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7. 实践案例 - GPU

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2023-12-01

Kubernetes 支持容器请求 GPU 资源（目前仅支持 NVIDIA GPU），在深度学习等场景中有大量应用。

使用方法

Kubernetes v1.8 及更新版本

从 Kubernetes v1.8 开始，GPU 开始以 DevicePlugin 的形式实现。在使用之前需要配置

  • kubelet/kube-apiserver/kube-controller-manager: --feature-gates="DevicePlugins=true"
  • 在所有的 Node 上安装 Nvidia 驱动，包括 NVIDIA Cuda Toolkit 和 cuDNN 等
  • Kubelet 配置使用 docker 容器引擎（默认就是 docker），其他容器引擎暂不支持该特性

NVIDIA 插件

NVIDIA 需要 nvidia-docker。

安装 nvidia-docker

  1. # Install docker-ce
  2. sudo apt-get install 
  3.     apt-transport-https 
  4.     ca-certificates 
  5.     curl 
  6.     software-properties-common
  7. curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
  8. sudo add-apt-repository 
  9.    "deb [arch=amd64] https://download.docker.com/linux/ubuntu 
  10.    $(lsb_release -cs) 
  11.    stable"
  12. sudo apt-get update
  13. sudo apt-get install docker-ce
  15. # Add the package repositories
  16. curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | 
  17.   sudo apt-key add -
  18. curl -s -L https://nvidia.github.io/nvidia-docker/ubuntu16.04/amd64/nvidia-docker.list | 
  19.   sudo tee /etc/apt/sources.list.d/nvidia-docker.list
  20. sudo apt-get update
  22. # Install nvidia-docker2 and reload the Docker daemon configuration
  23. sudo apt-get install -y nvidia-docker2
  24. sudo pkill -SIGHUP dockerd
  26. # Test nvidia-smi with the latest official CUDA image
  27. docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi

设置 Docker 默认运行时为 nvidia

  1. # cat /etc/docker/daemon.json
  2. {
  3.     "default-runtime": "nvidia",
  4.     "runtimes": {
  5.         "nvidia": {
  6.             "path": "/usr/bin/nvidia-container-runtime",
  7.             "runtimeArgs": []
  8.         }
  9.     }
  10. }

部署 NVDIA 设备插件

  1. # For Kubernetes v1.8
  2. kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v1.8/nvidia-device-plugin.yml
  4. # For Kubernetes v1.9
  5. kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v1.9/nvidia-device-plugin.yml

GCE/GKE GPU 插件

该插件不需要 nvidia-docker，并且也支持 CRI 容器运行时。

  1. # Install NVIDIA drivers on Container-Optimized OS:
  2. kubectl create -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/k8s-1.9/daemonset.yaml
  4. # Install NVIDIA drivers on Ubuntu (experimental):
  5. kubectl create -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/k8s-1.9/nvidia-driver-installer/ubuntu/daemonset.yaml
  7. # Install the device plugin:
  8. kubectl create -f https://raw.githubusercontent.com/kubernetes/kubernetes/release-1.9/cluster/addons/device-plugins/nvidia-gpu/daemonset.yaml

请求 nvidia.com/gpu 资源示例

  1. apiVersion: v1
  2. kind: Pod
  3. metadata:
  4.   name: cuda-vector-add
  5. spec:
  6.   restartPolicy: OnFailure
  7.   containers:
  8.     - name: cuda-vector-add
  9.       # https://github.com/kubernetes/kubernetes/blob/v1.7.11/test/images/nvidia-cuda/Dockerfile
  10.       image: "k8s.gcr.io/cuda-vector-add:v0.1"
  11.       resources:
  12.         limits:
  13.           nvidia.com/gpu: 1 # requesting 1 GPU

Kubernetes v1.6 和 v1.7

在 Kubernetes v1.6 和 v1.7 中使用 GPU 需要预先配置

  • 在所有的 Node 上安装 Nvidia 驱动，包括 NVIDIA Cuda Toolkit 和 cuDNN 等
  • 在 apiserver 和 kubelet 上开启 --feature-gates="Accelerators=true"
  • Kubelet 配置使用 docker 容器引擎（默认就是 docker），其他容器引擎暂不支持该特性

使用资源名 alpha.kubernetes.io/nvidia-gpu 指定请求 GPU 的个数，如

  1. apiVersion: v1
  2. kind: Pod
  3. metadata:
  4.   name: tensorflow
  5. spec:
  6.   restartPolicy: Never
  7.   containers:
  8.   - image: gcr.io/tensorflow/tensorflow:latest-gpu
  9.     name: gpu-container-1
  10.     command: ["python"]
  11.     env:
  12.     - name: LD_LIBRARY_PATH
  13.       value: /usr/lib/nvidia
  14.     args:
  15.     - -u
  16.     - -c
  17.     - from tensorflow.python.client import device_lib; print device_lib.list_local_devices()
  18.     resources:
  19.       limits:
  20.         alpha.kubernetes.io/nvidia-gpu: 1 # requests one GPU
  21.     volumeMounts:
  22.     - mountPath: /usr/local/nvidia/bin
  23.       name: bin
  24.     - mountPath: /usr/lib/nvidia
  25.       name: lib
  26.     - mountPath: /usr/lib/x86_64-linux-gnu/libcuda.so
  27.       name: libcuda-so
  28.     - mountPath: /usr/lib/x86_64-linux-gnu/libcuda.so.1
  29.       name: libcuda-so-1
  30.     - mountPath: /usr/lib/x86_64-linux-gnu/libcuda.so.375.66
  31.       name: libcuda-so-375-66
  32.   volumes:
  33.     - name: bin
  34.       hostPath:
  35.         path: /usr/lib/nvidia-375/bin
  36.     - name: lib
  37.       hostPath:
  38.         path: /usr/lib/nvidia-375
  39.     - name: libcuda-so
  40.       hostPath:
  41.         path: /usr/lib/x86_64-linux-gnu/libcuda.so
  42.     - name: libcuda-so-1
  43.       hostPath:
  44.         path: /usr/lib/x86_64-linux-gnu/libcuda.so.1
  45.     - name: libcuda-so-375-66
  46.       hostPath:
  47.         path: /usr/lib/x86_64-linux-gnu/libcuda.so.375.66
  1. $ kubectl create -f pod.yaml
  2. pod "tensorflow" created
  4. $ kubectl logs tensorflow
  5. ...
  6. [name: "/cpu:0"
  7. device_type: "CPU"
  8. memory_limit: 268435456
  9. locality {
  10. }
  11. incarnation: 9675741273569321173
  12. , name: "/gpu:0"
  13. device_type: "GPU"
  14. memory_limit: 11332668621
  15. locality {
  16.   bus_id: 1
  17. }
  18. incarnation: 7807115828340118187
  19. physical_device_desc: "device: 0, name: Tesla K80, pci bus id: 0000:00:04.0"
  20. ]

注意

  • GPU 资源必须在 resources.limits 中请求，resources.requests 中无效
  • 容器可以请求 1 个或多个 GPU，不能只请求一部分
  • 多个容器之间不能共享 GPU
  • 默认假设所有 Node 安装了相同型号的 GPU

多种型号的 GPU

如果集群 Node 中安装了多种型号的 GPU，则可以使用 Node Affinity 来调度 Pod 到指定 GPU 型号的 Node 上。

首先，在集群初始化时，需要给 Node 打上 GPU 型号的标签

  1. # Label your nodes with the accelerator type they have.
  2. kubectl label nodes <node-with-k80> accelerator=nvidia-tesla-k80
  3. kubectl label nodes <node-with-p100> accelerator=nvidia-tesla-p100

然后，在创建 Pod 时设置 Node Affinity：

  1. apiVersion: v1
  2. kind: Pod
  3. metadata:
  4.   name: cuda-vector-add
  5. spec:
  6.   restartPolicy: OnFailure
  7.   containers:
  8.     - name: cuda-vector-add
  9.       # https://github.com/kubernetes/kubernetes/blob/v1.7.11/test/images/nvidia-cuda/Dockerfile
  10.       image: "k8s.gcr.io/cuda-vector-add:v0.1"
  11.       resources:
  12.         limits:
  13.           nvidia.com/gpu: 1
  14.   nodeSelector:
  15.     accelerator: nvidia-tesla-p100 # or nvidia-tesla-k80 etc.

使用 CUDA 库

NVIDIA Cuda Toolkit 和 cuDNN 等需要预先安装在所有 Node 上。为了访问 /usr/lib/nvidia-375，需要将 CUDA 库以 hostPath volume 的形式传给容器：

  1. apiVersion: batch/v1
  2. kind: Job
  3. metadata:
  4.   name: nvidia-smi
  5.   labels:
  6.     name: nvidia-smi
  7. spec:
  8.   template:
  9.     metadata:
  10.       labels:
  11.         name: nvidia-smi
  12.     spec:
  13.       containers:
  14.       - name: nvidia-smi
  15.         image: nvidia/cuda
  16.         command: ["nvidia-smi"]
  17.         imagePullPolicy: IfNotPresent
  18.         resources:
  19.           limits:
  20.             alpha.kubernetes.io/nvidia-gpu: 1
  21.         volumeMounts:
  22.         - mountPath: /usr/local/nvidia/bin
  23.           name: bin
  24.         - mountPath: /usr/lib/nvidia
  25.           name: lib
  26.       volumes:
  27.         - name: bin
  28.           hostPath:
  29.             path: /usr/lib/nvidia-375/bin
  30.         - name: lib
  31.           hostPath:
  32.             path: /usr/lib/nvidia-375
  33.       restartPolicy: Never
  1. $ kubectl create -f job.yaml
  2. job "nvidia-smi" created
  4. $ kubectl get job
  5. NAME         DESIRED   SUCCESSFUL   AGE
  6. nvidia-smi   1         1            14m
  8. $ kubectl get pod -a
  9. NAME               READY     STATUS      RESTARTS   AGE
  10. nvidia-smi-kwd2m   0/1       Completed   0          14m
  12. $ kubectl logs nvidia-smi-kwd2m
  13. Fri Jun 16 19:49:53 2017
  14. +-----------------------------------------------------------------------------+
  15. | NVIDIA-SMI 375.66                 Driver Version: 375.66                    |
  16. |-------------------------------+----------------------+----------------------+
  17. | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
  18. | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
  19. |===============================+======================+======================|
  20. |   0  Tesla K80           Off  | 0000:00:04.0     Off |                    0 |
  21. | N/A   74C    P0    80W / 149W |      0MiB / 11439MiB |    100%      Default |
  22. +-------------------------------+----------------------+----------------------+
  24. +-----------------------------------------------------------------------------+
  25. | Processes:                                                       GPU Memory |
  26. |  GPU       PID  Type  Process name                               Usage      |
  27. |=============================================================================|
  28. |  No running processes found                                                 |
  29. +-----------------------------------------------------------------------------+

附录：CUDA 安装方法

安装 CUDA：

  1. # Check for CUDA and try to install.
  2. if ! dpkg-query -W cuda; then
  3.   # The 16.04 installer works with 16.10.
  4.   curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
  5.   dpkg -i ./cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
  6.   apt-get update
  7.   apt-get install cuda -y
  8. fi

安装 cuDNN：

首先到网站 https://developer.nvidia.com/cudnn 注册，并下载 cuDNN v5.1，然后运行命令安装

  1. tar zxvf cudnn-8.0-linux-x64-v5.1.tgz
  2. ln -s /usr/local/cuda-8.0 /usr/local/cuda
  3. sudo cp -P cuda/include/cudnn.h /usr/local/cuda/include
  4. sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64
  5. sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

安装完成后，可以运行 nvidia-smi 查看 GPU 设备的状态

  1. $ nvidia-smi
  2. Fri Jun 16 19:33:35 2017
  3. +-----------------------------------------------------------------------------+
  4. | NVIDIA-SMI 375.66                 Driver Version: 375.66                    |
  5. |-------------------------------+----------------------+----------------------+
  6. | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
  7. | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
  8. |===============================+======================+======================|
  9. |   0  Tesla K80           Off  | 0000:00:04.0     Off |                    0 |
  10. | N/A   74C    P0    80W / 149W |      0MiB / 11439MiB |    100%      Default |
  11. +-------------------------------+----------------------+----------------------+
  13. +-----------------------------------------------------------------------------+
  14. | Processes:                                                       GPU Memory |
  15. |  GPU       PID  Type  Process name                               Usage      |
  16. |=============================================================================|
  17. |  No running processes found                                                 |
  18. +-----------------------------------------------------------------------------+

参考文档

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