如何解决如何运行Kaggle / docker-python容器GPU特定版本
我正在尝试将Kaggle/docker-python Docker容器安装到Ubuntu 20.04 LTS。
您可以在* 5.CODE equipmentGrid.Columns[2].DataPropertyName = "errorCode[2]"
代码
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将存储库克隆到我的计算机
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进入文件夹“ docker-python”
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将项目重新基于我需要的特定版本（1个父级0c13748提交0411f8f7b6adcf7489a837f06bb2ef6bce485151），此版本具有与本地环境设置相同的自述文件指令，并与当前版本相同。
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ERROR: tensorflow-1.13.1-cp36-cp36m-linux_x86_64.whl is not a supported wheel on this platform. The command '/bin/sh -c pip uninstall -y tensorflow && pip install /tmp/tensorflow_gpu/tensorflow*.whl && rm -rf /tmp/tensorflow_gpu && conda uninstall -y pytorch-cpu torchvision-cpu && conda install -y pytorch torchvision cudatoolkit=10.0 -c pytorch && pip uninstall -y mxnet && pip install --no-deps mxnet-cu100 && /tmp/clean-layer.sh' returned a non-zero code: 1 -
'*'输出
./build --gpu
./build --gpu- 测试
+ docker build --rm --pull --no-cache -t kaggle/python-gpu-build -f gpu.Dockerfile .
Sending build context to Docker daemon 2.366MB
Step 1/22 : ARG BASE_TAG=staging
Step 2/22 : FROM nvidia/cuda:9.2-cudnn7-devel-ubuntu16.04 AS nvidia
9.2-cudnn7-devel-ubuntu16.04: Pulling from nvidia/cuda
Digest: sha256:7839953f1acd44e625c3a91dcb3fe6257b89a82b296bf379ee6d4ac53ade3b01
Status: Image is up to date for nvidia/cuda:9.2-cudnn7-devel-ubuntu16.04
---> 5eb2ff1d7f52
Step 3/22 : FROM gcr.io/kaggle-images/python-tensorflow-whl:1.13.1-py36 as tensorflow_whl
1.13.1-py36: Pulling from kaggle-images/python-tensorflow-whl
Digest: sha256:a0be5fce8571d74d1163d95608c130433ff3de84da2e21e2174a9e195a165e57
Status: Image is up to date for gcr.io/kaggle-images/python-tensorflow-whl:1.13.1-py36
---> 98acbb4d059d
Step 4/22 : FROM gcr.io/kaggle-images/python:${BASE_TAG}
staging: Pulling from kaggle-images/python
Digest: sha256:3f5b8e4a4b582370ae83b80ed5e6d51a42fe532b7ee1c0d79aff14392a7195d0
Status: Image is up to date for gcr.io/kaggle-images/python:staging
---> 63d5f197c566
Step 5/22 : ADD clean-layer.sh /tmp/clean-layer.sh
---> 7fdb1435b413
Step 6/22 : COPY --from=nvidia /etc/apt/sources.list.d/cuda.list /etc/apt/sources.list.d/
---> e02dffcd6c16
Step 7/22 : COPY --from=nvidia /etc/apt/sources.list.d/nvidia-ml.list /etc/apt/sources.list.d/
---> a86c5de9a16f
Step 8/22 : COPY --from=nvidia /etc/apt/trusted.gpg /etc/apt/trusted.gpg.d/cuda.gpg
---> 6982b711cebf
Step 9/22 : ENV CUDA_VERSION=10.0.130
---> Running in b43146b862e2
Removing intermediate container b43146b862e2
---> 7c6f5a9681ba
Step 10/22 : ENV CUDA_PKG_VERSION=10-0=$CUDA_VERSION-1
---> Running in 2a136bd7f75b
Removing intermediate container 2a136bd7f75b
---> 3f0df42c0bf6
Step 11/22 : LABEL com.nvidia.volumes.needed="nvidia_driver"
---> Running in f052485fedf9
Removing intermediate container f052485fedf9
---> 682a2ef8541c
Step 12/22 : LABEL com.nvidia.cuda.version="${CUDA_VERSION}"
---> Running in 52a27ca2c0a5
Removing intermediate container 52a27ca2c0a5
---> 8dfe813ffea3
Step 13/22 : ENV PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
---> Running in 8f9e4cd191f8
Removing intermediate container 8f9e4cd191f8
---> c8233368f6cf
Step 14/22 : ENV LD_LIBRARY_PATH="/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs"
---> Running in 6f4e255fed57
Removing intermediate container 6f4e255fed57
---> 582110d74757
Step 15/22 : ENV NVIDIA_VISIBLE_DEVICES=all
---> Running in 32adea6204a5
Removing intermediate container 32adea6204a5
---> 3c80f8553a90
Step 16/22 : ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
---> Running in 60c61ba99fe1
Removing intermediate container 60c61ba99fe1
---> 53fb75b7ee40
Step 17/22 : ENV NVIDIA_REQUIRE_CUDA="cuda>=10.0"
---> Running in 702ef0fe2cb6
Removing intermediate container 702ef0fe2cb6
---> e399883a92dc
Step 18/22 : RUN apt-get update && apt-get install -y --no-install-recommends cuda-cupti-$CUDA_PKG_VERSION cuda-cudart-$CUDA_PKG_VERSION cuda-cudart-dev-$CUDA_PKG_VERSION cuda-libraries-$CUDA_PKG_VERSION cuda-libraries-dev-$CUDA_PKG_VERSION cuda-nvml-dev-$CUDA_PKG_VERSION cuda-minimal-build-$CUDA_PKG_VERSION cuda-command-line-tools-$CUDA_PKG_VERSION libcudnn7=7.5.0.56-1+cuda10.0 libcudnn7-dev=7.5.0.56-1+cuda10.0 libnccl2=2.4.2-1+cuda10.0 libnccl-dev=2.4.2-1+cuda10.0 && ln -s /usr/local/cuda-10.0 /usr/local/cuda && ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1 && /tmp/clean-layer.sh
---> Running in 0f6bd3543d47
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Reading package lists...
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Building dependency tree...
Reading state information...
The following additional packages will be installed:
cuda-compiler-10-0 cuda-cublas-10-0 cuda-cublas-dev-10-0 cuda-cufft-10-0
cuda-cufft-dev-10-0 cuda-cuobjdump-10-0 cuda-curand-10-0
cuda-curand-dev-10-0 cuda-cusolver-10-0 cuda-cusolver-dev-10-0
cuda-cusparse-10-0 cuda-cusparse-dev-10-0 cuda-driver-dev-10-0 cuda-gdb-10-0
cuda-gpu-library-advisor-10-0 cuda-license-10-0 cuda-memcheck-10-0
cuda-misc-headers-10-0 cuda-npp-10-0 cuda-npp-dev-10-0 cuda-nvcc-10-0
cuda-nvdisasm-10-0 cuda-nvgraph-10-0 cuda-nvgraph-dev-10-0 cuda-nvjpeg-10-0
cuda-nvjpeg-dev-10-0 cuda-nvprof-10-0 cuda-nvprune-10-0 cuda-nvrtc-10-0
cuda-nvrtc-dev-10-0 cuda-nvtx-10-0
The following NEW packages will be installed:
cuda-command-line-tools-10-0 cuda-compiler-10-0 cuda-cublas-10-0
cuda-cublas-dev-10-0 cuda-cudart-10-0 cuda-cudart-dev-10-0 cuda-cufft-10-0
cuda-cufft-dev-10-0 cuda-cuobjdump-10-0 cuda-cupti-10-0 cuda-curand-10-0
cuda-curand-dev-10-0 cuda-cusolver-10-0 cuda-cusolver-dev-10-0
cuda-cusparse-10-0 cuda-cusparse-dev-10-0 cuda-driver-dev-10-0 cuda-gdb-10-0
cuda-gpu-library-advisor-10-0 cuda-libraries-10-0 cuda-libraries-dev-10-0
cuda-license-10-0 cuda-memcheck-10-0 cuda-minimal-build-10-0
cuda-misc-headers-10-0 cuda-npp-10-0 cuda-npp-dev-10-0 cuda-nvcc-10-0
cuda-nvdisasm-10-0 cuda-nvgraph-10-0 cuda-nvgraph-dev-10-0 cuda-nvjpeg-10-0
cuda-nvjpeg-dev-10-0 cuda-nvml-dev-10-0 cuda-nvprof-10-0 cuda-nvprune-10-0
cuda-nvrtc-10-0 cuda-nvrtc-dev-10-0 cuda-nvtx-10-0 libcudnn7 libcudnn7-dev
libnccl-dev libnccl2
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*** LICENSE AGREEMENT ***
By using this software you agree to fully comply with the terms and
conditions of the EULA (End User License Agreement). The EULA is located
at /usr/local/cuda-10.0/doc/EULA.txt. The EULA can also be found at
http://docs.nvidia.com/cuda/eula/index.html. If you do not agree to the
terms and conditions of the EULA,do not use the software.
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+ rm -rf '/root/.cache/pip/*'
+ apt-get autoremove -y
Reading package lists...
Building dependency tree...
Reading state information...
0 upgraded,0 newly installed,0 to remove and 102 not upgraded.
+ apt-get clean
+ rm -rf '/usr/local/src/*'
+ conda clean -y --tarballs
Cache location:
There are no tarballs to remove
Removing intermediate container 0f6bd3543d47
---> 772f18453069
Step 19/22 : COPY --from=tensorflow_whl /tmp/tensorflow_gpu/*.whl /tmp/tensorflow_gpu/
---> 755988937ab8
Step 20/22 : RUN pip uninstall -y tensorflow && pip install /tmp/tensorflow_gpu/tensorflow*.whl && rm -rf /tmp/tensorflow_gpu && conda uninstall -y pytorch-cpu torchvision-cpu && conda install -y pytorch torchvision cudatoolkit=10.0 -c pytorch && pip uninstall -y mxnet && pip install --no-deps mxnet-cu100 && /tmp/clean-layer.sh
---> Running in af6d3b9dbdd4
Found existing installation: tensorflow 2.3.1
Uninstalling tensorflow-2.3.1:
Successfully uninstalled tensorflow-2.3.1
ERROR: tensorflow-1.13.1-cp36-cp36m-linux_x86_64.whl is not a supported wheel on this platform.
The command '/bin/sh -c pip uninstall -y tensorflow && pip install /tmp/tensorflow_gpu/tensorflow*.whl && rm -rf /tmp/tensorflow_gpu && conda uninstall -y pytorch-cpu torchvision-cpu && conda install -y pytorch torchvision cudatoolkit=10.0 -c pytorch && pip uninstall -y mxnet && pip install --no-deps mxnet-cu100 && /tmp/clean-layer.sh' returned a non-zero code: 1
- 输出
./test --gpu
./test --gpu+ rm -rf /tmp/python-build
+ docker rm jupyter_test
Error: No such container: jupyter_test
+ true
+ mkdir -p /tmp/python-build/tmp
+ mkdir -p /tmp/python-build/devshm
+ mkdir -p /tmp/python-build/working
+ docker run -d --name=jupyter_test --read-only --net=none -e HOME=/tmp -v /home/fanatical/Documents/docker-python:/input:ro -v /tmp/python-build/working:/working -w=/working -v /tmp/python-build/tmp:/tmp -v /tmp/python-build/devshm:/dev/shm kaggle/python-gpu-build jupyter notebook --allow-root '--ip=*'
Unable to find image 'kaggle/python-gpu-build:latest' locally
docker: Error response from daemon: pull access denied for kaggle/python-gpu-build,repository does not exist or may require 'docker login': denied: requested access to the resource is denied.
See 'docker run --help'.
错误2
docker：来自守护程序的错误响应：OCI运行时创建失败： container_linux.go：349：启动容器进程引起 “ process_linux.go：449：容器初始化导致” process_linux.go：432： 运行预启动钩子1导致\“运行钩子错误：退出状态1， stdout：，stderr：nvidia-container-cli：初始化错误：驱动程序 错误：无法处理请求\\ n \“”“：未知。
我尝试过的解决方案1
docker run --runtime nvidia --rm -it gcr.io/kaggle-gpu-images/python /bin/bash
在项目根git clone https://github.com/Kaggle/docker-python.git
下，运行docker-python/。
将以下内容放在./build --gpu中[在macos中称为：bash_profile，在Linux中：bashrc]在[linux]中[https://askubuntu.com/questions/127056/where-is-bashrc]隐藏在主文件夹中的文件中，this是编辑文件的方式
我无法理解这一部分，因为它和以前一样。
kjupyter（）{ 码头工人运行--runtime = nvidia -v $ PWD：/ tmp / working -v / data：/ tmp / working / data -w = / tmp / working -p 8888：8888 --rm -it kaggle / python-gpu-build bash -c“导出 LD_LIBRARY_PATH = / usr / local / cuda / lib64;点安装 jupyter_contrib_nbextensions;点安装 jupyter_nbextensions_configurator; jupyter contrib nbextension安装 - 用户; jupyter笔记本--notebook-dir = / tmp / working --ip ='*'--port = 8888 --no-browser --allow-root“}
比运行.bash_profile
source .bash_profile
现在，您可以从终端运行 export LD_LIBRARY_PATH=/usr/local/cuda/lib64并转到kjupyter进行Jupyter Notebook。 Nbextensions的处理方式与CPU版本类似。
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