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docker-gpu.md
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docker-gpu.md
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https://www.nvidia.com/Download/index.aspx
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https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(version-2.0)
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https://github.com/NVIDIA/nvidia-docker/wiki/GPU-isolation-(version-1.0)
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https://unix.stackexchange.com/questions/249643/gpu-usage-per-process-on-a-linux-machine-cuda
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sudo yum install -y wget gcc pciutils git
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sudo yum install -y kernel-devel-$(uname -r) kernel-headers-$(uname -r)
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wget http://us.download.nvidia.com/tesla/410.129/NVIDIA-Linux-x86_64-410.129-diagnostic.run
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sudo bash NVIDIA-Linux-x86_64-410.129-diagnostic.run
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curl -fsSL https://get.docker.com | sh
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sudo usermod -aG docker $USER
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distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
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curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.repo | sudo tee /etc/yum.repos.d/nvidia-docker.repo
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sudo yum install -y nvidia-container-toolkit
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sudo systemctl restart docker
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git clone https://github.com/tensorflow/models.git
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docker run --gpus all -it --rm --mount type=bind,src=/home/newnius_cn/models,dst=/data tensorflow/tensorflow:1.14.0-gpu bash
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docker run --gpus '"device=0,1"' -it --rm --mount type=bind,src=/home/newnius_cn/models,dst=/data tensorflow/tensorflow:1.14.0-gpu bash
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export PYTHONPATH="$PYTHONPATH:/data"
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pip install requests
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# mnist
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python official/r1/mnist/mnist_test.py --benchmarks=.
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# CIFAR-10
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python official/r1/resnet/cifar10_download_and_extract.py --data_dir /data/ds
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python official/r1/resnet/cifar10_main.py --data_dir /data/ds/cifar-10-batches-bin --model_dir /tmp
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```python
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config = tf.ConfigProto()
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config.gpu_options.allow_growth = True
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config.log_device_placement = True
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sess = tf.Session(config=config)
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tf.keras.backend.set_session(sess)
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```
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sudo yum erase -y nvidia-container-runtime-hook.x86_64 nvidia-container-runtime.x86_64 nvidia-docker2.noarch docker-ce docker-ce-cli.x86_64
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sudo gpg --homedir /var/lib/yum/repos/x86_64/7/libnvidia-container/gpgdir --delete-key F796ECB0
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sudo gpg --homedir /var/lib/yum/repos/x86_64/7/nvidia-container-runtime/gpgdir --delete-key F796ECB0
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sudo gpg --homedir /var/lib/yum/repos/x86_64/7/nvidia-docker/gpgdir --delete-key F796ECB0
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[Can not use nvidia-container-runtime repository - repomod.xml signature could not be verified](https://github.com/NVIDIA/nvidia-docker/issues/836)
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ERROR: Unable to load the kernel module 'nvidia.ko'. This happens most frequently when this kernel module was built against the wrong or improperly configured kernel sources, with a version of gcc that differs from the one used to build the target kernel, or if a driver such as rivafb, nvidiafb, or nouveau is present and prevents the NVIDIA kernel module from obtaining ownership of the NVIDIA graphics device(s), or no NVIDIA GPU installed in this system is supported by this NVIDIA Linux graphics driver release.
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The problem is likely due to one of the issues listed in that error message. You'll need to go through them one by one, modifying things to rule them out.
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blacklisting nouveau is frequently not enough. Often it is necessary to remove it from the initrd image as well.
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as root:
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echo -e "blacklist nouveau\noptions nouveau modeset=0" > /etc/modprobe.d/disable-nouveau.conf
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update-initramfs -u
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如果提示command not found，似乎没有关系，重启机器即可。
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It looks like you did something like the first line already - that is the blacklist. The second line will remove it from the initrd image.
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Section 8.1 of the driver README document discusses nouveau:
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http://us.download.nvidia.com/XFree86/Linux-x86_64/340.24/README/index.html
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@ -1,26 +1,21 @@
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#!/bin/bash
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# Example: NUM=100 bash sbin/run_agent_mock.sh
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# docker ps -a | grep yao-agent:mock | awk '{print $1}' | xargs docker rm -f
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for ((i=1; i<=${NUM}; i++));
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do
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docker run \
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--name yao-agent-mock-$i \
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--name yao-agent-mock-$(hostname) \
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--network yao-net \
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--network-alias node-mock-$i \
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--hostname node-mock-$i \
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--network-alias node-mock-$(hostname) \
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--hostname node-mock-$(hostname) \
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-d \
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--restart always \
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--detach=true \
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--env NUMS=100 \
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--env ClientHost=$(hostname) \
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--env HeartbeatInterval=5 \
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--env NUMS=${NUM} \
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--env ClientHost=node-mock-$(hostname) \
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--env ReportAddress='http://yao-scheduler:8080/?action=agent_report' \
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--env PYTHONUNBUFFERED=1 \
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--mount type=bind,source=/etc/localtime,target=/etc/localtime,readonly \
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--mount type=bind,src=/var/run/docker.sock,dst=/var/run/docker.sock \
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quickdeploy/yao-agent:mock
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# reduce instant load
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sleep 1
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done
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