Pipeline¶

class dragon.vm.dali.Pipeline(
batch_size=1,
num_threads=1,
seed=3,
prefetch_queue_depth=2,
py_num_workers=1,
**kwargs
)[source]¶

The base pipeline class to define operations.

class MyPipeline(dali.Pipeline):
    def __init__(batch_size=1, num_threads=4):
        super(MyPipeline, self).__init__(batch_size, num_threads)

init¶

Pipeline.__init__(
batch_size=1,
num_threads=1,
seed=3,
prefetch_queue_depth=2,
py_num_workers=1,
**kwargs
)[source]¶

Create a Pipeline

Parameters:

batch_size (int, optional, default=1) – The number of examples in a batch.
num_threads (int, optional, default=1) – The number of threads to execute the operations.
seed (int, optional, default=3) – The seed for random generator.
prefetch_queue_depth (int, optional, default=2) – The number of prefetch queues.
py_num_workers (int, optional, default=1) – The number of workers to process external source.

Pipeline.device_id¶

Return the device index of pipeline.

Pipeline.max_batch_size¶

Return the maximum batch size of pipeline.

Pipeline.num_threads¶

Return the number of threads to execute pipeline.

Pipeline.build(define_graph=None)[source]¶

Build the pipeline.

Parameters:

define_graph (callable, optional) – The defined function to use instead.

Pipeline.define_graph()[source]¶: Define the symbolic operations for pipeline.

Pipeline.feed_input(
*args,
**kwargs
)[source]¶: Bind an array to the edge reference.