cifar_pool_sync_over_model.yaml

name: cifar_pool_sync_over_model_dropout
description: Test cifar pooled dropout using sync across model
project: k-dropout
program: train_net.py
method: grid
metric:
  name: test_loss
  goal: minimize
# TODO: early_terminate
command:
  - ${env}
  - ${interpreter}
  - ${program}
  - ${args}
  - --sync_over_model
parameters:
  # experiment
  restarts: # TODO: find a better way to do multiple restarts
    value: 1
  # model
  input_size:
    value: 3072
  hidden_size:
    value: 2000
  output_size:
    value: 10
  n_hidden:
    value: 2
  # dropout
  dropout_layer:
    value: pool
  p:
    value: 0.5
  pool_size:
    values: [1, 100, 1000, 10000, 100000, 1000000, 20000000]
  m:
    values: [1, 128, 512]
  # dataset
  dataset_name:
    value: cifar10
  batch_size:
    value: 512
  test_batch_size:
    value: 512
  num_workers:
    value: 4
  # training
  device: 
    value: cuda
  epochs:
    value: 300
  lr:
    value: 0.0005