numa-pl.yaml

apiVersion: numaflow.numaproj.io/v1alpha1
kind: Pipeline
metadata:
  name: numalogic-simple-pipeline
spec:
  watermark:
    disabled: true
  limits:
    readBatchSize: 10
    bufferMaxLength: 500
    bufferUsageLimit: 100
  vertices:
  - name: in
    source:
      http: {}
  - name: preprocess
    scale:
      min: 1
    udf:
      container:
        image: quay.io/numaio/numalogic/example-udf:v0.4
        env:
          - name: WIN_SIZE
            value: "12"
        args:
        - python
        - starter.py
        - preprocess
  - name: inference
    scale:
      min: 1
    udf:
      container:
        image: quay.io/numaio/numalogic/example-udf:v0.4
        env:
          - name: WIN_SIZE
            value: "12"
        args:
        - python
        - starter.py
        - inference
  - name: threshold
    scale:
      min: 1
    udf:
      container:
        image: quay.io/numaio/numalogic/example-udf:v0.4
        env:
          - name: WIN_SIZE
            value: "12"
        args:
          - python
          - starter.py
          - threshold
  - name: postprocess
    scale:
      min: 1
    udf:
      container:
        image: quay.io/numaio/numalogic/example-udf:v0.4
        env:
          - name: WIN_SIZE
            value: "12"
        args:
        - python
        - starter.py
        - postprocess
  - name: out
    scale:
      min: 1
    sink:
      log: {}
  - name: train
    scale:
      min: 1
    udf:
      container:
        env:
          - name: WIN_SIZE
            value: "12"
        image: quay.io/numaio/numalogic/example-udf:v0.4
        args:
          - python
          - starter.py
          - train
  edges:
    - from: in
      to: preprocess
    - from: preprocess
      to: inference
    - from: inference
      to: threshold
    - from: threshold
      to: train
      conditions:
        keyIn:
          - train
    - from: threshold
      to: postprocess
      conditions:
        keyIn:
          - postprocess
    - from: postprocess
      to: out

---
#mlflow deployment config
apiVersion: apps/v1
kind: Deployment
metadata:
  name: mlflow-sqlite
  labels:
    app: mlflow
spec:
  selector:
    matchLabels:
      app: mlflow
  template:
    metadata:
      labels:
        app: mlflow
    spec:
      containers:
        - image: quay.io/numaio/numalogic/example-mlflow
          name: mlflow
          args:
          - server
          - --backend-store-uri
          - sqlite:https:///mlflow.db
          - --artifacts-destination
          - file:https://app/.mlruns
          - --serve-artifacts
          - --host
          - "0.0.0.0"
          - --port
          - "5000"
          command:
            - mlflow
          ports:
            - containerPort: 5000
---
kind: Service
apiVersion: v1
metadata:
  name: mlflow-service
spec:
  type: ClusterIP
  selector:
    app: mlflow
  ports:
    - port: 5000
      targetPort: 5000