experimentName: example_pix2pix 
searchSpace:
  ngf:
    _type: choice
    _value: [16, 32, 64, 128]
  ndf:
    _type: choice
    _value: [16, 32, 64, 128]
  netG:
    _type: choice
    _value: ["unet_256", "resnet_9blocks"]
  netD:
    _type: choice
    _value: ["basic", "pixel", "n_layers"]
  norm:
    _type: choice
    _value: ["batch", "instance", "none"]
  init_type:
    _type: choice
    _value: ["xavier", "normal", "kaiming", "orthogonal"]
  lr:
    _type: choice
    _value: [0.0001, 0.0002, 0.0005, 0.001, 0.005, 0.01, 0.1]
  beta1:
    _type: uniform
    _value: [0, 1]
  lr_policy:
    _type: choice
    _value: ["linear", "step", "plateau", "cosine"]
  gan_mode:
    _type: choice
    _value: ["vanilla", "lsgan", "wgangp"]
  lambda_L1:
    _type: choice
    _value: [1, 5, 10, 100, 250, 500]
trainingService:
  platform: local
  useActiveGpu: true
  gpuIndices: '0'
trialCodeDirectory: .
trialCommand: python3 pix2pix.py
trialConcurrency: 1
trialGpuNumber: 1
tuner:
  name: TPE
  classArgs:
    optimize_mode: minimize