IBM/pytorchpipe

####################################################################
# Section defining all the default values of parameters used during testing.
# If you want to use different section during "processing" pass its name as command line argument '--section_name' to trainer (DEFAULT: test)
# Note: the following parameters will be (anyway) used as default values.
default_test:
  # Set the random seeds: -1 means that they will be picked randomly.
  seed_numpy: -1
  seed_torch: -1

  # Default batch size.
  batch_size: 64

  # Definition of the task (Mandatory!)
  #task:
  #  One must define its type (Mandatory!)
  #  type: ?
  #  The rest of the content of that section is task-specific...

  # Set a default configuration section for data loader.
  dataloader:
    # Shuffle set by default.
    shuffle: True 
    batch_sampler: None
     # Do not use multiprocessing by default.
    num_workers: 0
    pin_memory: False
    # Do not drop last frame by default.
    drop_last: False
    timeout: 0

  # Definition of sampler (Optional)
  # When this section will not be present, worker will use "standard" sampling (please refer to shuffle in dataloader)
  #sampler:
  #  # Type - generally all samplers from PyTorch (plus some new onses) are allowed (Mandatory!)
  #  # Options: 
  #  type: RandomSampler
  #  The rest of the content of that section is optimizer-specific...

 # Terminal condition that will be used during processing.
  terminal_conditions:
    # Terminal condition : maximal number of episodes (Optional, -1 means that processor will perform one pass over the whole dataset/split)
    episode_limit: -1


####################################################################
# Section defining all the default values of parameters used during training.
# If you want to use different section for validation pass its name as command line argument '--pipeline_section_name' to trainer (DEFAULT: pipeline)
pipeline: 
  # Pipeline must contain at least one component.
  #name_1:
  #   Each component must have defined its priority... (Mandatory!)
  #   priority: 0.1 # Can be float. Smaller means higher priority, up to zero.
  #   # ... and type (Mandatory!)
  #   type: ?
  #   The rest of the content of that section is component-specific...