【发布时间】：2020-10-02 02:30:39
【问题描述】：
我正在尝试使用 CUDA 10.0 版在 Colab 上运行 Detectron2 模块，但从今天开始，Cuda 编译器的版本出现了一些问题。
运行!nvidia-smi 后得到的输出是：
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.36.06 Driver Version: 418.67 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla P100-PCIE... Off | 00000000:00:04.0 Off | 0 |
| N/A 36C P0 26W / 250W | 0MiB / 16280MiB | 0% Default |
| | | ERR! |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
我在运行!nvcc --version 后得到的是：
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2018 NVIDIA Corporation
Built on Sat_Aug_25_21:08:01_CDT_2018
Cuda compilation tools, release 10.0, V10.0.130
我无法理解不匹配的原因。运行!python -m detectron2.utils.collect_env 后检测器的输出也是：
---------------------- ----------------------------------------------------------------------------
sys.platform linux
Python 3.6.9 (default, Apr 18 2020, 01:56:04) [GCC 8.4.0]
numpy 1.18.5
detectron2 0.1.3 @/content/gdrive/My Drive/Data/Table_Struct/detectron2_repo/detectron2
Compiler GCC 7.5
CUDA compiler CUDA 10.1
detectron2 arch flags sm_60
DETECTRON2_ENV_MODULE <not set>
PyTorch 1.4.0+cu100 @/usr/local/lib/python3.6/dist-packages/torch
PyTorch debug build False
GPU available True
GPU 0 Tesla K80
CUDA_HOME /usr/local/cuda
Pillow 7.0.0
torchvision 0.5.0+cu100 @/usr/local/lib/python3.6/dist-packages/torchvision
torchvision arch flags sm_35, sm_50, sm_60, sm_70, sm_75
fvcore 0.1.1
cv2 4.1.2
---------------------- ----------------------------------------------------------------------------
PyTorch built with:
- GCC 7.3
- Intel(R) Math Kernel Library Version 2019.0.4 Product Build 20190411 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v0.21.1 (Git Hash 7d2fd500bc78936d1d648ca713b901012f470dbc)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- NNPACK is enabled
- CUDA Runtime 10.0
- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
- CuDNN 7.6.3
- Magma 2.5.1
- Build settings: BLAS=MKL, BUILD_NAMEDTENSOR=OFF, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -fopenmp -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -O2 -fPIC -Wno-narrowing -Wall -Wextra -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Wno-stringop-overflow, DISABLE_NUMA=1, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_STATIC_DISPATCH=OFF,
我的猜测是 Colab 上的 CUDA 版本与我使用的 Detectron2 不匹配。如果是这样，我该如何更改才能使其在 Google Colab 上运行。
【问题讨论】：
-
没有不匹配。驱动程序告诉您（通过 nvidia-smi）它支持的最大 cuda 版本。而已。您正在尝试解决一个不存在的问题
-
那么我为 Cuda 10.0 构建的 pytorch 应该可以正常工作吗？但我收到错误“RuntimeError: CUDA error: no kernel image is available for execution on the device”
-
我无法评论 PyTorch。如果这就是您的实际问题，那么将其设为 PyTorch 问题
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我认为这是由于 Pytorch 和 CUDA 版本不匹配造成的。 pytorch 是为 CUDA 10.0 版构建的，但系统不提供该环境。
-
可能是 CUDA 工具包版本，而不是 CUDA 驱动程序。这不是一个与 CUDA 编程相关的问题——它是关于让 PyTorch 在 colab 上运行，我认为这不是 Stack Overflow 的真正主题
标签： pytorch google-colaboratory