TensorFlow Plugin API reference

class nvidia.dali.plugin.tf.DALIDataset(pipeline, **kwargs)

Creates a DALIDataset compatible with tf.data.Dataset from a DALI pipeline. It supports TensorFlow 1.15 and 2.0

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.

Parameters

  • pipeline (nvidia.dali.Pipeline) – defining the data processing to be performed.

  • output_dtypes (tf.DType or tuple of tf.DType) – expected output types

  • output_shapes (tuple of shapes, optional) – expected output shapes. If provided, must match arity of the output_dtypes. When set to None, DALI will infer the shapes on its own. Individual shapes can be also set to None or contain None to indicate unknown dimensions. If specified must be compatible with shape returned from DALI Pipeline and with batch_size argument which will be the outermost dimension of returned tensors. In case of batch_size = 1 it can be omitted in the shape. DALI Dataset will try to match requested shape by squeezing 1-sized dimensions from shape obtained from Pipeline.

  • fail_on_device_mismatch (bool, optional) – When set to True runtime check will be performed to ensure DALI device and TF device are both CPU or both GPU. In some contexts this check might be inaccurate. When set to False will skip the check but print additional logs to check the devices. Keep in mind that this may allow hidden GPU to CPU copies in the workflow and impact performance.

  • batch_size (int, optional) – batch size of the pipeline.

  • num_threads (int, optional) – number of CPU threads used by the pipeline.

  • device_id (int, optional) – id of GPU used by the pipeline.

  • exec_separated (bool, optional) – Whether to execute the pipeline in a way that enables overlapping CPU and GPU computation, typically resulting in faster execution speed, but larger memory consumption.

  • prefetch_queue_depth (int, optional) – depth of the executor queue. Deeper queue makes DALI more resistant to uneven execution time of each batch, but it also consumes more memory for internal buffers. Value will be used with exec_separated set to False.

  • cpu_prefetch_queue_depth (int, optional) – depth of the executor cpu queue. Deeper queue makes DALI more resistant to uneven execution time of each batch, but it also consumes more memory for internal buffers. Value will be used with exec_separated set to True.

  • gpu_prefetch_queue_depth (int, optional) – depth of the executor gpu queue. Deeper queue makes DALI more resistant to uneven execution time of each batch, but it also consumes more memory for internal buffers. Value will be used with exec_separated set to True.

Returns

Return type

DALIDataset object based on DALI pipeline and compatible with tf.data.Dataset API.

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.

  • enable_memory_stats – An optional bool. Defaults to False.

  • 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()
nvidia.dali.plugin.tf.dataset_options()
nvidia.dali.plugin.tf.serialize_pipeline(pipeline)