作者:运维有术星主
随着人工智能、机器学习、AI 大模型技术的迅猛发展,我们对计算资源的需求也在不断攀升。特别是对于需要处理大规模数据和复杂算法的 AI 大模型,GPU 资源的使用变得至关重要。对于运维工程师而言,掌握如何在 Kubernetes 集群上管理和配置 GPU 资源,以及如何高效部署依赖这些资源的应用,已成为一项不可或缺的技能。
今天,我将带领大家深入了解如何在 KubeSphere 平台上,利用 Kubernetes 强大的生态和工具,实现 GPU 资源的管理和应用部署。以下是本文将要探讨的三个核心主题:
通过阅读本文,您将获得 Kubernetes 上 管理 GPU 资源的知识和技巧,帮助您在云原生环境中,充分利用 GPU 资源,推动 AI 应用的快速发展。
KubeSphere 最佳实战「2024」 系列文档的实验环境硬件配置和软件信息如下:
实战服务器配置(架构1:1复刻小规模生产环境,配置略有不同)
主机名 | IP | CPU | 内存 | 系统盘 | 数据盘 | 用途 |
---|---|---|---|---|---|---|
ksp-registry | 192.168.9.90 | 4 | 8 | 40 | 200 | Harbor 镜像仓库 |
ksp-control-1 | 192.168.9.91 | 4 | 8 | 40 | 100 | KubeSphere/k8s-control-plane |
ksp-control-2 | 192.168.9.92 | 4 | 8 | 40 | 100 | KubeSphere/k8s-control-plane |
ksp-control-3 | 192.168.9.93 | 4 | 8 | 40 | 100 | KubeSphere/k8s-control-plane |
ksp-worker-1 | 192.168.9.94 | 4 | 16 | 40 | 100 | k8s-worker/CI |
ksp-worker-2 | 192.168.9.95 | 4 | 16 | 40 | 100 | k8s-worker |
ksp-worker-3 | 192.168.9.96 | 4 | 16 | 40 | 100 | k8s-worker |
ksp-storage-1 | 192.168.9.97 | 4 | 8 | 40 | 300+ | ElasticSearch/Ceph/Longhorn/NFS/ |
ksp-storage-2 | 192.168.9.98 | 4 | 8 | 40 | 300+ | ElasticSearch//Ceph/Longhorn |
ksp-storage-3 | 192.168.9.99 | 4 | 8 | 40 | 300+ | ElasticSearch//Ceph/Longhorn |
ksp-gpu-worker-1 | 192.168.9.101 | 4 | 16 | 40 | 100 | k8s-worker(GPU NVIDIA Tesla M40 24G) |
ksp-gpu-worker-2 | 192.168.9.102 | 4 | 16 | 40 | 100 | k8s-worker(GPU NVIDIA Tesla P100 16G) |
ksp-gateway-1 | 192.168.9.103 | 2 | 4 | 40 | 自建应用服务代理网关/VIP:192.168.9.100 | |
ksp-gateway-2 | 192.168.9.104 | 2 | 4 | 40 | 自建应用服务代理网关/VIP:192.168.9.100 | |
ksp-mid | 192.168.9.105 | 4 | 8 | 40 | 100 | 部署在 k8s 集群之外的服务节点(Gitlab 等) |
合计 | 15 | 56 | 152 | 600 | 2000 |
实战环境涉及软件版本信息
鉴于资源和成本的限制,我没有高端物理主机和显卡来做实验。只能增加两台配备入门级 GPU 显卡的虚拟机,作为集群的 Worker 节点。
尽管这些显卡在性能上不及高端型号,但它们足以应对大多数学习和开发任务,在资源有限的情况下,这样的配置为我提供了宝贵的实践机会,让我能够深入探索 Kubernetes 集群中 GPU 资源的管理和调度策略。
请参考 Kubernetes 集群节点 openEuler 22.03 LTS SP3 系统初始化指南,完成操作系统初始化配置。
初始化配置指南中没有涉及操作系统升级的任务,在能联网的环境初始化系统的时候一定要升级操作系统,然后重启节点。
接下来我们使用 KubeKey 将新增加的 GPU 节点加入到已有的 Kubernetes 集群,参考官方说明文档,整个过程比较简单,仅需两步。
在 Control-1 节点,切换到部署用的 kubekey 目录,修改原有的集群配置文件,我们实战中使用的名字为 ksp-v341-v1288.yaml,请根据实际情况修改 。
主要修改点:
修改后的示例如下:
apiVersion: kubekey.kubesphere.io/v1alpha2 kind: Cluster metadata: name: opsxlab spec: hosts: ......(保持不变) - {name: ksp-gpu-worker-1, address: 192.168.9.101, internalAddress: 192.168.9.101, user: root, password: "OpsXlab@2024"} - {name: ksp-gpu-worker-2, address: 192.168.9.102, internalAddress: 192.168.9.102, user: root, password: "OpsXlab@2024"} roleGroups: ......(保持不变) worker: ......(保持不变) - ksp-gpu-worker-1 - ksp-gpu-worker-2 # 下面的内容保持不变
在增加节点之前,我们再确认一下当前集群的节点信息。
$ kubectl get nodes -o wide NAME STATUS ROLES AGE VERSION INTERNAL-IP EXTERNAL-IP OS-IMAGE KERNEL-VERSION CONTAINER-RUNTIME ksp-control-1 Ready control-plane 24h v1.28.8 192.168.9.91 <none> openEuler 22.03 (LTS-SP3) 5.10.0-182.0.0.95.oe2203sp3.x86_64 containerd://1.7.13 ksp-control-2 Ready control-plane 24h v1.28.8 192.168.9.92 <none> openEuler 22.03 (LTS-SP3) 5.10.0-182.0.0.95.oe2203sp3.x86_64 containerd://1.7.13 ksp-control-3 Ready control-plane 24h v1.28.8 192.168.9.93 <none> openEuler 22.03 (LTS-SP3) 5.10.0-182.0.0.95.oe2203sp3.x86_64 containerd://1.7.13 ksp-worker-1 Ready worker 24h v1.28.8 192.168.9.94 <none> openEuler 22.03 (LTS-SP3) 5.10.0-182.0.0.95.oe2203sp3.x86_64 containerd://1.7.13 ksp-worker-2 Ready worker 24h v1.28.8 192.168.9.95 <none> openEuler 22.03 (LTS-SP3) 5.10.0-182.0.0.95.oe2203sp3.x86_64 containerd://1.7.13 ksp-worker-3 Ready worker 24h v1.28.8 192.168.9.96 <none> openEuler 22.03 (LTS-SP3) 5.10.0-182.0.0.95.oe2203sp3.x86_64 containerd://1.7.13
接下来我们执行下面的命令,使用修改后的配置文件将新增的 Worker 节点加入集群。
export KKZONE=cn ./kk add nodes -f ksp-v341-v1288.yaml
上面的命令执行后,KubeKey 先检查部署 Kubernetes 的依赖及其它配置是否符合要求。通过检查后,系统将提示您确认安装。输入 yes 并按 ENTER 继续部署。
部署完成需要大约 5 分钟左右,具体时间看网速、机器配置、增加的节点数量。
部署完成后,您应该会在终端上看到类似于下面的输出。
...... 19:29:26 CST [AutoRenewCertsModule] Generate k8s certs renew script 19:29:27 CST success: [ksp-control-2] 19:29:27 CST success: [ksp-control-1] 19:29:27 CST success: [ksp-control-3] 19:29:27 CST [AutoRenewCertsModule] Generate k8s certs renew service 19:29:29 CST success: [ksp-control-3] 19:29:29 CST success: [ksp-control-2] 19:29:29 CST success: [ksp-control-1] 19:29:29 CST [AutoRenewCertsModule] Generate k8s certs renew timer 19:29:30 CST success: [ksp-control-2] 19:29:30 CST success: [ksp-control-1] 19:29:30 CST success: [ksp-control-3] 19:29:30 CST [AutoRenewCertsModule] Enable k8s certs renew service 19:29:30 CST success: [ksp-control-3] 19:29:30 CST success: [ksp-control-2] 19:29:30 CST success: [ksp-control-1] 19:29:30 CST Pipeline[AddNodesPipeline] execute successfully
我们打开浏览器访问 Control-1 节点的 IP 地址和端口 30880,登陆 KubeSphere 管理控制台的登录页面。
进入集群管理界面,单击左侧「节点」菜单,点击「集群节点」查看 Kubernetes 集群可用节点的详细信息。
在 Control-1 节点运行 kubectl 命令获取 Kubernetes 集群的节点信息。
kubectl get nodes -o wide
在输出结果中可以看到,当前的 Kubernetes 集群有 8个节点,并详细展示每个节点的名字、状态、角色、存活时间、Kubernetes 版本号、内部 IP、操作系统类型、内核版本和容器运行时等信息。
$ kubectl get nodes -o wide NAME STATUS ROLES AGE VERSION INTERNAL-IP EXTERNAL-IP OS-IMAGE KERNEL-VERSION CONTAINER-RUNTIME ksp-control-1 Ready control-plane 25h v1.28.8 192.168.9.91 <none> openEuler 22.03 (LTS-SP3) 5.10.0-182.0.0.95.oe2203sp3.x86_64 containerd://1.7.13 ksp-control-2 Ready control-plane 25h v1.28.8 192.168.9.92 <none> openEuler 22.03 (LTS-SP3) 5.10.0-182.0.0.95.oe2203sp3.x86_64 containerd://1.7.13 ksp-control-3 Ready control-plane 25h v1.28.8 192.168.9.93 <none> openEuler 22.03 (LTS-SP3) 5.10.0-182.0.0.95.oe2203sp3.x86_64 containerd://1.7.13 ksp-gpu-worker-1 Ready worker 59m v1.28.8 192.168.9.101 <none> openEuler 22.03 (LTS-SP3) 5.10.0-199.0.0.112.oe2203sp3.x86_64 containerd://1.7.13 ksp-gpu-worker-2 Ready worker 59m v1.28.8 192.168.9.102 <none> openEuler 22.03 (LTS-SP3) 5.10.0-199.0.0.112.oe2203sp3.x86_64 containerd://1.7.13 ksp-worker-1 Ready worker 25h v1.28.8 192.168.9.94 <none> openEuler 22.03 (LTS-SP3) 5.10.0-182.0.0.95.oe2203sp3.x86_64 containerd://1.7.13 ksp-worker-2 Ready worker 25h v1.28.8 192.168.9.95 <none> openEuler 22.03 (LTS-SP3) 5.10.0-182.0.0.95.oe2203sp3.x86_64 containerd://1.7.13 ksp-worker-3 Ready worker 25h v1.28.8 192.168.9.96 <none> openEuler 22.03 (LTS-SP3) 5.10.0-182.0.0.95.oe2203sp3.x86_64 containerd://1.7.13
至此,我们完成了利用 Kubekey 在现有的 3个 Master 节点和 3个 Worker 节点组成的 Kubernetes 集群中增加 2 个 Worker 节点的全部任务。
接下来我们安装 NVIDIA 官方出品的 NVIDIA GPU Operator,实现 K8s 调度 Pod 使用 GPU 资源。
NVIDIA GPU Operator 支持自动安装显卡驱动,但是只 CentOS 7、8 和 Ubuntu 20.04、22.04 等版本,并不支持 openEuler,所以需要手工安装显卡驱动。
请参考 KubeSphere 最佳实战:openEuler 22.03 LTS SP3 安装 NVIDIA 显卡驱动,完成显卡驱动安装。
Node Feature Discovery (NFD) 检测功能检查。
$ kubectl get nodes -o json | jq '.items[].metadata.labels | keys | any(startswith("feature.node.kubernetes.io"))'
上面的命令执行结果为 true
, 说明 NFD
已经在集群中运行。如果NFD已经在集群中运行,那么在安装 Operator 时必须禁用部署 NFD。
说明: 使用 KubeSphere 部署的 K8s 集群默认不会安装配置 NFD。
helm repo add nvidia https://helm.ngc.nvidia.com/nvidia && helm repo update
使用默认配置文件,禁用自动安装显卡驱动功能,安装 GPU Operator。
helm install -n gpu-operator --create-namespace gpu-operator nvidia/gpu-operator --set driver.enabled=false
注意: 由于安装的镜像比较大,所以初次安装过程中可能会出现超时的情形,请检查你的镜像是否成功拉取!可以考虑使用离线安装解决该类问题。
helm install -f gpu-operator-values.yaml -n gpu-operator --create-namespace gpu-operator nvidia/gpu-operator --set driver.enabled=false
正确执行输出结果如下:
$ helm install -n gpu-operator --create-namespace gpu-operator nvidia/gpu-operator --set driver.enabled=false NAME: gpu-operator LAST DEPLOYED: Tue Jul 2 21:40:29 2024 NAMESPACE: gpu-operator STATUS: deployed REVISION: 1 TEST SUITE: None
执行安装 GPU Operator 的命令后请耐心等待所有镜像成功拉取,所有 Pod 都处于 Running 状态。
$ kubectl get pods -n gpu-operator NAME READY STATUS RESTARTS AGE gpu-feature-discovery-czdf5 1/1 Running 0 15m gpu-feature-discovery-q9qlm 1/1 Running 0 15m gpu-operator-67c68ddccf-x29pm 1/1 Running 0 15m gpu-operator-node-feature-discovery-gc-57457b6d8f-zjqhr 1/1 Running 0 15m gpu-operator-node-feature-discovery-master-5fb74ff754-fzbzm 1/1 Running 0 15m gpu-operator-node-feature-discovery-worker-68459 1/1 Running 0 15m gpu-operator-node-feature-discovery-worker-74ps5 1/1 Running 0 15m gpu-operator-node-feature-discovery-worker-dpmg9 1/1 Running 0 15m gpu-operator-node-feature-discovery-worker-jvk4t 1/1 Running 0 15m gpu-operator-node-feature-discovery-worker-k5kwq 1/1 Running 0 15m gpu-operator-node-feature-discovery-worker-ll4bk 1/1 Running 0 15m gpu-operator-node-feature-discovery-worker-p4q5q 1/1 Running 0 15m gpu-operator-node-feature-discovery-worker-rmk99 1/1 Running 0 15m nvidia-container-toolkit-daemonset-9zcnj 1/1 Running 0 15m nvidia-container-toolkit-daemonset-kcz9g 1/1 Running 0 15m nvidia-cuda-validator-l8vjb 0/1 Completed 0 14m nvidia-cuda-validator-svn2p 0/1 Completed 0 13m nvidia-dcgm-exporter-9lq4c 1/1 Running 0 15m nvidia-dcgm-exporter-qhmkg 1/1 Running 0 15m nvidia-device-plugin-daemonset-7rvfm 1/1 Running 0 15m nvidia-device-plugin-daemonset-86gx2 1/1 Running 0 15m nvidia-operator-validator-csr2z 1/1 Running 0 15m nvidia-operator-validator-svlc4 1/1 Running 0 15m
$ kubectl describe node ksp-gpu-worker-1 | grep "^Capacity" -A 7 Capacity: cpu: 4 ephemeral-storage: 35852924Ki hugepages-1Gi: 0 hugepages-2Mi: 0 memory: 15858668Ki nvidia.com/gpu: 1 pods: 110
说明: 重点关注
nvidia.com/gpu:
字段的值。
创建成功的工作负载如下:
GPU Operator 正确安装完成后,使用 CUDA 基础镜像,测试 K8s 是否能正确创建使用 GPU 资源的 Pod。
vi cuda-ubuntu.yaml
apiVersion: v1 kind: Pod metadata: name: cuda-ubuntu2204 spec: restartPolicy: OnFailure containers: - name: cuda-ubuntu2204 image: "nvcr.io/nvidia/cuda:12.4.0-base-ubuntu22.04" resources: limits: nvidia.com/gpu: 1 command: ["nvidia-smi"]
kubectl apply -f cuda-ubuntu.yaml
从结果中可以看到 pod 创建在了 ksp-gpu-worker-2 节点(该节点显卡型号 Tesla P100-PCIE-16GB)。
$ kubectl get pods -o wide NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES cuda-ubuntu2204 0/1 Completed 0 73s 10.233.99.15 ksp-gpu-worker-2 <none> <none> ollama-79688d46b8-vxmhg 1/1 Running 0 47m 10.233.72.17 ksp-gpu-worker-1 <none> <none>
kubectl logs pod/cuda-ubuntu2204
正确执行输出结果如下:
$ kubectl logs pod/cuda-ubuntu2204 Mon Jul 8 11:10:59 2024 +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 550.54.15 Driver Version: 550.54.15 CUDA Version: 12.4 | |-----------------------------------------+------------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+========================+======================| | 0 Tesla P100-PCIE-16GB Off | 00000000:00:10.0 Off | 0 | | N/A 40C P0 26W / 250W | 0MiB / 16384MiB | 0% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ +-----------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=========================================================================================| | No running processes found | +-----------------------------------------------------------------------------------------+
kubectl apply -f cuda-ubuntu.yaml
执行一个简单的 CUDA 示例,用于将两个向量(vectors)相加。
vi cuda-vectoradd.yaml
apiVersion: v1 kind: Pod metadata: name: cuda-vectoradd spec: restartPolicy: OnFailure containers: - name: cuda-vectoradd image: "nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda11.7.1-ubuntu20.04" resources: limits: nvidia.com/gpu: 1
$ kubectl apply -f cuda-vectoradd.yaml
Pod 创建成功,启动后会运行 vectorAdd
命令并退出。
$ kubectl logs pod/cuda-vectoradd
正确执行输出结果如下:
[Vector addition of 50000 elements] Copy input data from the host memory to the CUDA device CUDA kernel launch with 196 blocks of 256 threads Copy output data from the CUDA device to the host memory Test PASSED Done
kubectl delete -f cuda-vectoradd.yaml
通过上面的验证测试,证明可以在 K8s 集群上创建使用 GPU 的 Pod 资源,接下来我们结合实际使用需求,利用 KubeSphere 在 K8s 集群创建一套大模型管理工具 Ollama。
本示例属于简单测试,存储选择了 hostPath 模式,实际使用中请替换为存储类或是其他类型的持久化存储。
vi deploy-ollama.yaml
kind: Deployment apiVersion: apps/v1 metadata: name: ollama namespace: default labels: app: ollama spec: replicas: 1 selector: matchLabels: app: ollama template: metadata: labels: app: ollama spec: volumes: - name: ollama-models hostPath: path: /data/openebs/local/ollama type: '' - name: host-time hostPath: path: /etc/localtime type: '' containers: - name: ollama image: 'ollama/ollama:latest' ports: - name: http-11434 containerPort: 11434 protocol: TCP resources: limits: nvidia.com/gpu: '1' requests: nvidia.com/gpu: '1' volumeMounts: - name: ollama-models mountPath: /root/.ollama - name: host-time readOnly: true mountPath: /etc/localtime imagePullPolicy: IfNotPresent restartPolicy: Always --- kind: Service apiVersion: v1 metadata: name: ollama namespace: default labels: app: ollama spec: ports: - name: http-11434 protocol: TCP port: 11434 targetPort: 11434 nodePort: 31434 selector: app: ollama type: NodePort
特殊说明: KubeSphere 的管理控制台支持图形化配置 Deployment 等资源使用 GPU 资源,配置示例如下,感兴趣的朋友可以自行研究。
kubectl apply -f deploy-ollama.yaml
从结果中可以看到 pod 创建在了 ksp-gpu-worker-1 节点(该节点显卡型号 Tesla M40 24GB)。
$ kubectl get pods -o wide NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES k 1/1 Running 0 12s 10.233.72.17 ksp-gpu-worker-1 <none> <none>
[root@ksp-control-1 ~]# kubectl logs ollama-79688d46b8-vxmhg 2024/07/08 18:24:27 routes.go:1064: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST:http://0.0.0.0:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE: OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:1 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:0 OLLAMA_MODELS:/root/.ollama/models OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:1 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://*] OLLAMA_RUNNERS_DIR: OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES:]" time=2024-07-08T18:24:27.829+08:00 level=INFO source=images.go:730 msg="total blobs: 5" time=2024-07-08T18:24:27.829+08:00 level=INFO source=images.go:737 msg="total unused blobs removed: 0" time=2024-07-08T18:24:27.830+08:00 level=INFO source=routes.go:1111 msg="Listening on [::]:11434 (version 0.1.48)" time=2024-07-08T18:24:27.830+08:00 level=INFO source=payload.go:30 msg="extracting embedded files" dir=/tmp/ollama2414166698/runners time=2024-07-08T18:24:32.454+08:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu cpu_avx cpu_avx2 cuda_v11 rocm_v60101]" time=2024-07-08T18:24:32.567+08:00 level=INFO source=types.go:98 msg="inference compute" id=GPU-9e48dc13-f8f1-c6bb-860f-c82c96df22a4 library=cuda compute=5.2 driver=12.4 name="Tesla M40 24GB" total="22.4 GiB" available="22.3 GiB"
本示例为了节省时间,采用阿里开源的 qwen2 1.5b 小尺寸模型作为测试模型。
kubectl exec -it ollama-79688d46b8-vxmhg -- ollama pull qwen2:1.5b
正确执行输出结果如下:
[root@ksp-control-1 ~]# kubectl exec -it ollama-79688d46b8-vxmhg -- ollama pull qwen2:1.5b pulling manifest pulling 405b56374e02... 100% ▕█████████████████████████████████████████████████████▏ 934 MB pulling 62fbfd9ed093... 100% ▕█████████████████████████████████████████████████████▏ 182 B pulling c156170b718e... 100% ▕█████████████████████████████████████████████████████▏ 11 KB pulling f02dd72bb242... 100% ▕█████████████████████████████████████████████████████▏ 59 B pulling c9f5e9ffbc5f... 100% ▕█████████████████████████████████████████████████████▏ 485 B verifying sha256 digest writing manifest removing any unused layers success
在 ksp-gpu-worker-1 节点执行下面的查看命令
$ ls -R /data/openebs/local/ollama/ /data/openebs/local/ollama/: id_ed25519 id_ed25519.pub models /data/openebs/local/ollama/models: blobs manifests /data/openebs/local/ollama/models/blobs: sha256-405b56374e02b21122ae1469db646be0617c02928fd78e246723ebbb98dbca3e sha256-62fbfd9ed093d6e5ac83190c86eec5369317919f4b149598d2dbb38900e9faef sha256-c156170b718ec29139d3653d40ed1986fd92fb7e0959b5c71f3c48f62e6636f4 sha256-c9f5e9ffbc5f14febb85d242942bd3d674a8e4c762aaab034ec88d6ba839b596 sha256-f02dd72bb2423204352eabc5637b44d79d17f109fdb510a7c51455892aa2d216 /data/openebs/local/ollama/models/manifests: registry.ollama.ai /data/openebs/local/ollama/models/manifests/registry.ollama.ai: library /data/openebs/local/ollama/models/manifests/registry.ollama.ai/library: qwen2 /data/openebs/local/ollama/models/manifests/registry.ollama.ai/library/qwen2: 1.5b
curl http://192.168.9.91:31434/api/chat -d '{ "model": "qwen2:1.5b", "messages": [ { "role": "user", "content": "用20个字,介绍你自己" } ] }'
$ curl http://192.168.9.91:31434/api/chat -d '{ "model": "qwen2:1.5b", "messages": [ { "role": "user", "content": "用20个字,介绍你自己" } ] }' {"model":"qwen2:1.5b","created_at":"2024-07-08T09:54:48.011798927Z","message":{"role":"assistant","content":"我"},"done":false} {"model":"qwen2:1.5b","created_at":"2024-07-08T09:54:48.035291669Z","message":{"role":"assistant","content":"是一个"},"done":false} {"model":"qwen2:1.5b","created_at":"2024-07-08T09:54:48.06360233Z","message":{"role":"assistant","content":"人工智能"},"done":false} {"model":"qwen2:1.5b","created_at":"2024-07-08T09:54:48.092411266Z","message":{"role":"assistant","content":"助手"},"done":false} {"model":"qwen2:1.5b","created_at":"2024-07-08T09:54:48.12016935Z","message":{"role":"assistant","content":","},"done":false} {"model":"qwen2:1.5b","created_at":"2024-07-08T09:54:48.144921623Z","message":{"role":"assistant","content":"专注于"},"done":false} {"model":"qwen2:1.5b","created_at":"2024-07-08T09:54:48.169803961Z","message":{"role":"assistant","content":"提供"},"done":false} {"model":"qwen2:1.5b","created_at":"2024-07-08T09:54:48.194796364Z","message":{"role":"assistant","content":"信息"},"done":false} {"model":"qwen2:1.5b","created_at":"2024-07-08T09:54:48.21978104Z","message":{"role":"assistant","content":"和"},"done":false} {"model":"qwen2:1.5b","created_at":"2024-07-08T09:54:48.244976103Z","message":{"role":"assistant","content":"帮助"},"done":false} {"model":"qwen2:1.5b","created_at":"2024-07-08T09:54:48.270233992Z","message":{"role":"assistant","content":"。"},"done":false} {"model":"qwen2:1.5b","created_at":"2024-07-08T09:54:48.29548561Z","message":{"role":"assistant","content":""},"done_reason":"stop","done":true,"total_duration":454377627,"load_duration":1535754,"prompt_eval_duration":36172000,"eval_count":12,"eval_duration":287565000}
$ kubectl describe node ksp-gpu-worker-1 | grep "Allocated resources" -A 9 Allocated resources: (Total limits may be over 100 percent, i.e., overcommitted.) Resource Requests Limits -------- -------- ------ cpu 487m (13%) 2 (55%) memory 315115520 (2%) 800Mi (5%) ephemeral-storage 0 (0%) 0 (0%) hugepages-1Gi 0 (0%) 0 (0%) hugepages-2Mi 0 (0%) 0 (0%) nvidia.com/gpu 1 1
在 Worker 节点上执行 nvidia-smi -l
观察 GPU 的使用情况。
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