在本文中我将展示如何将Jetson Nano开发板连接到Kubernetes集群以作为一个GPU节点。我将介绍使用GPU运行容器所需的NVIDIA docker设置,以及将Jetson连接到Kubernetes集群。在成功将节点连接到集群后,我还将展示如何在Jetson Nano上使用GPU运行简单的TensorFlow 2训练会话。
K3s还是K8s?
K3s是一个轻量级Kubernetes发行版,其大小不超过100MB。在我看来,它是单板计算机的理想选择,因为它所需的资源明显减少。你可以查看我们的往期文章,了解更多关于K3s的教程和生态。在K3s生态中,有一款不得不提的开源工具K3sup,这是由Alex Ellis开发的,用于简化K3s集群安装。你可以访问Github了解这款工具:
https://github.com/alexellis/k3sup
我们需要准备什么?
计划步骤
设置NVIDIA docker
在我们配置Docker以使用nvidia-docker作为默认的运行时之前,我需要先解释一下为什么要这样做。默认情况下,当用户在Jetson Nano上运行容器时,运行方式与其他硬件设备相同,你不能从容器中访问GPU,至少在没有黑客攻击的情况下不能。如果你想自己测试一下,你可以运行以下命令,应该会看到类似的结果:
root@jetson:~# echo "python3 -c 'import tensorflow'" | docker run -i icetekio/jetson-nano-tensorflow /bin/bash
2020-05-14 00:10:23.370761: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libcudart.so.10.2'; dlerror: libcudart.so.10.2: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-10.2/targets/aarch64-linux/lib:
2020-05-14 00:10:23.370859: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2020-05-14 00:10:25.946896: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-10.2/targets/aarch64-linux/lib:
2020-05-14 00:10:25.947219: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-10.2/targets/aarch64-linux/lib:
2020-05-14 00:10:25.947273: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
/usr/lib/python3/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
如果你现在尝试运行相同的命令,但在docker命令中添--runtime=nvidia参数,你应该看到类似以下的内容:
root@jetson:~# echo "python3 -c 'import tensorflow'" | docker run --runtime=nvidia -i icetekio/jetson-nano-tensorflow /bin/bash
2020-05-14 00:12:16.767624: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.2
2020-05-14 00:12:19.386354: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libnvinfer.so.7
2020-05-14 00:12:19.388700: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libnvinfer_plugin.so.7
/usr/lib/python3/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
nvidia-docker已经配置完成,但是默认情况下并没有启用。要启用docker运行nvidia-docker运行时作为默认值,需要将"default-runtime":"nvidia"添加到/etc/docker/daemon.json配置文件中,如下所示:
{
"runtimes": {
"nvidia": {
"path": "nvidia-container-runtime",
"runtimeArgs": []
}
},
"default-runtime": "nvidia"
}
现在你可以跳过docker run命令中--runtime=nvidia参数,GPU将被默认初始化。这样K3s就会用nvidia-docker运行时来使用Docker,让Pod不需要任何特殊配置就能使用GPU。
将Jetson作为K8S节点连接
使用K3sup将Jetson作为Kubernetes节点连接只需要1个命令,然而要想成功连接Jetson和master节点,我们需要能够在没有密码的情况下同时连接到Jetson和master节点,并且在没有密码的情况下做sudo,或者以root用户的身份连接。
如果你需要生成SSH 密钥并复制它们,你需要运行以下命令:
ssh-keygen -t rsa -b 4096 -f ~/.ssh/rpi -P ""
ssh-copy-id -i .ssh/rpi user@host
默认情况下,Ubuntu安装要求用户在使用sudo命令时输入密码,因此,更简单的方法是用root账户来使用K3sup。要使这个方法有效,需要将你的~/.ssh/authorized_keys复制到/root/.ssh/目录下。
在连接Jetson之前,我们查看一下想要连接到的集群:
upgrade@ZeroOne:~$ kubectl get node -o wide
NAME STATUS ROLES AGE VERSION INTERNAL-IP EXTERNAL-IP OS-IMAGE KERNEL-VERSION CONTAINER-RUNTIME
nexus Ready master 32d v1.17.2+k3s1 192.168.0.12 <none> Ubuntu 18.04.4 LTS 4.15.0-96-generic containerd://1.3.3-k3s1
rpi3-32 Ready <none> 32d v1.17.2+k3s1 192.168.0.30 <none> Ubuntu 18.04.4 LTS 5.3.0-1022-raspi2 containerd://1.3.3-k3s1
rpi3-64 Ready <none> 32d v1.17.2+k3s1 192.168.0.32 <none> Ubuntu 18.04.4 LTS 5.3.0-1022-raspi2 containerd://1.3.3-k3s1
你可能会注意到,master节点是一台IP为192.168.0.12的nexus主机,它正在运行containerd。默认状态下,k3s会将containerd作为运行时,但这是可以修改的。由于我们设置了nvidia-docker与docker一起运行,我们需要修改containerd。无需担心,将containerd修改为Docker我们仅需传递一个额外的参数到k3sup命令即可。所以,运行以下命令即可连接Jetson到集群:
k3sup join --ssh-key ~/.ssh/rpi --server-ip 192.168.0.12 --ip 192.168.0.40 --k3s-extra-args '--docker'
IP 192.168.0.40是我的Jetson Nano。正如你所看到的,我们传递了--k3s-extra-args'--docker'标志,在安装k3s agent 时,将--docker标志传递给它。多亏如此,我们使用的是nvidia-docker设置的docker,而不是containerd。
要检查节点是否正确连接,我们可以运行kubectl get node -o wide:
upgrade@ZeroOne:~$ kubectl get node -o wide
NAME STATUS ROLES AGE VERSION INTERNAL-IP EXTERNAL-IP OS-IMAGE KERNEL-VERSION CONTAINER-RUNTIME
nexus Ready master 32d v1.17.2+k3s1 192.168.0.12 <none> Ubuntu 18.04.4 LTS 4.15.0-96-generic containerd://1.3.3-k3s1
rpi3-32 Ready <none> 32d v1.17.2+k3s1 192.168.0.30 <none> Ubuntu 18.04.4 LTS 5.3.0-1022-raspi2 containerd://1.3.3-k3s1
rpi3-64 Ready <none> 32d v1.17.2+k3s1 192.168.0.32 <none> Ubuntu 18.04.4 LTS 5.3.0-1022-raspi2 containerd://1.3.3-k3s1
jetson Ready <none> 11s v1.17.2+k3s1 192.168.0.40 <none> Ubuntu 18.04.4 LTS 4.9.140-tegra docker://19.3.6
简易验证
我们现在可以使用相同的docker镜像和命令来运行pod,以检查是否会有与本文开头在Jetson Nano上运行docker相同的结果。要做到这一点,我们可以应用这个pod规范:
apiVersion: v1
kind: Pod
metadata:
name: gpu-test
spec:
nodeSelector:
kubernetes.io/hostname: jetson
containers:
- image: icetekio/jetson-nano-tensorflow
name: gpu-test
command:
- "/bin/bash"
- "-c"
- "echo 'import tensorflow' | python3"
restartPolicy: Never
等待docker镜像拉取,然后通过运行以下命令查看日志:
upgrade@ZeroOne:~$ kubectl logs gpu-test
2020-05-14 10:01:51.341661: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.2
2020-05-14 10:01:53.996300: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libnvinfer.so.7
2020-05-14 10:01:53.998563: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libnvinfer_plugin.so.7
/usr/lib/python3/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
如你所见,我们的日志信息与之前在Jetson上运行Docker相似。
运行MNIST训练
我们有一个支持GPU的运行节点,所以现在我们可以测试出机器学习的 "Hello world",并使用MNIST数据集运行TensorFlow 2模型示例。
要运行一个简单的训练会话,以证明GPU的使用情况,应用下面的manifest:
apiVersion: v1
kind: Pod
metadata:
name: mnist-training
spec:
nodeSelector:
kubernetes.io/hostname: jetson
initContainers:
- name: git-clone
image: iceci/utils
command:
- "git"
- "clone"
- "<https://github.com/IceCI/example-mnist-training.git>"
- "/workspace"
volumeMounts:
- mountPath: /workspace
name: workspace
containers:
- image: icetekio/jetson-nano-tensorflow
name: mnist
command:
- "python3"
- "/workspace/mnist.py"
volumeMounts:
- mountPath: /workspace
name: workspace
restartPolicy: Never
volumes:
- name: workspace
emptyDir: {}
从下面的日志中可以看到,GPU正在运行:
...
2020-05-14 11:30:02.846289: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-05-14 11:30:02.846434: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.2
....
如果你在节点上,你可以通过运行tegrastats命令来测试CPU和GPU的使用情况:
upgrade@jetson:~$ tegrastats --interval 5000
RAM 2462/3964MB (lfb 2x4MB) SWAP 362/1982MB (cached 6MB) CPU [52%@1479,41%@1479,43%@1479,34%@1479] EMC_FREQ 0% GR3D_FREQ 9% PLL@23.5C CPU@26C PMIC@100C GPU@24C AO@28.5C thermal@25C POM_5V_IN 3410/3410 POM_5V_GPU 451/451 POM_5V_CPU 1355/1355
RAM 2462/3964MB (lfb 2x4MB) SWAP 362/1982MB (cached 6MB) CPU [53%@1479,42%@1479,45%@1479,35%@1479] EMC_FREQ 0% GR3D_FREQ 9% PLL@23.5C CPU@26C PMIC@100C GPU@24C AO@28.5C thermal@24.75C POM_5V_IN 3410/3410 POM_5V_GPU 451/451 POM_5V_CPU 1353/1354
RAM 2461/3964MB (lfb 2x4MB) SWAP 362/1982MB (cached 6MB) CPU [52%@1479,38%@1479,43%@1479,33%@1479] EMC_FREQ 0% GR3D_FREQ 10% PLL@24C CPU@26C PMIC@100C GPU@24C AO@29C thermal@25.25C POM_5V_IN 3410/3410 POM_5V_GPU 493/465 POM_5V_CPU 1314/1340
总 结
如你所见,将Jetson Nano连接到Kubernetes集群是一个非常简单的过程。只需几分钟,你就能利用Kubernetes来运行机器学习工作负载——同时也能利用NVIDIA袖珍GPU的强大功能。你将能够在Kubernetes上运行任何为Jetson Nano设计的GPU容器,这可以简化你的开发和测试。
作者:
Jakub Czapliński,Icetek编辑
原文链接:
https://medium.com/icetek/how-to-connect-jetson-nano-to-kubernetes-using-k3s-and-k3sup-c715cf2bf212
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About k3s
k3s 是首个进入 CNCF 沙箱项目的 K8S 发行版,同时也是当前全球用户量最大的 CNCF 认证轻量级 K8S 发行版。自2019年3月发布以来,备受全球开发者们关注,至今GitHub Star数已超过 14,600,成为了开源社区最受欢迎的边缘计算 K8S 解决方案。截至目前,K3s全球下载量超过100万次,每周平均被安装超过2万次,其中30%的下载量来自中国。
k3s 专为在资源有限的环境中运行 Kubernetes 的研发和运维人员设计,将满足日益增长的在边缘计算环境中运行在 x86、ARM64 和 ARMv7 处理器上的小型、易于管理的 Kubernetes 集群需求。k3s 的发布,为开发者们提供了以“Rancher 2.X + k3s”为核心的从数据中心到云到边到端的 K8S 即服务(Kubernetes-as-a-Service),推动 Kubernetes Everywhere。