0\bin\x64;;E:\PyTorch_Build\pytorch\build\lib.win-amd64-cpython-310\torch\lib;E:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0\bin;E:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0\bin;C:\Users\Administrator\AppData\Local\Microsoft\dotnet;C:\Users\Administrator\AppData\Local\Microsoft\dotnet;C:\Users\Administrator\AppData\Local\Microsoft\dotnet\;C:\Program Files\dotnet;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0\;C:\WINDOWS\System32\OpenSSH\;E:\Python310;C:\Program Files\dotnet\;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\Users\Administrator\.dotnet\tools;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\Users\Administrator\.dotnet\tools;E:\Python310\Scripts;E:\Python310\Scripts;C:\Program Files\PowerShell\7\;E:\Program Files\Microsoft VS Code\bin;E:\Program Files\Git\cmd;C:\Program Files\NVIDIA Corporation\Nsight Compute 2025.3.0\;E:\Program Files\CMake\bin;C:\Program Files\Microsoft SQL Server\150\Tools\Binn\;C:\Program Files\Microsoft SQL Server\Client SDK\ODBC\170\Tools\Binn\;C:\Program Files (x86)\Incredibuild;E:\PyTorch_Build\pytorch\build\lib.win-amd64-cpython-310\torch\lib;E:\Program Files (x86)\Windows Kits\10\Windows Performance Toolkit\;C:\ProgramData\chocolatey\bin;E:\Program Files\Rust\.cargo\bin;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\Users\Administrator\.dotnet\tools;C:\Users\Administrator\miniconda3\Scripts;E:\Program Files\Rust\.cargo\bin;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\Users\Administrator\.dotnet\tools;C:\Users\Administrator\miniconda3\Scripts;E:\Program Files\Rust\.cargo\bin;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\Users\Administrator\.dotnet\tools;C:\ProgramData\mingw64\mingw64\bin PS C:\Users\Administrator\Desktop> Write-Host "CUDA_PATH: $env:CUDA_PATH" CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1 PS C:\Users\Administrator\Desktop> Write-Host "VSCMD_ARG_TGT_ARCH: $env:VSCMD_ARG_TGT_ARCH" VSCMD_ARG_TGT_ARCH: PS C:\Users\Administrator\Desktop> cd E:\PyTorch_Build\pytorch PS E:\PyTorch_Build\pytorch> python -m venv rtx5070_env PS E:\PyTorch_Build\pytorch> .\rtx5070_env\Scripts\activate (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 查找 NVCC 编译器路径 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $possiblePaths = @( >> "E:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0\bin\nvcc.exe", >> "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\*\bin\nvcc.exe" >> ) (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> Write-Host "=== NVCC 编译器位置 ===" -ForegroundColor Cyan === NVCC 编译器位置 === (rtx5070_env) PS E:\PyTorch_Build\pytorch> foreach ($path in $possiblePaths) { >> $found = Get-Item $path -ErrorAction SilentlyContinue >> if ($found) { >> Write-Host "✅ 找到 NVCC: $($found.FullName)" -ForegroundColor Green >> # 测试 NVCC 版本 >> & $found.FullName --version | Select-Object -First 2 >> break >> } >> } ✅ 找到 NVCC: E:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0\bin\nvcc.exe nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2025 NVIDIA Corporation (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> if (-not $found) { >> Write-Host "❌ 未找到 NVCC 编译器" -ForegroundColor Red >> Write-Host "请检查 CUDA Toolkit 是否安装正确" -ForegroundColor Yellow >> } (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 验证 Visual Studio 编译器 (rtx5070_env) PS E:\PyTorch_Build\pytorch> cl.exe /? cl.exe: The term 'cl.exe' is not recognized as a name of a cmdlet, function, script file, or executable program. Check the spelling of the name, or if a path was included, verify that the path is correct and try again. (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 应显示 Microsoft C/C++ 编译器信息 (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 验证 CUDA 环境 (rtx5070_env) PS E:\PyTorch_Build\pytorch> nvcc.exe --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2025 NVIDIA Corporation Built on Wed_Jul_16_20:06:48_Pacific_Daylight_Time_2025 Cuda compilation tools, release 13.0, V13.0.48 Build cuda_13.0.r13.0/compiler.36260728_0 (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 应显示 CUDA 编译器版本信息 (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 验证环境变量 (rtx5070_env) PS E:\PyTorch_Build\pytorch> Write-Host "关键环境变量:" 关键环境变量: (rtx5070_env) PS E:\PyTorch_Build\pytorch> Write-Host "PATH: $env:PATH" PATH: E:\PyTorch_Build\pytorch\rtx5070_env\Scripts;C:\Program Files\PowerShell\7;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\libnvvp;C:\Miniconda3\condabin;C:\Miniconda3\Scripts;E:\PyTorch_Build\pytorch\pytorch_env\Scripts;C:\Program Files\PowerShell\7;E:\PyTorch_Build\pytorch\pytorch_env\Scripts;C:\Program Files\PowerShell\7;E:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0\bin\x64;E:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0\bin;E:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0\bin\x64;;E:\PyTorch_Build\pytorch\build\lib.win-amd64-cpython-310\torch\lib;E:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0\bin;E:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0\bin;C:\Users\Administrator\AppData\Local\Microsoft\dotnet;C:\Users\Administrator\AppData\Local\Microsoft\dotnet;C:\Users\Administrator\AppData\Local\Microsoft\dotnet\;C:\Program Files\dotnet;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0\;C:\WINDOWS\System32\OpenSSH\;E:\Python310;C:\Program Files\dotnet\;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\Users\Administrator\.dotnet\tools;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\Users\Administrator\.dotnet\tools;E:\Python310\Scripts;E:\Python310\Scripts;C:\Program Files\PowerShell\7\;E:\Program Files\Microsoft VS Code\bin;E:\Program Files\Git\cmd;C:\Program Files\NVIDIA Corporation\Nsight Compute 2025.3.0\;E:\Program Files\CMake\bin;C:\Program Files\Microsoft SQL Server\150\Tools\Binn\;C:\Program Files\Microsoft SQL Server\Client SDK\ODBC\170\Tools\Binn\;C:\Program Files (x86)\Incredibuild;E:\PyTorch_Build\pytorch\build\lib.win-amd64-cpython-310\torch\lib;E:\Program Files (x86)\Windows Kits\10\Windows Performance Toolkit\;C:\ProgramData\chocolatey\bin;E:\Program Files\Rust\.cargo\bin;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\Users\Administrator\.dotnet\tools;C:\Users\Administrator\miniconda3\Scripts;E:\Program Files\Rust\.cargo\bin;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\Users\Administrator\.dotnet\tools;C:\Users\Administrator\miniconda3\Scripts;E:\Program Files\Rust\.cargo\bin;C:\Users\Administrator\AppData\Local\Microsoft\WindowsApps;C:\Users\Administrator\.dotnet\tools;C:\ProgramData\mingw64\mingw64\bin (rtx5070_env) PS E:\PyTorch_Build\pytorch> Write-Host "CUDA_PATH: $env:CUDA_PATH" CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1 (rtx5070_env) PS E:\PyTorch_Build\pytorch> Write-Host "VSCMD_ARG_TGT_ARCH: $env:VSCMD_ARG_TGT_ARCH" VSCMD_ARG_TGT_ARCH: (rtx5070_env) PS E:\PyTorch_Build\pytorch> 你看这是找到了吗?E:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0\bin里面有“crt x64 nvcc device query.exe bin2c.exe compute-sanitizer.bat ctadvisor.exe cu++filt.exe cudafe++.exe cuobjdump.exe fatbinary.exe l nvcc.exe Envcc.profile nvdisasm.exe nvlink.exe nvprune.exe ptxas.exe”

PowerShell 7 环境已加载 (版本: 7.5.2) PowerShell 7 环境已加载 (版本: 7.5.2) PS C:\Users\Administrator\Desktop> cd E:\PyTorch_Build\pytorch PS E:\PyTorch_Build\pytorch> python -m venv rtx5070_env PS E:\PyTorch_Build\pytorch> .\rtx5070_env\Scripts\activate (rtx5070_env) PS E:\PyTorch_Build\pytorch> python fixed_diagnostic_test.py ================================================== CUDA Toolkit 验证: ✅ NVCC 版本: nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2025 NVIDIA Corporation Built on Wed_Jul_16_20:06:48_Pacific_Daylight_Time_2025 Cuda compilation tools, release 13.0, V13.0.48 Build cuda_13.0.r13.0/compiler.36260728_0 ✅ NVIDIA-SMI 输出: Mon Sep 1 21:01:44 2025 +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 580.97 Driver Version: 580.97 CUDA Version: 13.0 | +-----------------------------------------+------------------------+----------------------+ | GPU Name Driver-Model | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+========================+======================| | 0 NVIDIA GeForce RTX 5070 WDDM | 00000000:01:00.0 On | N/A | | 0% 35C P8 9W / 250W | 1266MiB / 12227MiB | 0% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ +-----------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=========================================================================================| | 0 N/A N/A 1288 C+G ...les\Tencent\Weixin\Weixin.exe N/A | | 0 N/A N/A 1776 C+G C:\Windows\System32\dwm.exe N/A | | 0 N/A N/A 2272 C+G ...t\Edge\Application\msedge.exe N/A | | 0 N/A N/A 3268 C+G ...em32\ApplicationFrameHost.exe N/A | | 0 N/A N/A 5188 C+G ...yb3d8bbwe\WindowsTerminal.exe N/A | | 0 N/A N/A 7860 C+G C:\Windows\explorer.exe N/A | | 0 N/A N/A 8004 C+G ...indows\System32\ShellHost.exe N/A | | 0 N/A N/A 8852 C+G ..._cw5n1h2txyewy\SearchHost.exe N/A | | 0 N/A N/A 8876 C+G ...y\StartMenuExperienceHost.exe N/A | | 0 N/A N/A 10540 C+G ...0.3405.125\msedgewebview2.exe N/A | | 0 N/A N/A 12380 C+G ...5n1h2txyewy\TextInputHost.exe N/A | | 0 N/A N/A 15340 C+G ...acted\runtime\WeChatAppEx.exe N/A | | 0 N/A N/A 18600 C+G ...ntrolPanel\SystemSettings.exe N/A | +-----------------------------------------------------------------------------------------+ ✅ CUDA_PATH: E:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0 ================================================== PyTorch 安装诊断: ✅ torch 模块已导入: E:\PyTorch_Build\pytorch\rtx5070_env\lib\site-packages\torch\__init__.py PyTorch 版本: 2.6.0.dev20241112+cu121 编译 CUDA 版本: 12.1 cuDNN 版本: 90100 CUDA 可用: True 检测到 GPU 数量: 1 E:\PyTorch_Build\pytorch\rtx5070_env\lib\site-packages\torch\cuda\__init__.py:235: UserWarning: NVIDIA GeForce RTX 5070 with CUDA capability sm_120 is not compatible with the current PyTorch installation. The current PyTorch install supports CUDA capabilities sm_50 sm_60 sm_61 sm_70 sm_75 sm_80 sm_86 sm_90. If you want to use the NVIDIA GeForce RTX 5070 GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/ warnings.warn( GPU 0: NVIDIA GeForce RTX 5070, 计算能力: 12.0 ================================================== GPU 计算能力测试: ❌ 计算测试失败: CUDA error: no kernel image is available for execution on the device CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1 Compile with TORCH_USE_CUDA_DSA to enable device-side assertions. 按 Enter 键退出... (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 卸载旧版本 (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip uninstall -y torch torchvision torchaudio Found existing installation: torch 2.6.0.dev20241112+cu121 Uninstalling torch-2.6.0.dev20241112+cu121: Successfully uninstalled torch-2.6.0.dev20241112+cu121 Found existing installation: torchvision 0.20.0.dev20241112+cu121 Uninstalling torchvision-0.20.0.dev20241112+cu121: Successfully uninstalled torchvision-0.20.0.dev20241112+cu121 Found existing installation: torchaudio 2.5.0.dev20241112+cu121 Uninstalling torchaudio-2.5.0.dev20241112+cu121: Successfully uninstalled torchaudio-2.5.0.dev20241112+cu121 (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 安装支持 RTX 5070 的预编译版本 (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip install --pre torch torchvision torchaudio >> --index-url https://download.pytorch.org/whl/nightly/cu121 >> --no-deps Looking in indexes: https://download.pytorch.org/whl/nightly/cu121 Collecting torch Using cached https://download.pytorch.org/whl/nightly/cu121/torch-2.6.0.dev20241112%2Bcu121-cp310-cp310-win_amd64.whl (2456.2 MB) Collecting torchvision Using cached https://download.pytorch.org/whl/nightly/cu121/torchvision-0.20.0.dev20241112%2Bcu121-cp310-cp310-win_amd64.whl (6.2 MB) Collecting torchaudio Using cached https://download.pytorch.org/whl/nightly/cu121/torchaudio-2.5.0.dev20241112%2Bcu121-cp310-cp310-win_amd64.whl (4.2 MB) Installing collected packages: torchaudio, torchvision, torch Successfully installed torch-2.6.0.dev20241112+cu121 torchaudio-2.5.0.dev20241112+cu121 torchvision-0.20.0.dev20241112+cu121 (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 安装运行时依赖 (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip install pyyaml numpy typing_extensions mkl mkl-include intel-openmp Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Requirement already satisfied: pyyaml in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (6.0.2) Requirement already satisfied: numpy in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (2.2.6) Requirement already satisfied: typing_extensions in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (4.15.0) Requirement already satisfied: mkl in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (2025.2.0) Requirement already satisfied: mkl-include in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (2025.2.0) Requirement already satisfied: intel-openmp in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (2025.2.1) Requirement already satisfied: tbb==2022.* in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (from mkl) (2022.2.0) Requirement already satisfied: intel-cmplr-lib-ur==2025.2.1 in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (from intel-openmp) (2025.2.1) Requirement already satisfied: umf==0.11.* in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (from intel-cmplr-lib-ur==2025.2.1->intel-openmp) (0.11.0) Requirement already satisfied: tcmlib==1.* in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (from tbb==2022.*->mkl) (1.4.0) (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 修复 cuDNN 路径配置 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:CUDA_PATH = "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1" (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:PATH = "$env:CUDA_PATH\bin;" + $env:PATH (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 执行诊断测试 (rtx5070_env) PS E:\PyTorch_Build\pytorch> python fixed_diagnostic_test.py ================================================== CUDA Toolkit 验证: ✅ NVCC 版本: nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2025 NVIDIA Corporation Built on Wed_Jul_16_20:06:48_Pacific_Daylight_Time_2025 Cuda compilation tools, release 13.0, V13.0.48 Build cuda_13.0.r13.0/compiler.36260728_0 ✅ NVIDIA-SMI 输出: Mon Sep 1 21:03:21 2025 +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 580.97 Driver Version: 580.97 CUDA Version: 13.0 | +-----------------------------------------+------------------------+----------------------+ | GPU Name Driver-Model | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+========================+======================| | 0 NVIDIA GeForce RTX 5070 WDDM | 00000000:01:00.0 On | N/A | | 0% 35C P0 10W / 250W | 1248MiB / 12227MiB | 0% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ +-----------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=========================================================================================| | 0 N/A N/A 1288 C+G ...les\Tencent\Weixin\Weixin.exe N/A | | 0 N/A N/A 1776 C+G C:\Windows\System32\dwm.exe N/A | | 0 N/A N/A 2272 C+G ...t\Edge\Application\msedge.exe N/A | | 0 N/A N/A 3268 C+G ...em32\ApplicationFrameHost.exe N/A | | 0 N/A N/A 5188 C+G ...yb3d8bbwe\WindowsTerminal.exe N/A | | 0 N/A N/A 7860 C+G C:\Windows\explorer.exe N/A | | 0 N/A N/A 8004 C+G ...indows\System32\ShellHost.exe N/A | | 0 N/A N/A 8852 C+G ..._cw5n1h2txyewy\SearchHost.exe N/A | | 0 N/A N/A 8876 C+G ...y\StartMenuExperienceHost.exe N/A | | 0 N/A N/A 10540 C+G ...0.3405.125\msedgewebview2.exe N/A | | 0 N/A N/A 12380 C+G ...5n1h2txyewy\TextInputHost.exe N/A | | 0 N/A N/A 15340 C+G ...acted\runtime\WeChatAppEx.exe N/A | | 0 N/A N/A 18600 C+G ...ntrolPanel\SystemSettings.exe N/A | +-----------------------------------------------------------------------------------------+ ✅ CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1 ================================================== PyTorch 安装诊断: ✅ torch 模块已导入: E:\PyTorch_Build\pytorch\rtx5070_env\lib\site-packages\torch\__init__.py PyTorch 版本: 2.6.0.dev20241112+cu121 编译 CUDA 版本: 12.1 cuDNN 版本: 90100 CUDA 可用: True 检测到 GPU 数量: 1 E:\PyTorch_Build\pytorch\rtx5070_env\lib\site-packages\torch\cuda\__init__.py:235: UserWarning: NVIDIA GeForce RTX 5070 with CUDA capability sm_120 is not compatible with the current PyTorch installation. The current PyTorch install supports CUDA capabilities sm_50 sm_60 sm_61 sm_70 sm_75 sm_80 sm_86 sm_90. If you want to use the NVIDIA GeForce RTX 5070 GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/ warnings.warn( GPU 0: NVIDIA GeForce RTX 5070, 计算能力: 12.0 ================================================== GPU 计算能力测试: ❌ 计算测试失败: CUDA error: no kernel image is available for execution on the device CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1 Compile with TORCH_USE_CUDA_DSA to enable device-side assertions. 按 Enter 键退出... (rtx5070_env) PS E:\PyTorch_Build\pytorch>

PowerShell 7 环境已加载 (版本: 7.5.2) PowerShell 7 环境已加载 (版本: 7.5.2) PS C:\Users\Administrator\Desktop> cd E:\PyTorch_Build\pytorch PS E:\PyTorch_Build\pytorch> .\pytorch_env\Scripts\activate (pytorch_env) PS E:\PyTorch_Build\pytorch> # 退出虚拟环境 (pytorch_env) PS E:\PyTorch_Build\pytorch> deactivate PS E:\PyTorch_Build\pytorch> PS E:\PyTorch_Build\pytorch> # 删除旧环境 PS E:\PyTorch_Build\pytorch> Remove-Item -Recurse -Force .\pytorch_env PS E:\PyTorch_Build\pytorch> Remove-Item -Recurse -Force .\cuda_env PS E:\PyTorch_Build\pytorch> PS E:\PyTorch_Build\pytorch> # 创建新虚拟环境 PS E:\PyTorch_Build\pytorch> python -m venv rtx5070_env PS E:\PyTorch_Build\pytorch> .\rtx5070_env\Scripts\activate (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 安装基础编译工具 (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip install -U pip setuptools wheel ninja cmake Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Requirement already satisfied: pip in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (22.3.1) Collecting pip Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b7/3f/945ef7ab14dc4f9d7f40288d2df998d1837ee0888ec3659c813487572faa/pip-25.2-py3-none-any.whl (1.8 MB) Requirement already satisfied: setuptools in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (65.5.0) Collecting setuptools Using cached https://pypi.tuna.tsinghua.edu.cn/packages/a3/dc/17031897dae0efacfea57dfd3a82fdd2a2aeb58e0ff71b77b87e44edc772/setuptools-80.9.0-py3-none-any.whl (1.2 MB) Collecting wheel Using cached https://pypi.tuna.tsinghua.edu.cn/packages/0b/2c/87f3254fd8ffd29e4c02732eee68a83a1d3c346ae39bc6822dcbcb697f2b/wheel-0.45.1-py3-none-any.whl (72 kB) Collecting ninja Using cached https://pypi.tuna.tsinghua.edu.cn/packages/29/45/c0adfbfb0b5895aa18cec400c535b4f7ff3e52536e0403602fc1a23f7de9/ninja-1.13.0-py3-none-win_amd64.whl (309 kB) Collecting cmake Using cached https://pypi.tuna.tsinghua.edu.cn/packages/7c/d0/73cae88d8c25973f2465d5a4457264f95617c16ad321824ed4c243734511/cmake-4.1.0-py3-none-win_amd64.whl (37.6 MB) ERROR: To modify pip, please run the following command: E:\PyTorch_Build\pytorch\rtx5070_env\Scripts\python.exe -m pip install -U pip setuptools wheel ninja cmake [notice] A new release of pip available: 22.3.1 -> 25.2 [notice] To update, run: python.exe -m pip install --upgrade pip (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 验证 CUDA 安装 (rtx5070_env) PS E:\PyTorch_Build\pytorch> nvcc --version # 应显示 CUDA 12.x nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2025 NVIDIA Corporation Built on Wed_Jul_16_20:06:48_Pacific_Daylight_Time_2025 Cuda compilation tools, release 13.0, V13.0.48 Build cuda_13.0.r13.0/compiler.36260728_0 (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 正确更新 pip 和工具链 (rtx5070_env) PS E:\PyTorch_Build\pytorch> python -m pip install -U pip setuptools wheel ninja cmake Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Requirement already satisfied: pip in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (22.3.1) Collecting pip Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b7/3f/945ef7ab14dc4f9d7f40288d2df998d1837ee0888ec3659c813487572faa/pip-25.2-py3-none-any.whl (1.8 MB) Requirement already satisfied: setuptools in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (65.5.0) Collecting setuptools Using cached https://pypi.tuna.tsinghua.edu.cn/packages/a3/dc/17031897dae0efacfea57dfd3a82fdd2a2aeb58e0ff71b77b87e44edc772/setuptools-80.9.0-py3-none-any.whl (1.2 MB) Collecting wheel Using cached https://pypi.tuna.tsinghua.edu.cn/packages/0b/2c/87f3254fd8ffd29e4c02732eee68a83a1d3c346ae39bc6822dcbcb697f2b/wheel-0.45.1-py3-none-any.whl (72 kB) Collecting ninja Using cached https://pypi.tuna.tsinghua.edu.cn/packages/29/45/c0adfbfb0b5895aa18cec400c535b4f7ff3e52536e0403602fc1a23f7de9/ninja-1.13.0-py3-none-win_amd64.whl (309 kB) Collecting cmake Using cached https://pypi.tuna.tsinghua.edu.cn/packages/7c/d0/73cae88d8c25973f2465d5a4457264f95617c16ad321824ed4c243734511/cmake-4.1.0-py3-none-win_amd64.whl (37.6 MB) Installing collected packages: wheel, setuptools, pip, ninja, cmake Attempting uninstall: setuptools Found existing installation: setuptools 65.5.0 Uninstalling setuptools-65.5.0: Successfully uninstalled setuptools-65.5.0 Attempting uninstall: pip Found existing installation: pip 22.3.1 Uninstalling pip-22.3.1: Successfully uninstalled pip-22.3.1 Successfully installed cmake-4.1.0 ninja-1.13.0 pip-25.2 setuptools-80.9.0 wheel-0.45.1 (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 验证版本 (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip --version # 应显示 25.2+ pip 25.2 from E:\PyTorch_Build\pytorch\rtx5070_env\lib\site-packages\pip (python 3.10) (rtx5070_env) PS E:\PyTorch_Build\pytorch> cmake --version # 应显示 4.1.0+ cmake version 4.1.0 CMake suite maintained and supported by Kitware (kitware.com/cmake). (rtx5070_env) PS E:\PyTorch_Build\pytorch> ninja --version # 应显示 1.13.0+ 1.13.0.git.kitware.jobserver-pipe-1 (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 设置 CUDA 12.1 环境变量 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:CUDA_PATH = "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1" (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:PATH = "$env:CUDA_PATH\bin;" + $env:PATH (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 验证 CUDA 版本 (rtx5070_env) PS E:\PyTorch_Build\pytorch> nvcc --version # 应显示 release 12.1 nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2025 NVIDIA Corporation Built on Wed_Jul_16_20:06:48_Pacific_Daylight_Time_2025 Cuda compilation tools, release 13.0, V13.0.48 Build cuda_13.0.r13.0/compiler.36260728_0 (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 设置 cuDNN 路径(根据实际安装位置) (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:CUDNN_INCLUDE_DIR = "$env:CUDA_PATH\include" (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:CUDNN_LIBRARY = "$env:CUDA_PATH\lib\x64\cudnn.lib" (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 安装必要依赖 (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip install pyyaml numpy typing_extensions Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Collecting pyyaml Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b5/84/0fa4b06f6d6c958d207620fc60005e241ecedceee58931bb20138e1e5776/PyYAML-6.0.2-cp310-cp310-win_amd64.whl (161 kB) Collecting numpy Using cached https://pypi.tuna.tsinghua.edu.cn/packages/a3/dd/4b822569d6b96c39d1215dbae0582fd99954dcbcf0c1a13c61783feaca3f/numpy-2.2.6-cp310-cp310-win_amd64.whl (12.9 MB) Collecting typing_extensions Using cached https://pypi.tuna.tsinghua.edu.cn/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl (44 kB) Installing collected packages: typing_extensions, pyyaml, numpy Successfully installed numpy-2.2.6 pyyaml-6.0.2 typing_extensions-4.15.0 (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 安装 GPU 相关依赖 (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip install mkl mkl-include intel-openmp Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Collecting mkl Using cached https://pypi.tuna.tsinghua.edu.cn/packages/91/ae/025174ee141432b974f97ecd2aea529a3bdb547392bde3dd55ce48fe7827/mkl-2025.2.0-py2.py3-none-win_amd64.whl (153.6 MB) Collecting mkl-include Using cached https://pypi.tuna.tsinghua.edu.cn/packages/06/87/3eee37bf95c6b820b6394ad98e50132798514ecda1b2584c71c2c96b973c/mkl_include-2025.2.0-py2.py3-none-win_amd64.whl (1.3 MB) Collecting intel-openmp Using cached https://pypi.tuna.tsinghua.edu.cn/packages/89/ed/13fed53fcc7ea17ff84095e89e63418df91d4eeefdc74454243d529bf5a3/intel_openmp-2025.2.1-py2.py3-none-win_amd64.whl (34.0 MB) Collecting tbb==2022.* (from mkl) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/4e/d2/01e2a93f9c644585088188840bf453f23ed1a2838ec51d5ba1ada1ebca71/tbb-2022.2.0-py3-none-win_amd64.whl (420 kB) Collecting intel-cmplr-lib-ur==2025.2.1 (from intel-openmp) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/a8/70/938e81f58886fd4e114d5a5480d98c1396e73e40b7650f566ad0c4395311/intel_cmplr_lib_ur-2025.2.1-py2.py3-none-win_amd64.whl (1.2 MB) Collecting umf==0.11.* (from intel-cmplr-lib-ur==2025.2.1->intel-openmp) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/33/a0/c8d755f08f50ddd99cb4a29a7e950ced7a0903cb72253e57059063609103/umf-0.11.0-py2.py3-none-win_amd64.whl (231 kB) Collecting tcmlib==1.* (from tbb==2022.*->mkl) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/91/7b/e30c461a27b97e0090e4db822eeb1d37b310863241f8c3ee56f68df3e76e/tcmlib-1.4.0-py2.py3-none-win_amd64.whl (370 kB) Installing collected packages: tcmlib, mkl-include, umf, tbb, intel-cmplr-lib-ur, intel-openmp, mkl Successfully installed intel-cmplr-lib-ur-2025.2.1 intel-openmp-2025.2.1 mkl-2025.2.0 mkl-include-2025.2.0 tbb-2022.2.0 tcmlib-1.4.0 umf-0.11.0 (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 安装必要依赖 (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip install pyyaml numpy typing_extensions Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Requirement already satisfied: pyyaml in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (6.0.2) Requirement already satisfied: numpy in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (2.2.6) Requirement already satisfied: typing_extensions in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (4.15.0) (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 安装 GPU 相关依赖 (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip install mkl mkl-include intel-openmp Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Requirement already satisfied: mkl in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (2025.2.0) Requirement already satisfied: mkl-include in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (2025.2.0) Requirement already satisfied: intel-openmp in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (2025.2.1) Requirement already satisfied: tbb==2022.* in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (from mkl) (2022.2.0) Requirement already satisfied: intel-cmplr-lib-ur==2025.2.1 in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (from intel-openmp) (2025.2.1) Requirement already satisfied: umf==0.11.* in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (from intel-cmplr-lib-ur==2025.2.1->intel-openmp) (0.11.0) Requirement already satisfied: tcmlib==1.* in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (from tbb==2022.*->mkl) (1.4.0) (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 设置编译参数 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:USE_CUDA=1 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:USE_CUDNN=1 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:CMAKE_GENERATOR="Ninja" (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:MAX_JOBS=8 # 根据 CPU 核心数设置 (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 运行编译 (rtx5070_env) PS E:\PyTorch_Build\pytorch> python setup.py install >> --cmake >> --cmake-only >> --cmake-generator="Ninja" >> --verbose >> -DCMAKE_CUDA_COMPILER="${env:CUDA_PATH}\bin\nvcc.exe" >> -DCUDNN_INCLUDE_DIR="${env:CUDNN_INCLUDE_DIR}" >> -DCUDNN_LIBRARY="${env:CUDNN_LIBRARY}" >> -DTORCH_CUDA_ARCH_LIST="8.9;9.0;12.0" Building wheel torch-2.9.0a0+git2d31c3d option --cmake-generator not recognized (rtx5070_env) PS E:\PyTorch_Build\pytorch> python rtx5070_test.py ============================================================ Traceback (most recent call last): File "E:\PyTorch_Build\pytorch\rtx5070_test.py", line 39, in <module> verify_gpu_support() File "E:\PyTorch_Build\pytorch\rtx5070_test.py", line 6, in verify_gpu_support if not torch.cuda.is_available(): AttributeError: module 'torch' has no attribute 'cuda' (rtx5070_env) PS E:\PyTorch_Build\pytorch>

PowerShell 7 环境已加载 (版本: 7.5.2) PS C:\Users\Administrator\Desktop> cd E:\PyTorch_Build\pytorch PS E:\PyTorch_Build\pytorch> .\pytorch_env\Scripts\activate (pytorch_env) PS E:\PyTorch_Build\pytorch> # 退出虚拟环境 (pytorch_env) PS E:\PyTorch_Build\pytorch> deactivate PS E:\PyTorch_Build\pytorch> PS E:\PyTorch_Build\pytorch> # 删除旧环境 PS E:\PyTorch_Build\pytorch> Remove-Item -Recurse -Force .\pytorch_env PS E:\PyTorch_Build\pytorch> Remove-Item -Recurse -Force .\cuda_env PS E:\PyTorch_Build\pytorch> PS E:\PyTorch_Build\pytorch> # 创建新虚拟环境 PS E:\PyTorch_Build\pytorch> python -m venv rtx5070_env PS E:\PyTorch_Build\pytorch> .\rtx5070_env\Scripts\activate (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 安装基础编译工具 (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip install -U pip setuptools wheel ninja cmake Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Requirement already satisfied: pip in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (22.3.1) Collecting pip Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b7/3f/945ef7ab14dc4f9d7f40288d2df998d1837ee0888ec3659c813487572faa/pip-25.2-py3-none-any.whl (1.8 MB) Requirement already satisfied: setuptools in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (65.5.0) Collecting setuptools Using cached https://pypi.tuna.tsinghua.edu.cn/packages/a3/dc/17031897dae0efacfea57dfd3a82fdd2a2aeb58e0ff71b77b87e44edc772/setuptools-80.9.0-py3-none-any.whl (1.2 MB) Collecting wheel Using cached https://pypi.tuna.tsinghua.edu.cn/packages/0b/2c/87f3254fd8ffd29e4c02732eee68a83a1d3c346ae39bc6822dcbcb697f2b/wheel-0.45.1-py3-none-any.whl (72 kB) Collecting ninja Using cached https://pypi.tuna.tsinghua.edu.cn/packages/29/45/c0adfbfb0b5895aa18cec400c535b4f7ff3e52536e0403602fc1a23f7de9/ninja-1.13.0-py3-none-win_amd64.whl (309 kB) Collecting cmake Using cached https://pypi.tuna.tsinghua.edu.cn/packages/7c/d0/73cae88d8c25973f2465d5a4457264f95617c16ad321824ed4c243734511/cmake-4.1.0-py3-none-win_amd64.whl (37.6 MB) ERROR: To modify pip, please run the following command: E:\PyTorch_Build\pytorch\rtx5070_env\Scripts\python.exe -m pip install -U pip setuptools wheel ninja cmake [notice] A new release of pip available: 22.3.1 -> 25.2 [notice] To update, run: python.exe -m pip install --upgrade pip (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 验证 CUDA 安装 (rtx5070_env) PS E:\PyTorch_Build\pytorch> nvcc --version # 应显示 CUDA 12.x nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2025 NVIDIA Corporation Built on Wed_Jul_16_20:06:48_Pacific_Daylight_Time_2025 Cuda compilation tools, release 13.0, V13.0.48 Build cuda_13.0.r13.0/compiler.36260728_0 (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 正确更新 pip 和工具链 (rtx5070_env) PS E:\PyTorch_Build\pytorch> python -m pip install -U pip setuptools wheel ninja cmake Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Requirement already satisfied: pip in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (22.3.1) Collecting pip Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b7/3f/945ef7ab14dc4f9d7f40288d2df998d1837ee0888ec3659c813487572faa/pip-25.2-py3-none-any.whl (1.8 MB) Requirement already satisfied: setuptools in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (65.5.0) Collecting setuptools Using cached https://pypi.tuna.tsinghua.edu.cn/packages/a3/dc/17031897dae0efacfea57dfd3a82fdd2a2aeb58e0ff71b77b87e44edc772/setuptools-80.9.0-py3-none-any.whl (1.2 MB) Collecting wheel Using cached https://pypi.tuna.tsinghua.edu.cn/packages/0b/2c/87f3254fd8ffd29e4c02732eee68a83a1d3c346ae39bc6822dcbcb697f2b/wheel-0.45.1-py3-none-any.whl (72 kB) Collecting ninja Using cached https://pypi.tuna.tsinghua.edu.cn/packages/29/45/c0adfbfb0b5895aa18cec400c535b4f7ff3e52536e0403602fc1a23f7de9/ninja-1.13.0-py3-none-win_amd64.whl (309 kB) Collecting cmake Using cached https://pypi.tuna.tsinghua.edu.cn/packages/7c/d0/73cae88d8c25973f2465d5a4457264f95617c16ad321824ed4c243734511/cmake-4.1.0-py3-none-win_amd64.whl (37.6 MB) Installing collected packages: wheel, setuptools, pip, ninja, cmake Attempting uninstall: setuptools Found existing installation: setuptools 65.5.0 Uninstalling setuptools-65.5.0: Successfully uninstalled setuptools-65.5.0 Attempting uninstall: pip Found existing installation: pip 22.3.1 Uninstalling pip-22.3.1: Successfully uninstalled pip-22.3.1 Successfully installed cmake-4.1.0 ninja-1.13.0 pip-25.2 setuptools-80.9.0 wheel-0.45.1 (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 验证版本 (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip --version # 应显示 25.2+ pip 25.2 from E:\PyTorch_Build\pytorch\rtx5070_env\lib\site-packages\pip (python 3.10) (rtx5070_env) PS E:\PyTorch_Build\pytorch> cmake --version # 应显示 4.1.0+ cmake version 4.1.0 CMake suite maintained and supported by Kitware (kitware.com/cmake). (rtx5070_env) PS E:\PyTorch_Build\pytorch> ninja --version # 应显示 1.13.0+ 1.13.0.git.kitware.jobserver-pipe-1 (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 设置 CUDA 12.1 环境变量 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:CUDA_PATH = "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1" (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:PATH = "$env:CUDA_PATH\bin;" + $env:PATH (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 验证 CUDA 版本 (rtx5070_env) PS E:\PyTorch_Build\pytorch> nvcc --version # 应显示 release 12.1 nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2025 NVIDIA Corporation Built on Wed_Jul_16_20:06:48_Pacific_Daylight_Time_2025 Cuda compilation tools, release 13.0, V13.0.48 Build cuda_13.0.r13.0/compiler.36260728_0 (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 设置 cuDNN 路径(根据实际安装位置) (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:CUDNN_INCLUDE_DIR = "$env:CUDA_PATH\include" (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:CUDNN_LIBRARY = "$env:CUDA_PATH\lib\x64\cudnn.lib" (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 安装必要依赖 (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip install pyyaml numpy typing_extensions Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Collecting pyyaml Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b5/84/0fa4b06f6d6c958d207620fc60005e241ecedceee58931bb20138e1e5776/PyYAML-6.0.2-cp310-cp310-win_amd64.whl (161 kB) Collecting numpy Using cached https://pypi.tuna.tsinghua.edu.cn/packages/a3/dd/4b822569d6b96c39d1215dbae0582fd99954dcbcf0c1a13c61783feaca3f/numpy-2.2.6-cp310-cp310-win_amd64.whl (12.9 MB) Collecting typing_extensions Using cached https://pypi.tuna.tsinghua.edu.cn/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl (44 kB) Installing collected packages: typing_extensions, pyyaml, numpy Successfully installed numpy-2.2.6 pyyaml-6.0.2 typing_extensions-4.15.0 (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 安装 GPU 相关依赖 (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip install mkl mkl-include intel-openmp Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Collecting mkl Using cached https://pypi.tuna.tsinghua.edu.cn/packages/91/ae/025174ee141432b974f97ecd2aea529a3bdb547392bde3dd55ce48fe7827/mkl-2025.2.0-py2.py3-none-win_amd64.whl (153.6 MB) Collecting mkl-include Using cached https://pypi.tuna.tsinghua.edu.cn/packages/06/87/3eee37bf95c6b820b6394ad98e50132798514ecda1b2584c71c2c96b973c/mkl_include-2025.2.0-py2.py3-none-win_amd64.whl (1.3 MB) Collecting intel-openmp Using cached https://pypi.tuna.tsinghua.edu.cn/packages/89/ed/13fed53fcc7ea17ff84095e89e63418df91d4eeefdc74454243d529bf5a3/intel_openmp-2025.2.1-py2.py3-none-win_amd64.whl (34.0 MB) Collecting tbb==2022.* (from mkl) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/4e/d2/01e2a93f9c644585088188840bf453f23ed1a2838ec51d5ba1ada1ebca71/tbb-2022.2.0-py3-none-win_amd64.whl (420 kB) Collecting intel-cmplr-lib-ur==2025.2.1 (from intel-openmp) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/a8/70/938e81f58886fd4e114d5a5480d98c1396e73e40b7650f566ad0c4395311/intel_cmplr_lib_ur-2025.2.1-py2.py3-none-win_amd64.whl (1.2 MB) Collecting umf==0.11.* (from intel-cmplr-lib-ur==2025.2.1->intel-openmp) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/33/a0/c8d755f08f50ddd99cb4a29a7e950ced7a0903cb72253e57059063609103/umf-0.11.0-py2.py3-none-win_amd64.whl (231 kB) Collecting tcmlib==1.* (from tbb==2022.*->mkl) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/91/7b/e30c461a27b97e0090e4db822eeb1d37b310863241f8c3ee56f68df3e76e/tcmlib-1.4.0-py2.py3-none-win_amd64.whl (370 kB) Installing collected packages: tcmlib, mkl-include, umf, tbb, intel-cmplr-lib-ur, intel-openmp, mkl Successfully installed intel-cmplr-lib-ur-2025.2.1 intel-openmp-2025.2.1 mkl-2025.2.0 mkl-include-2025.2.0 tbb-2022.2.0 tcmlib-1.4.0 umf-0.11.0 (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 安装必要依赖 (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip install pyyaml numpy typing_extensions Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Requirement already satisfied: pyyaml in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (6.0.2) Requirement already satisfied: numpy in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (2.2.6) Requirement already satisfied: typing_extensions in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (4.15.0) (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 安装 GPU 相关依赖 (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip install mkl mkl-include intel-openmp Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Requirement already satisfied: mkl in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (2025.2.0) Requirement already satisfied: mkl-include in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (2025.2.0) Requirement already satisfied: intel-openmp in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (2025.2.1) Requirement already satisfied: tbb==2022.* in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (from mkl) (2022.2.0) Requirement already satisfied: intel-cmplr-lib-ur==2025.2.1 in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (from intel-openmp) (2025.2.1) Requirement already satisfied: umf==0.11.* in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (from intel-cmplr-lib-ur==2025.2.1->intel-openmp) (0.11.0) Requirement already satisfied: tcmlib==1.* in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (from tbb==2022.*->mkl) (1.4.0) (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 设置编译参数 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:USE_CUDA=1 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:USE_CUDNN=1 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:CMAKE_GENERATOR="Ninja" (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:MAX_JOBS=8 # 根据 CPU 核心数设置 (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 运行编译 (rtx5070_env) PS E:\PyTorch_Build\pytorch> python setup.py install >> --cmake >> --cmake-only >> --cmake-generator="Ninja" >> --verbose >> -DCMAKE_CUDA_COMPILER="${env:CUDA_PATH}\bin\nvcc.exe" >> -DCUDNN_INCLUDE_DIR="${env:CUDNN_INCLUDE_DIR}" >> -DCUDNN_LIBRARY="${env:CUDNN_LIBRARY}" >> -DTORCH_CUDA_ARCH_LIST="8.9;9.0;12.0" Building wheel torch-2.9.0a0+git2d31c3d option --cmake-generator not recognized (rtx5070_env) PS E:\PyTorch_Build\pytorch> python rtx5070_test.py ============================================================ Traceback (most recent call last): File "E:\PyTorch_Build\pytorch\rtx5070_test.py", line 39, in <module> verify_gpu_support() File "E:\PyTorch_Build\pytorch\rtx5070_test.py", line 6, in verify_gpu_support if not torch.cuda.is_available(): AttributeError: module 'torch' has no attribute 'cuda' (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 设置编译架构参数 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:TORCH_CUDA_ARCH_LIST="8.9;9.0;12.0" (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 使用正确的编译命令 (rtx5070_env) PS E:\PyTorch_Build\pytorch> python setup.py install >> --cmake >> --verbose >> -DCMAKE_CUDA_COMPILER="${env:CUDA_PATH}\bin\nvcc.exe" >> -DCUDNN_INCLUDE_DIR="${env:CUDNN_INCLUDE_DIR}" >> -DCUDNN_LIBRARY="${env:CUDNN_LIBRARY}" >> -DCMAKE_GENERATOR="Ninja" >> -DUSE_CUDA=ON >> -DUSE_CUDNN=ON Building wheel torch-2.9.0a0+git2d31c3d option -D not recognized (rtx5070_env) PS E:\PyTorch_Build\pytorch> python enhanced_test.py ============================================================ Python 版本: 3.10.10 Traceback (most recent call last): File "E:\PyTorch_Build\pytorch\enhanced_test.py", line 64, in <module> verify_installation() File "E:\PyTorch_Build\pytorch\enhanced_test.py", line 11, in verify_installation print(f"\nPyTorch 版本: {torch.__version__}") AttributeError: module 'torch' has no attribute '__version__' (rtx5070_env) PS E:\PyTorch_Build\pytorch>

“PowerShell 7 环境已加载 (版本: 7.5.2) PS C:\Users\Administrator\Desktop> cd E:\PyTorch_Build\pytorch PS E:\PyTorch_Build\pytorch> python -m venv rtx5070_env PS E:\PyTorch_Build\pytorch> .\rtx5070_env\Scripts\activate (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 可随时运行的验证脚本 (rtx5070_env) PS E:\PyTorch_Build\pytorch> import torch import: The term 'import' is not recognized as a name of a cmdlet, function, script file, or executable program. Check the spelling of the name, or if a path was included, verify that the path is correct and try again. (rtx5070_env) PS E:\PyTorch_Build\pytorch> print(f"编译前CUDA状态: {torch.cuda.is_available()}") f编译前CUDA状态: {torch.cuda.is_available()}: The term 'f编译前CUDA状态: {torch.cuda.is_available()}' is not recognized as a name of a cmdlet, function, script file, or executable program. Check the spelling of the name, or if a path was included, verify that the path is correct and try again. (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 备份并重置问题配置文件 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $profileBackup = "$env:USERPROFILE\Documents\WindowsPowerShell\profile_backup_$(Get-Date -Format yyyyMMdd).ps1" (rtx5070_env) PS E:\PyTorch_Build\pytorch> Copy-Item $PROFILE.CurrentUserAllHosts $profileBackup -Force Copy-Item: Cannot find path 'C:\Users\Administrator\Documents\PowerShell\profile.ps1' because it does not exist. (rtx5070_env) PS E:\PyTorch_Build\pytorch> Set-Content $PROFILE.CurrentUserAllHosts -Value "" (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 创建新脚本文件 (避免变量依赖) (rtx5070_env) PS E:\PyTorch_Build\pytorch> $scriptPath = "E:\PyTorch_Build\pytorch\build_optimized_v11.ps1" (rtx5070_env) PS E:\PyTorch_Build\pytorch> $scriptContent = @' >> # 禁用配置文件加载 >> $env:PSModuleAutoloadingPreference = "None" >> >> # 设置控制台编码为系统默认 >> [Console]::OutputEncoding = [System.Text.Encoding]::Default >> Remove-Item Env:\PYTHONUTF8 -ErrorAction SilentlyContinue >> >> # ------ 核心构建函数 ------ >> function Build-PyTorch { >> param( >> [string]$PythonPath, >> [string]$CudaPath, >> [string]$CudnnPath >> ) >> >> try { >> # 验证路径存在性 >> if (-not (Test-Path $PythonPath)) { throw "Python路径无效" } >> if (-not (Test-Path "$CudaPath\bin\nvcc.exe")) { throw "CUDA路径无效" } >> if (-not (Test-Path "$CudnnPath\include\cudnn.h")) { throw "cuDNN路径无效" } >> >> # 设置环境变量 >> $env:CUDA_PATH = $CudaPath >> $env:CUDNN_PATH = $CudnnPath >> $env:PATH = "$CudaPath\bin;$CudnnPath\bin;$env:PATH" >> >> # 激活Python环境 >> & "$PythonPath\Scripts\Activate.ps1" >> >> # 执行构建命令 >> python setup.py clean >> pip install -r requirements.txt >> python setup.py develop >> >> return $true >> } >> catch { >> Write-Host "构建错误: $_" -ForegroundColor Red >> return $false >> } >> } >> # ------ 主执行流程 ------ >> $success = Build-PyTorch >> -PythonPath "E:\PyTorch_Build\pytorch\rtx5070_env" >> -CudaPath "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1" >> -CudnnPath "E:\Program Files\NVIDIA\CUDNN\v8.9.6" >> >> if ($success) { >> Write-Host "✅ PyTorch 构建成功" -ForegroundColor Green >> python -c "import torch; print('CUDA可用:', torch.cuda.is_available())" >> } >> else { >> Write-Host "❌ 构建失败,请检查错误日志" -ForegroundColor Red >> } >> '@ (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> Set-Content -Path $scriptPath -Value $scriptContent (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 配置执行策略(仅当前会话) (rtx5070_env) PS E:\PyTorch_Build\pytorch> Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass -Force (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 运行脚本(不加载配置文件) (rtx5070_env) PS E:\PyTorch_Build\pytorch> powershell -NoProfile -File $scriptPath 所在位置 E:\PyTorch_Build\pytorch\build_optimized_v11.ps1:52 字符: 16 + Write-Host "鉂?鏋勫缓澶辫触锛岃妫€鏌ラ敊璇棩蹇? -ForegroundColor Red + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 字符串缺少终止符: "。 所在位置 E:\PyTorch_Build\pytorch\build_optimized_v11.ps1:51 字符: 6 + else { + ~ 语句块或类型定义中缺少右“}”。 + CategoryInfo : ParserError: (:) [], ParentContainsErrorRecordException + FullyQualifiedErrorId : TerminatorExpectedAtEndOfString (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 测试1:独立验证CUDA (rtx5070_env) PS E:\PyTorch_Build\pytorch> & "$env:CUDA_PATH\bin\nvcc.exe" --version &: The term 'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin\nvcc.exe' is not recognized as a name of a cmdlet, function, script file, or executable program. Check the spelling of the name, or if a path was included, verify that the path is correct and try again. (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 测试2:验证cuDNN (rtx5070_env) PS E:\PyTorch_Build\pytorch> Get-ChildItem "$env:CUDNN_PATH\bin\cudnn64_*.dll" | >> ForEach-Object { [System.Diagnostics.FileVersionInfo]::GetVersionInfo($_.FullName).FileVersion } Get-ChildItem: Cannot find path 'E:\bin' because it does not exist. (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 测试3:纯净Python环境 (rtx5070_env) PS E:\PyTorch_Build\pytorch> & "E:\PyTorch_Build\pytorch\rtx5070_env\Scripts\python.exe" -c "print('环境验证通过')" 环境验证通过 (rtx5070_env) PS E:\PyTorch_Build\pytorch> ”你先给我解释一下 现在的情况好么?我可以理解为 我们非但没有解决问题 还把我原来的powershell7给弄崩溃了吗?

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