mnist_config.yaml

io:
  input:
    task:
      training_ratio: 0.9
  output:
    serialize:
      save: True
      path: ${job.startup_path}/mnist.inference.pkl
launcher:
  type: local
  strategies:
    local:
job:
  ray_tune: false
  testing: False
  test:
    sample_num: 16
    epoch_num: 3
  batching:
    type: fixed_size
    strategies:
      fixed_size:
        batch_size: 16
        drop_last: False
  distributed: "no"
  dataloader:
    worker_num: 0 # load data using multi-process
    pin_memory: false
    timeout: 0
    resource_allocation_range: 10000
    drop_last: False
  resource:
    computing:
      cpu_per_trial: 4
      cpus: 4
      gpu_per_trial: 0
      gpus: 0
    memory:
      inferencing_memory_limit: 60000
      learning_memory_limit: 25000
  metric_logging:
    type: tableprint
    strategies:
      tableprint: { }
system:
  serialize:
    save: True
    path: ${job.startup_path}/mnist.system.pkl
    load: False
  task:
    evaluator:
  problem:
    evaluator:
  machine:
    arch:
      image_size: [28, 28]
      channel1: 32
      channel2: 64
      conv_kernel: 3
      pool_kernel: 2
      dropout1: 0.25
      dropout2: 0.5
      ff_dim: 128
      class_num: 10
  learner:
    optimizer:
      strategy:
        epochs: 3
      algorithm:
        lr: 1e-3
      lr_scheduler:
        gamma: 0.8
      gradient_clip_val: 1
    modelselector:
      stage: "machine"
      channel: "dev"
      metric: "label.precision"
      better: "max"
      save_prefix: "best"
    evaluator_trigger:
      trigger_at_begin: False
      evaluate_only: False
      interval: 1
      epoch_interval: -1