config.yaml

trainingInput:
  pythonVersion: "3.5"
  scaleTier: BASIC_GPU # BASIC | BASIC_GPU | STANDARD_1 | PREMIUM_1
######### example of custom scale tier config ##############
#  scaleTier: CUSTOM
#  masterType: complex_model_m
#  workerType: complex_model_m
#  parameterServerType: large_model
#  workerCount: 9
#  parameterServerCount: 3
######### example of hyper-parameter tuning config ##############
#  hyperparameters:
#    goal: MINIMIZE # Set to MINIMIZE or MAXIMIZE (e.g. rmse vs. accuracy)
#    hyperparameterMetricTag: rmse # Set to the name of the metric you assigned to the estimator
#    enableTrialEarlyStopping: True
#    maxTrials: 30
#    maxParallelTrials: 4
######### params to tune ##############
#    params:
#    - parameterName: num-layers
#      type: DISCRETE
#      discreteValues:
#      - 2
#      - 3
#      - 4
#      - 5
#      scaleType: UNIT_LINEAR_SCALE
#    - parameterName: layer-sizes-scale-factor
#      type: DOUBLE
#      minValue: 0.2
#      maxValue: 0.8
#      scaleType: UNIT_LINEAR_SCALE
#    - parameterName: learning-rate
#      type: DOUBLE
#      minValue: 0.0001
#      maxValue: 0.01
#      scaleType: UNIT_LOG_SCALE
#
##### add more parameters that you want to tune...