TensorFlow Plugin API reference

nvidia.dali.plugin.tf.DALIDataset

alias of nvidia.dali.plugin.tf._DALIDatasetV2

nvidia.dali.plugin.tf.DALIIterator()

TF Plugin Wrapper

This operator works in the same way as DALI TensorFlow plugin, with the exception that is also accepts Pipeline objects as the input and serializes it internally. For more information, please look TensorFlow Plugin API reference in the documentation.

nvidia.dali.plugin.tf.DALIIteratorWrapper(pipeline=None, serialized_pipeline=None, sparse=[], shapes=[], dtypes=[], batch_size=-1, prefetch_queue_depth=2, **kwargs)

TF Plugin Wrapper

This operator works in the same way as DALI TensorFlow plugin, with the exception that is also accepts Pipeline objects as the input and serializes it internally. For more information, please look TensorFlow Plugin API reference in the documentation.

nvidia.dali.plugin.tf.DALIRawIterator()

DALI TensorFlow plugin

Creates a Dali pipeline for classification tasks from serialized DALI pipeline (given in serialized_pipeline parameter). shapes must match the shape of the coresponding DALI Pipeline output tensor shape. dtypes must match the type of the coresponding DALI Pipeline output tensors type.

Parameters

  • serialized_pipeline – A string.

  • shapes – A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.

  • dtypes – A list of tf.DTypes from: tf.half, tf.float32, tf.uint8, tf.int16, tf.int32, tf.int64 that has length >= 1.

  • num_threads – An optional int. Defaults to -1.

  • device_id – An optional int. Defaults to -1.

  • exec_separated – An optional bool. Defaults to False.

  • gpu_prefetch_queue_depth – An optional int. Defaults to 2.

  • cpu_prefetch_queue_depth – An optional int. Defaults to 2.

  • sparse – An optional list of bools. Defaults to [].

  • batch_size – An optional int. Defaults to -1.

  • name – A name for the operation (optional).

Returns

A list of Tensor objects of type dtypes.

Please keep in mind that TensorFlow allocates almost all available device memory by default. This might cause errors in DALI due to insufficient memory. On how to change this behaviour please look into the TensorFlow documentation, as it may differ based on your use case.

nvidia.dali.plugin.tf.dataset_compatible_tensorflow()