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GPU

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。
# Install docker-ce
curl https://get.docker.com | sh \
&& sudo systemctl --now enable docker
# Add the package repositories
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
&& curl -s -L https://nvidia.github.io/libnvidia-container/experimental/$distribution/libnvidia-container.list | \
sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
# Install nvidia-docker2 and reload the Docker daemon configuration
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
# Test nvidia-smi with the latest official CUDA image
sudo docker run --rm --gpus all nvidia/cuda:11.6.2-base-ubuntu20.04 nvidia-smi
部署 NVDIA 设备插件
kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.13.0/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://github.com/GoogleCloudPlatform/container-engine-accelerators/raw/master/daemonset.yaml
# Install NVIDIA drivers on Ubuntu (experimental):
kubectl create -f https://github.com/GoogleCloudPlatform/container-engine-accelerators/raw/master/nvidia-driver-installer/ubuntu/daemonset.yaml
# Install the device plugin:
kubectl create -f https://github.com/kubernetes/kubernetes/raw/master/cluster/addons/device-plugins/nvidia-gpu/daemonset.yaml

NVIDIA GPU Operator

Nvidia GPU Operator 是一个 Kubernetes Operator,用于在 Kubernetes 集群中部署和管理 Nvidia GPU。
helm install --wait --generate-name \
-n gpu-operator --create-namespace \
nvidia/gpu-operator

请求 nvidia.com/gpu 资源示例

$ cat <<EOF | kubectl apply -f -
apiVersion: v1
kind: Pod
metadata:
name: gpu-pod
spec:
restartPolicy: Never
containers:
- name: cuda-container
image: nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda10.2
resources:
limits:
nvidia.com/gpu: 1 # requesting 1 GPU
tolerations:
- key: nvidia.com/gpu
operator: Exists
effect: NoSchedule
EOF

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