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# This example shows more configurable fields comparing to the minimal "config.yml"
# You can use "nnictl create --config config_detailed.yml" to launch this experiment.
# If you see an error message saying "port 8080 is used", use "nnictl stop --all" to stop previous experiments.

experimentName: MNIST           # An optional name to help you distinguish experiments.

# Hyper-parameter search space can either be configured here or in a seperate file.
# "config.yml" shows how to specify a seperate search space file.
# The common schema of search space is documented here:
#   https://nni.readthedocs.io/en/stable/Tutorial/SearchSpaceSpec.html
searchSpace:
  dropout_rate:
    _type: uniform
    _value: [0.5, 0.9]
  conv_size:
    _type: choice
    _value: [2, 3, 5, 7]
  hidden_size:
    _type: choice
    _value: [128, 512, 1024]
  batch_size:
    _type: choice
    _value: [16, 32]
  learning_rate:
    _type: choice
    _value: [0.0001, 0.001, 0.01, 0.1]

trialCommand: python3 mnist.py  # The command to launch a trial. NOTE: change "python3" to "python" if you are using Windows.
trialCodeDirectory: .           # The path of trial code. By default it's ".", which means the same directory of this config file.
trialGpuNumber: 1               # How many GPUs should each trial use. CUDA is required when it's greater than zero.

trialConcurrency: 4             # Run 4 trials concurrently.
maxTrialNumber: 10              # Generate at most 10 trials.
maxExperimentDuration: 1h       # Stop generating trials after 1 hour.

tuner:                          # Configure the tuning algorithm.
  name: TPE                     # Supported algorithms: TPE, Random, Anneal, Evolution, GridSearch, GPTuner, PBTTuner, etc.
                                #   Full list:  https://nni.readthedocs.io/en/latest/Tuner/BuiltinTuner.html
  classArgs:                    # Algorithm specific arguments. See the tuner's doc for details.
    optimize_mode: maximize     #   "minimize" or "maximize"

# Configure the training platform.
# Supported platforms: local, remote, openpai, aml, kubeflow, kubernetes, adl.
# You can find config template of some platforms in this directory, and others in mnist-pytorch example.
trainingService:
  platform: local
  useActiveGpu: false           # NOTE: Use "true" if you are using an OS with graphical interface (e.g. Windows 10, Ubuntu desktop)
                                #   Reason and details:  https://nni.readthedocs.io/en/latest/reference/experiment_config.html#useactivegpu