GPU

Last updated 3 days ago

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

# Install docker-ce
sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
sudo apt-get update
sudo apt-get install docker-ce
# Add the package repositories
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \
sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/ubuntu16.04/amd64/nvidia-docker.list | \
sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
# Install nvidia-docker2 and reload the Docker daemon configuration
sudo apt-get install -y nvidia-docker2
sudo pkill -SIGHUP dockerd
# Test nvidia-smi with the latest official CUDA image
docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi

设置 Docker 默认运行时为 nvidia

# cat /etc/docker/daemon.json
{
"default-runtime": "nvidia",
"runtimes": {
"nvidia": {
"path": "/usr/bin/nvidia-container-runtime",
"runtimeArgs": []
}
}
}

部署 NVDIA 设备插件

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

# Install NVIDIA drivers on Container-Optimized OS:
kubectl create -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/k8s-1.9/daemonset.yaml
# Install NVIDIA drivers on Ubuntu (experimental):
kubectl create -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/k8s-1.9/nvidia-driver-installer/ubuntu/daemonset.yaml
# Install the device plugin:
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

apiVersion: v1
kind: Pod
metadata:
name: cuda-vector-add
spec:
restartPolicy: OnFailure
containers:
- name: cuda-vector-add
# https://github.com/kubernetes/kubernetes/blob/v1.7.11/test/images/nvidia-cuda/Dockerfile
image: "k8s.gcr.io/cuda-vector-add:v0.1"
resources:
limits:
nvidia.com/gpu: 1 # requesting 1 GPU

Kubernetes v1.6 和 v1.7

alpha.kubernetes.io/nvidia-gpu 已在 v1.10 中删除,新版本请使用 nvidia.com/gpu

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

apiVersion: v1
kind: Pod
metadata:
name: tensorflow
spec:
restartPolicy: Never
containers:
- image: gcr.io/tensorflow/tensorflow:latest-gpu
name: gpu-container-1
command: ["python"]
env:
- name: LD_LIBRARY_PATH
value: /usr/lib/nvidia
args:
- -u
- -c
- from tensorflow.python.client import device_lib; print device_lib.list_local_devices()
resources:
limits:
alpha.kubernetes.io/nvidia-gpu: 1 # requests one GPU
volumeMounts:
- mountPath: /usr/local/nvidia/bin
name: bin
- mountPath: /usr/lib/nvidia
name: lib
- mountPath: /usr/lib/x86_64-linux-gnu/libcuda.so
name: libcuda-so
- mountPath: /usr/lib/x86_64-linux-gnu/libcuda.so.1
name: libcuda-so-1
- mountPath: /usr/lib/x86_64-linux-gnu/libcuda.so.375.66
name: libcuda-so-375-66
volumes:
- name: bin
hostPath:
path: /usr/lib/nvidia-375/bin
- name: lib
hostPath:
path: /usr/lib/nvidia-375
- name: libcuda-so
hostPath:
path: /usr/lib/x86_64-linux-gnu/libcuda.so
- name: libcuda-so-1
hostPath:
path: /usr/lib/x86_64-linux-gnu/libcuda.so.1
- name: libcuda-so-375-66
hostPath:
path: /usr/lib/x86_64-linux-gnu/libcuda.so.375.66
$ kubectl create -f pod.yaml
pod "tensorflow" created
$ kubectl logs tensorflow
...
[name: "/cpu:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 9675741273569321173
, name: "/gpu:0"
device_type: "GPU"
memory_limit: 11332668621
locality {
bus_id: 1
}
incarnation: 7807115828340118187
physical_device_desc: "device: 0, name: Tesla K80, pci bus id: 0000:00:04.0"
]

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

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

然后,在创建 Pod 时设置 Node Affinity:

apiVersion: v1
kind: Pod
metadata:
name: cuda-vector-add
spec:
restartPolicy: OnFailure
containers:
- name: cuda-vector-add
# https://github.com/kubernetes/kubernetes/blob/v1.7.11/test/images/nvidia-cuda/Dockerfile
image: "k8s.gcr.io/cuda-vector-add:v0.1"
resources:
limits:
nvidia.com/gpu: 1
nodeSelector:
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:

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

附录:CUDA 安装方法

安装 CUDA:

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

tar zxvf cudnn-8.0-linux-x64-v5.1.tgz
ln -s /usr/local/cuda-8.0 /usr/local/cuda
sudo cp -P cuda/include/cudnn.h /usr/local/cuda/include
sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64
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

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

参考文档