# 在 Kubernetes 部署 我們繼續在 Kubernotes 中進行部署，如下所示： 1. 使用以下內容創建`mnist.yaml`文件： ```py apiVersion: extensions/v1beta1 kind: Deployment metadata: name: mnist-deployment spec: replicas: 3 template: metadata: labels: app: mnist-server spec: containers: - name: mnist-container image: neurasights/mnist-serving command: - /bin/sh args: - -c - tensorflow_model_server --model_name=mnist --model_base_path=/tmp/mnist_model ports: - containerPort: 8500 --- apiVersion: v1 kind: Service metadata: labels: run: mnist-service name: mnist-service spec: ports: - port: 8500 targetPort: 8500 selector: app: mnist-server # type: LoadBalancer ``` 如果您在 AWS 或 GCP 云中運行它，則取消注釋前一個文件中的`LoadBalancer`行。 由于我們在單個節點上本地運行整個集群，因此我們沒有外部 LoadBalancer。 1. 創建 Kubernetes 部署和服務： ```py $ kubectl create -f mnist.yaml deployment "mnist-deployment" created service "mnist-service" created ``` 1. 檢查部署，窗格和服務： ```py $ kubectl get deployments NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE mnist-deployment 3 3 3 0 1m $ kubectl get pods NAME READY STATUS RESTARTS AGE default-http-backend-bbchw 1/1 Running 3 9d mnist-deployment-554f4b674b-pwk8z 0/1 ContainerCreating 0 1m mnist-deployment-554f4b674b-vn6sd 0/1 ContainerCreating 0 1m mnist-deployment-554f4b674b-zt4xt 0/1 ContainerCreating 0 1m nginx-ingress-controller-724n5 1/1 Running 2 9d ``` ```py $ kubectl get services NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE default-http-backend ClusterIP 10.152.183.223 <none> 80/TCP 9d kubernetes ClusterIP 10.152.183.1 <none> 443/TCP 9d mnist-service LoadBalancer 10.152.183.66 <pending> 8500:32414/TCP 1m ``` ```py $ kubectl describe service mnist-service Name: mnist-service Namespace: default Labels: run=mnist-service Annotations: <none> Selector: app=mnist-server Type: LoadBalancer IP: 10.152.183.66 Port: <unset> 8500/TCP TargetPort: 8500/TCP NodePort: <unset> 32414/TCP Endpoints: 10.1.43.122:8500,10.1.43.123:8500,10.1.43.124:8500 Session Affinity: None External Traffic Policy: Cluster Events: <none> ``` 1. 等到所有 pod 的狀態為`Running`： ```py $ kubectl get pods NAME READY STATUS RESTARTS AGE default-http-backend-bbchw 1/1 Running 3 9d mnist-deployment-554f4b674b-pwk8z 1/1 Running 0 3m mnist-deployment-554f4b674b-vn6sd 1/1 Running 0 3m mnist-deployment-554f4b674b-zt4xt 1/1 Running 0 3m nginx-ingress-controller-724n5 1/1 Running 2 9d ``` 1. 檢查其中一個 pod 的日志，您應該看到如下內容： ```py $ kubectl logs mnist-deployment-59dfc5df64-g7prf I tensorflow_serving/model_servers/main.cc:147] Building single TensorFlow model file config: model_name: mnist model_base_path: /tmp/mnist_model I tensorflow_serving/model_servers/server_core.cc:441] Adding/updating models. I tensorflow_serving/model_servers/server_core.cc:492] (Re-)adding model: mnist I tensorflow_serving/core/basic_manager.cc:705] Successfully reserved resources to load servable {name: mnist version: 1} I tensorflow_serving/core/loader_harness.cc:66] Approving load for servable version {name: mnist version: 1} I tensorflow_serving/core/loader_harness.cc:74] Loading servable version {name: mnist version: 1} I external/org_tensorflow/tensorflow/contrib/session_bundle/bundle_shim.cc:360] Attempting to load native SavedModelBundle in bundle-shim from: /tmp/mnist_model/1 I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:236] Loading SavedModel from: /tmp/mnist_model/1 I external/org_tensorflow/tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:155] Restoring SavedModel bundle. I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:190] Running LegacyInitOp on SavedModel bundle. I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:284] Loading SavedModel: success. Took 45319 microseconds. I tensorflow_serving/core/loader_harness.cc:86] Successfully loaded servable version {name: mnist version: 1} E1122 12:18:04.566415410 6 ev_epoll1_linux.c:1051] grpc epoll fd: 3 I tensorflow_serving/model_servers/main.cc:288] Running ModelServer at 0.0.0.0:8500 ... ``` 1. 您還可以使用以下命令查看 UI 控制臺： ```py $ kubectl proxy xdg-open http://localhost:8001/ui ``` Kubernetes UI 控制臺如下圖所示：  由于我們在單個節點上本地運行集群，因此我們的服務僅在集群中公開，無法從外部訪問。登錄我們剛剛實例化的三個 pod 中的一個： ```py $ kubectl exec -it mnist-deployment-59dfc5df64-bb24q -- /bin/bash ``` 切換到主目錄并運行 MNIST 客戶端來測試服務： ```py $ kubectl exec -it mnist-deployment-59dfc5df64-bb24q -- /bin/bash root@mnist-deployment-59dfc5df64-bb24q:/# cd root@mnist-deployment-59dfc5df64-bb24q:~# python serving/tensorflow_serving/example/mnist_client.py --num_tests=100 --server=10.152.183.67:8500 Extracting /tmp/train-images-idx3-ubyte.gz Extracting /tmp/train-labels-idx1-ubyte.gz Extracting /tmp/t10k-images-idx3-ubyte.gz Extracting /tmp/t10k-labels-idx1-ubyte.gz .................................................................................................... Inference error rate: 7.0% root@mnist-deployment-59dfc5df64-bb24q:~# ``` 我們學習了如何在本地單個節點上運行的 Kubernetes 集群上部署 TensorFlow 服務。您可以使用相同的概念知識在您的場所內的公共云或私有云上部署服務。