Control of data loading process

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This article refers to BaseModel accessed via Docker container. Please refer to Snowflake Native App section if you are using BaseModel as SF GUI application.

data_loader_params block allows you to set constructor parameters for PyTorch DataLoader.
These settings modify how the data is loaded, such as batch sizes, workers etc.

• batch_size : int
  default: 256
  The size of the batch: how many samples per batch to load.
  
• num_workers : int
  default: 0
  How many sub-processes to use for data loading. 0 means that the data will be loaded in the main process. Increasing number of workers results in splitting queries into smaller pieces which reduce memory consumption on the database end.
  
• pin_memory : boolean
  default: False
  If True, the data loader will copy Tensors into device/CUDA pinned memory before returning them.
  
• drop_last : boolean
  default: False
  Set to True to drop the last incomplete batch if the dataset size is not divisible by.
  
• pin_memory_device : str
  default: None
  The device memory should be pinned to, if pin_memory is True.
  
• prefetch_factor : int
  default: 2
  Number of batches loaded in advance by each worker.
  

data_loader_params:
  batch_size: 256
  num_workers: 5