Server Config

Note: This section is for advanced usage where you want to define new types of inference servers.

Server configurations define how to create an inference server. By default one is provided for Seldon MLServer and one for NVIDIA Triton Inference Server. Both these servers support the Open Inference Protocol which is a requirement for all inference servers. They define how the Kubernetes ReplicaSet is defined which includes the Seldon Agent reverse proxy as well as an Rclone server for downloading artifacts for the server. The Kustomize ServerConfig for MlServer is shown below:

https://github.com/SeldonIO/seldon-core/blob/v2/operator/config/serverconfigs/mlserver.yaml

---
apiVersion: mlops.seldon.io/v1alpha1
kind: ServerConfig
metadata:
  name: mlserver
spec:
  podSpec:
    terminationGracePeriodSeconds: 120
    serviceAccountName: seldon-server
    containers:
    - image: rclone:latest
      imagePullPolicy: IfNotPresent
      name: rclone
      ports:
      - containerPort: 5572
        name: rclone
        protocol: TCP
      lifecycle:
        preStop:
          httpGet:
            port: 9007
            path: terminate
      resources:
        requests:
          cpu: "200m"
          memory: '100M'
      readinessProbe:
        failureThreshold: 3
        initialDelaySeconds: 5
        periodSeconds: 5
        successThreshold: 1
        tcpSocket:
          port: 5572
        timeoutSeconds: 1
      volumeMounts:
      - mountPath: /mnt/agent
        name: mlserver-models
    - image: agent:latest
      imagePullPolicy: IfNotPresent
      command:
        - /bin/agent
      args:
        - --tracing-config-path=/mnt/tracing/tracing.json
      name: agent
      env:
      - name: SELDON_SERVER_CAPABILITIES
        value: "mlserver,alibi-detect,alibi-explain,huggingface,lightgbm,mlflow,python,sklearn,spark-mlib,xgboost"
      - name: SELDON_OVERCOMMIT_PERCENTAGE
        value: "10"
      - name: SELDON_MODEL_INFERENCE_LAG_THRESHOLD
        value: "30"
      - name: SELDON_MODEL_INACTIVE_SECONDS_THRESHOLD
        value: "600"
      - name: SELDON_SCALING_STATS_PERIOD_SECONDS
        value: "20"
      - name: SELDON_SERVER_HTTP_PORT
        value: "9000"
      - name: SELDON_SERVER_GRPC_PORT
        value: "9500"
      - name: SELDON_REVERSE_PROXY_HTTP_PORT
        value: "9001"
      - name: SELDON_REVERSE_PROXY_GRPC_PORT
        value: "9501"
      - name: SELDON_SCHEDULER_HOST
        value: "seldon-scheduler"
      - name: SELDON_SCHEDULER_PORT
        value: "9005"
      - name: SELDON_SCHEDULER_TLS_PORT
        value: "9055"
      - name: SELDON_METRICS_PORT
        value: "9006"
      - name: SELDON_DRAINER_PORT
        value: "9007"
      - name: SELDON_READINESS_PORT
        value: "9008"
      - name: AGENT_TLS_SECRET_NAME
        value: ""
      - name: AGENT_TLS_FOLDER_PATH
        value: ""
      - name: SELDON_SERVER_TYPE
        value: "mlserver"
      - name: SELDON_ENVOY_HOST
        value: "seldon-mesh"
      - name: SELDON_ENVOY_PORT
        value: "80"
      - name: SELDON_LOG_LEVEL
        value: "warn"
      - name: POD_NAME
        valueFrom:
          fieldRef:
            fieldPath: metadata.name
      - name: POD_NAMESPACE
        valueFrom:
          fieldRef:
            fieldPath: metadata.namespace
      - name: MEMORY_REQUEST
        valueFrom:
          resourceFieldRef:
            containerName: mlserver
            resource: requests.memory
      - name: SELDON_USE_DEPLOYMENTS_FOR_SERVERS
        value: "false"
      ports:
      - containerPort: 9501
        name: grpc
        protocol: TCP
      - containerPort: 9001
        name: http
        protocol: TCP
      - containerPort: 9006
        name: metrics
        protocol: TCP
      - containerPort: 9008
        name: readiness-port
      lifecycle:
        preStop:
          httpGet:
            port: 9007
            path: terminate
      readinessProbe:
        httpGet:
          path: /ready
          port: 9008
        failureThreshold: 1
        periodSeconds: 5
      startupProbe:
        httpGet:
          path: /ready
          port: 9008
        failureThreshold: 60
        periodSeconds: 15
      resources:
        requests:
          cpu: "500m"
          memory: '500M'
      volumeMounts:
      - mountPath: /mnt/agent
        name: mlserver-models
      - name: config-volume
        mountPath: /mnt/config
      - name: tracing-config-volume
        mountPath: /mnt/tracing
    - image: mlserver:latest
      imagePullPolicy: IfNotPresent
      env:
      - name: MLSERVER_HTTP_PORT
        value: "9000"
      - name: MLSERVER_GRPC_PORT
        value: "9500"
      - name: MLSERVER_MODELS_DIR
        value: "/mnt/agent/models"
      - name: MLSERVER_MODEL_PARALLEL_WORKERS
        value: "1"
      - name: MLSERVER_LOAD_MODELS_AT_STARTUP
        value: "false"
      - name: MLSERVER_GRPC_MAX_MESSAGE_LENGTH
        value: "1048576000" # 100MB (100 * 1024 * 1024)
      resources:
        requests:
          cpu: 1
          memory: '1G'
      lifecycle:
        preStop:
          httpGet:
            port: 9007
            path: terminate
      livenessProbe:
        httpGet:
          path: /v2/health/live
          port: server-http
      readinessProbe:
        httpGet:
          path: /v2/health/live
          port: server-http
        initialDelaySeconds: 5
        periodSeconds: 5
      startupProbe:
        httpGet:
          path: /v2/health/live
          port: server-http
        failureThreshold: 10
        periodSeconds: 10
      name: mlserver
      ports:
      - containerPort: 9500
        name: server-grpc
        protocol: TCP
      - containerPort: 9000
        name: server-http
        protocol: TCP
      - containerPort: 8082
        name: server-metrics
      volumeMounts:
      - mountPath: /mnt/agent
        name: mlserver-models
        readOnly: true
      - mountPath: /mnt/certs
        name: downstream-ca-certs
        readOnly: true
    securityContext:
      fsGroup: 2000
      runAsUser: 1000
      runAsNonRoot: true
    volumes:
    - name: config-volume
      configMap:
        name: seldon-agent
    - name: tracing-config-volume
      configMap:
        name: seldon-tracing
    - name: downstream-ca-certs
      secret:
        secretName: seldon-downstream-server
        optional: true
  volumeClaimTemplates:
  - name: mlserver-models
    spec:
      accessModes: [ "ReadWriteOnce" ]
      resources:
        requests:
          storage: 1Gi