fatal error: cuda_runtime_api.h: No such file or directory when trying to use cuda in docker
fatal error: cuda_runtime_api.h: No such file or directory when trying to use cuda in docker
我正在尝试为我想部署的 python 脚本构建一个 docker 图像。
这是我第一次使用 docker，所以我可能做错了什么，但我不知道是什么。
我的系统：
OS: Ubuntu 20.04
docker version: 19.03.8
我正在使用这个 Dockerfile：
# Dockerfile
FROM nvidia/cuda:11.0-base
COPY . /SingleModelTest
WORKDIR /SingleModelTest
RUN nvidia-smi
RUN set -xe \ #these are just to make sure pip and git are installed to install the requirements
&& apt-get update \
&& apt-get install python3-pip -y \
&& apt-get install git -y
RUN pip3 install --upgrade pip
RUN pip3 install -r requirements/requirements1.txt
RUN pip3 install -r requirements/requirements2.txt #this is where it fails
ENTRYPOINT ["python"]
CMD ["TabNetAPI.py"]
nvidia-smi 的输出符合预期：
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.80.02 Driver Version: 450.80.02 CUDA Version: 11.0 |
|-------------------------------+----------------------+----------------------+
| 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 GeForce GTX 1050 Off | 00000000:01:00.0 On | N/A |
| 0% 54C P0 N/A / 90W | 1983MiB / 1995MiB | 18% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
所以 cuda 确实可以工作，但是当我尝试从需求文件安装所需的包时，发生了这种情况：
command: /usr/bin/python3 -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/SingleModelTest/src/mmdet/setup.py'"'"'; __file__='"'"'/SingleModelTest/src/mmdet/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' develop --no-deps
cwd: /SingleModelTest/src/mmdet/
Complete output (24 lines):
running develop
running egg_info
creating mmdet.egg-info
writing mmdet.egg-info/PKG-INFO
writing dependency_links to mmdet.egg-info/dependency_links.txt
writing requirements to mmdet.egg-info/requires.txt
writing top-level names to mmdet.egg-info/top_level.txt
writing manifest file 'mmdet.egg-info/SOURCES.txt'
reading manifest file 'mmdet.egg-info/SOURCES.txt'
writing manifest file 'mmdet.egg-info/SOURCES.txt'
running build_ext
building 'mmdet.ops.utils.compiling_info' extension
creating build
creating build/temp.linux-x86_64-3.8
creating build/temp.linux-x86_64-3.8/mmdet
creating build/temp.linux-x86_64-3.8/mmdet/ops
creating build/temp.linux-x86_64-3.8/mmdet/ops/utils
creating build/temp.linux-x86_64-3.8/mmdet/ops/utils/src
x86_64-linux-gnu-gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DWITH_CUDA -I/usr/local/lib/python3.8/dist-packages/torch/include -I/usr/local/lib/python3.8/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.8/dist-packages/torch/include/TH -I/usr/local/lib/python3.8/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.8 -c mmdet/ops/utils/src/compiling_info.cpp -o build/temp.linux-x86_64-3.8/mmdet/ops/utils/src/compiling_info.o -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=compiling_info -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11
mmdet/ops/utils/src/compiling_info.cpp:3:10: fatal error: cuda_runtime_api.h: No such file or directory
3 | #include <cuda_runtime_api.h>
| ^~~~~~~~~~~~~~~~~~~~
compilation terminated.
error: command 'x86_64-linux-gnu-gcc' failed with exit status 1
----------------------------------------
ERROR: Command errored out with exit status 1: /usr/bin/python3 -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/SingleModelTest/src/mmdet/setup.py'"'"'; __file__='"'"'/SingleModelTest/src/mmdet/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' develop --no-deps Check the logs for full command output.
失败的包是mmdetection。
我正在使用 2 个单独的需求文件来确保某些包先于其他包安装，以防止依赖项失败
requirements1.txt:
torch==1.4.0+cu100
-f https://download.pytorch.org/whl/torch_stable.html
torchvision==0.5.0+cu100
-f https://download.pytorch.org/whl/torch_stable.html
numpy==1.19.2
requirements2.txt:
addict==2.3.0
albumentations==0.5.0
appdirs==1.4.4
asynctest==0.13.0
attrs==20.2.0
certifi==2020.6.20
chardet==3.0.4
cityscapesScripts==2.1.7
click==7.1.2
codecov==2.1.10
coloredlogs==14.0
coverage==5.3
cycler==0.10.0
Cython==0.29.21
decorator==4.4.2
flake8==3.8.4
Flask==1.1.2
humanfriendly==8.2
idna==2.10
imagecorruptions==1.1.0
imageio==2.9.0
imgaug==0.4.0
iniconfig==1.1.1
isort==5.6.4
itsdangerous==1.1.0
Jinja2==2.11.2
kiwisolver==1.2.0
kwarray==0.5.9
MarkupSafe==1.1.1
matplotlib==3.3.2
mccabe==0.6.1
mmcv==0.4.3
-e git+https://github.com/open-mmlab/mmdetection.git@0f33c08d8d46eba8165715a0995841a975badfd4#egg=mmdet
networkx==2.5
opencv-python==4.4.0.44
opencv-python-headless==4.4.0.44
ordered-set==4.0.2
packaging==20.4
pandas==1.1.3
Pillow==6.2.2
pluggy==0.13.1
py==1.9.0
pycocotools==2.0.2
pycodestyle==2.6.0
pyflakes==2.2.0
pyparsing==2.4.7
pyquaternion==0.9.9
pytesseract==0.3.6
pytest==6.1.1
pytest-cov==2.10.1
pytest-runner==5.2
python-dateutil==2.8.1
pytz==2020.1
PyWavelets==1.1.1
PyYAML==5.3.1
requests==2.24.0
scikit-image==0.17.2
scipy==1.5.3
Shapely==1.7.1
six==1.15.0
terminaltables==3.1.0
tifffile==2020.9.3
toml==0.10.1
tqdm==4.50.2
typing==3.7.4.3
ubelt==0.9.2
urllib3==1.25.11
Werkzeug==1.0.1
xdoctest==0.15.0
yapf==0.30.0
我用来（尝试）构建图像的命令：
nvidia-docker build -t firstdockertestsinglemodel:latest
我尝试过的事情：
- 设置 cuda 环境变量，如 CUDA_HOME、LIBRARY_PATH、LD_LIBRARY_PATH，但我不确定我是否正确设置，因为我无法检查我设置的路径，因为我在 Ubuntu 文件应用
中看不到它们
如果有人能提供任何帮助，我将不胜感激。
如果我需要提供更多信息，我会很乐意。
编辑：这个答案只是告诉您如何验证 docker 图像中发生的事情。不幸的是，我无法弄清楚为什么会这样。
如何查看？
在 docker 构建的每一步，您都可以看到正在生成的各个层。您可以使用该 ID 创建一个临时图像来检查发生了什么。例如
docker build -t my_bonk_example .
[...]
Removing intermediate container xxxxxxxxxxxxx
---> 57778e7c9788
Step 19/31 : RUN mkdir -p /tmp/spark-events
---> Running in afd21d853bcb
Removing intermediate container xxxxxxxxxxxxx
---> 33b26e1a2286 <-- let's use this ID
[ failure happens ]
docker run -it --rm --name bonk_container_before_failure 33b26e1a2286 bash
# now you're in the container
echo $LD_LIBRARY_PATH
ls /usr/local/cuda
关于您的 Docker 文件的附注：
如果您更改 Docker 文件中的指令顺序，则可以缩短未来构建的构建时间。 Docker 使用的缓存会在发现与之前构建不同的内容时失效。我希望您比 docker 图像的要求更频繁地更改代码，因此在 apt 指令之后移动 COPY 是有意义的。例如
# Dockerfile
FROM nvidia/cuda:10.2-base
RUN set -xe \
&& apt-get update \
&& apt-get install python3-pip -y \
&& apt-get install git -y
RUN pip3 install --upgrade pip
WORKDIR /SingleModelTest
COPY requirements /SingleModelTest/requirements
RUN pip3 install -r requirements/requirements1.txt
RUN pip3 install -r requirements/requirements2.txt
COPY . /SingleModelTest
RUN nvidia-smi
ENTRYPOINT ["python"]
CMD ["TabNetAPI.py"]
注意：这只是一个示例。
关于为什么无法构建图像，我发现 PyTorch 1.4 不支持 CUDE 11.0 (https://discuss.pytorch.org/t/pytorch-with-cuda-11-compatibility/89254)，但使用以前版本的 CUDA 也不能解决问题。
感谢@Robert Crovella，我解决了我的问题。
结果我只需要使用 nvidia/cuda/10.0-devel 作为基本图像而不是 nvidia/cuda/10.0-base
所以我的 Dockerfile 现在是：
# Dockerfile
FROM nvidia/cuda:10.0-devel
RUN nvidia-smi
RUN set -xe \
&& apt-get update \
&& apt-get install python3-pip -y \
&& apt-get install git -y
RUN pip3 install --upgrade pip
WORKDIR /SingleModelTest
COPY requirements /SingleModelTest/requirements
RUN export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64
RUN pip3 install -r requirements/requirements1.txt
RUN pip3 install -r requirements/requirements2.txt
COPY . /SingleModelTest
ENTRYPOINT ["python"]
CMD ["TabNetAPI.py"]
我正在尝试为我想部署的 python 脚本构建一个 docker 图像。 这是我第一次使用 docker，所以我可能做错了什么，但我不知道是什么。
我的系统：
OS: Ubuntu 20.04
docker version: 19.03.8
我正在使用这个 Dockerfile：
# Dockerfile
FROM nvidia/cuda:11.0-base
COPY . /SingleModelTest
WORKDIR /SingleModelTest
RUN nvidia-smi
RUN set -xe \ #these are just to make sure pip and git are installed to install the requirements
&& apt-get update \
&& apt-get install python3-pip -y \
&& apt-get install git -y
RUN pip3 install --upgrade pip
RUN pip3 install -r requirements/requirements1.txt
RUN pip3 install -r requirements/requirements2.txt #this is where it fails
ENTRYPOINT ["python"]
CMD ["TabNetAPI.py"]
nvidia-smi 的输出符合预期：
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.80.02 Driver Version: 450.80.02 CUDA Version: 11.0 |
|-------------------------------+----------------------+----------------------+
| 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 GeForce GTX 1050 Off | 00000000:01:00.0 On | N/A |
| 0% 54C P0 N/A / 90W | 1983MiB / 1995MiB | 18% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
所以 cuda 确实可以工作，但是当我尝试从需求文件安装所需的包时，发生了这种情况：
command: /usr/bin/python3 -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/SingleModelTest/src/mmdet/setup.py'"'"'; __file__='"'"'/SingleModelTest/src/mmdet/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' develop --no-deps
cwd: /SingleModelTest/src/mmdet/
Complete output (24 lines):
running develop
running egg_info
creating mmdet.egg-info
writing mmdet.egg-info/PKG-INFO
writing dependency_links to mmdet.egg-info/dependency_links.txt
writing requirements to mmdet.egg-info/requires.txt
writing top-level names to mmdet.egg-info/top_level.txt
writing manifest file 'mmdet.egg-info/SOURCES.txt'
reading manifest file 'mmdet.egg-info/SOURCES.txt'
writing manifest file 'mmdet.egg-info/SOURCES.txt'
running build_ext
building 'mmdet.ops.utils.compiling_info' extension
creating build
creating build/temp.linux-x86_64-3.8
creating build/temp.linux-x86_64-3.8/mmdet
creating build/temp.linux-x86_64-3.8/mmdet/ops
creating build/temp.linux-x86_64-3.8/mmdet/ops/utils
creating build/temp.linux-x86_64-3.8/mmdet/ops/utils/src
x86_64-linux-gnu-gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DWITH_CUDA -I/usr/local/lib/python3.8/dist-packages/torch/include -I/usr/local/lib/python3.8/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.8/dist-packages/torch/include/TH -I/usr/local/lib/python3.8/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.8 -c mmdet/ops/utils/src/compiling_info.cpp -o build/temp.linux-x86_64-3.8/mmdet/ops/utils/src/compiling_info.o -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=compiling_info -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11
mmdet/ops/utils/src/compiling_info.cpp:3:10: fatal error: cuda_runtime_api.h: No such file or directory
3 | #include <cuda_runtime_api.h>
| ^~~~~~~~~~~~~~~~~~~~
compilation terminated.
error: command 'x86_64-linux-gnu-gcc' failed with exit status 1
----------------------------------------
ERROR: Command errored out with exit status 1: /usr/bin/python3 -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/SingleModelTest/src/mmdet/setup.py'"'"'; __file__='"'"'/SingleModelTest/src/mmdet/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' develop --no-deps Check the logs for full command output.
失败的包是mmdetection。 我正在使用 2 个单独的需求文件来确保某些包先于其他包安装，以防止依赖项失败
requirements1.txt:
torch==1.4.0+cu100
-f https://download.pytorch.org/whl/torch_stable.html
torchvision==0.5.0+cu100
-f https://download.pytorch.org/whl/torch_stable.html
numpy==1.19.2
requirements2.txt:
addict==2.3.0
albumentations==0.5.0
appdirs==1.4.4
asynctest==0.13.0
attrs==20.2.0
certifi==2020.6.20
chardet==3.0.4
cityscapesScripts==2.1.7
click==7.1.2
codecov==2.1.10
coloredlogs==14.0
coverage==5.3
cycler==0.10.0
Cython==0.29.21
decorator==4.4.2
flake8==3.8.4
Flask==1.1.2
humanfriendly==8.2
idna==2.10
imagecorruptions==1.1.0
imageio==2.9.0
imgaug==0.4.0
iniconfig==1.1.1
isort==5.6.4
itsdangerous==1.1.0
Jinja2==2.11.2
kiwisolver==1.2.0
kwarray==0.5.9
MarkupSafe==1.1.1
matplotlib==3.3.2
mccabe==0.6.1
mmcv==0.4.3
-e git+https://github.com/open-mmlab/mmdetection.git@0f33c08d8d46eba8165715a0995841a975badfd4#egg=mmdet
networkx==2.5
opencv-python==4.4.0.44
opencv-python-headless==4.4.0.44
ordered-set==4.0.2
packaging==20.4
pandas==1.1.3
Pillow==6.2.2
pluggy==0.13.1
py==1.9.0
pycocotools==2.0.2
pycodestyle==2.6.0
pyflakes==2.2.0
pyparsing==2.4.7
pyquaternion==0.9.9
pytesseract==0.3.6
pytest==6.1.1
pytest-cov==2.10.1
pytest-runner==5.2
python-dateutil==2.8.1
pytz==2020.1
PyWavelets==1.1.1
PyYAML==5.3.1
requests==2.24.0
scikit-image==0.17.2
scipy==1.5.3
Shapely==1.7.1
six==1.15.0
terminaltables==3.1.0
tifffile==2020.9.3
toml==0.10.1
tqdm==4.50.2
typing==3.7.4.3
ubelt==0.9.2
urllib3==1.25.11
Werkzeug==1.0.1
xdoctest==0.15.0
yapf==0.30.0
我用来（尝试）构建图像的命令：
nvidia-docker build -t firstdockertestsinglemodel:latest
我尝试过的事情：
- 设置 cuda 环境变量，如 CUDA_HOME、LIBRARY_PATH、LD_LIBRARY_PATH，但我不确定我是否正确设置，因为我无法检查我设置的路径，因为我在 Ubuntu 文件应用 中看不到它们
如果有人能提供任何帮助，我将不胜感激。 如果我需要提供更多信息，我会很乐意。
编辑：这个答案只是告诉您如何验证 docker 图像中发生的事情。不幸的是，我无法弄清楚为什么会这样。
如何查看？
在 docker 构建的每一步，您都可以看到正在生成的各个层。您可以使用该 ID 创建一个临时图像来检查发生了什么。例如
docker build -t my_bonk_example .
[...]
Removing intermediate container xxxxxxxxxxxxx
---> 57778e7c9788
Step 19/31 : RUN mkdir -p /tmp/spark-events
---> Running in afd21d853bcb
Removing intermediate container xxxxxxxxxxxxx
---> 33b26e1a2286 <-- let's use this ID
[ failure happens ]
docker run -it --rm --name bonk_container_before_failure 33b26e1a2286 bash
# now you're in the container
echo $LD_LIBRARY_PATH
ls /usr/local/cuda
关于您的 Docker 文件的附注：
如果您更改 Docker 文件中的指令顺序，则可以缩短未来构建的构建时间。 Docker 使用的缓存会在发现与之前构建不同的内容时失效。我希望您比 docker 图像的要求更频繁地更改代码，因此在 apt 指令之后移动 COPY 是有意义的。例如
# Dockerfile
FROM nvidia/cuda:10.2-base
RUN set -xe \
&& apt-get update \
&& apt-get install python3-pip -y \
&& apt-get install git -y
RUN pip3 install --upgrade pip
WORKDIR /SingleModelTest
COPY requirements /SingleModelTest/requirements
RUN pip3 install -r requirements/requirements1.txt
RUN pip3 install -r requirements/requirements2.txt
COPY . /SingleModelTest
RUN nvidia-smi
ENTRYPOINT ["python"]
CMD ["TabNetAPI.py"]
注意：这只是一个示例。
关于为什么无法构建图像，我发现 PyTorch 1.4 不支持 CUDE 11.0 (https://discuss.pytorch.org/t/pytorch-with-cuda-11-compatibility/89254)，但使用以前版本的 CUDA 也不能解决问题。
感谢@Robert Crovella，我解决了我的问题。
结果我只需要使用 nvidia/cuda/10.0-devel 作为基本图像而不是 nvidia/cuda/10.0-base
所以我的 Dockerfile 现在是：
# Dockerfile
FROM nvidia/cuda:10.0-devel
RUN nvidia-smi
RUN set -xe \
&& apt-get update \
&& apt-get install python3-pip -y \
&& apt-get install git -y
RUN pip3 install --upgrade pip
WORKDIR /SingleModelTest
COPY requirements /SingleModelTest/requirements
RUN export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64
RUN pip3 install -r requirements/requirements1.txt
RUN pip3 install -r requirements/requirements2.txt
COPY . /SingleModelTest
ENTRYPOINT ["python"]
CMD ["TabNetAPI.py"]