07_training/serverlessml/hparam.yaml

displayName: "FlowersHpTuningJob"
maxTrialCount: 50
parallelTrialCount: 2
studySpec:
  metrics:
  - metricId: accuracy
    goal: MAXIMIZE
  parameters:
  - parameterId: l2
    scaleType: UNIT_LINEAR_SCALE
    doubleValueSpec:
      minValue: 0
      maxValue: 0.2
  - parameterId: batch_size
    scaleType: SCALE_TYPE_UNSPECIFIED
    discreteValueSpec:
      values:
      - 16
      - 32
      - 64
  - parameterId: num_hidden
    scaleType: SCALE_TYPE_UNSPECIFIED
    discreteValueSpec:
      values:
      - 8
      - 16
      - 24
  - parameterId: with_color_distort
    scaleType: SCALE_TYPE_UNSPECIFIED
    discreteValueSpec:
      values:
      - False
      - True
  - parameterId: crop_ratio
    scaleType: UNIT_LINEAR_SCALE
    doubleValueSpec:
      minValue: 0.5
      maxValue: 0.8
  algorithm: ALGORITHM_UNSPECIFIED
trialJobSpec:
  baseOutputDirectory:
    outputUriPrefix: gs://{BUCKET}/flowers_trained/hptune  # TODO: Add your bucket!
  workerPoolSpecs:
  - machineSpec:
      machineType: n1-standard-4
      acceleratorType: NVIDIA_TESLA_T4
      acceleratorCount: 2
    replicaCount: 1
  pythonPackageSpec:
    executorImageUri: us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-4:latest
    packageUris: gs://{BUCKET}/flowers-1.0.tar.gz  # TODO: Add your bucket!
    pythonModule: flowers.classifier.train
    args: 
    - --pattern="-*"
    - --num_epochs=20
    - --distribute="cpu"