nvidia.dali.fn.external_source¶
-
nvidia.dali.fn.
external_source
(source=None, num_outputs=None, *, cycle=None, name=None, device='cpu', layout=None, dtype=None, ndim=None, cuda_stream=None, use_copy_kernel=None, batch=True, **kwargs)¶ Creates a data node which is populated with data from a Python source. The data can be provided by the
source
function or iterable, or it can be provided bypipeline.feed_input(name, data, layout, cuda_stream)
insidepipeline.iter_setup
.In the case of the GPU input, it is the user responsibility to modify the provided GPU memory content only using provided stream (DALI schedules a copy on it and all work is properly queued). If no stream is provided feeding input blocks until the provided memory is copied to the internal buffer.
Note
nvidia.dali.fn.external_source()
operator is partially compatible with TensorFlow integration vianvidia.dali.plugin.tf.experimental.DALIDatasetWithInputs()
. Please refer to its documentation for details.Note
To return a batch of copies of the same tensor, use
nvidia.dali.types.Constant()
, which is more performant.- Parameters
source (callable or iterable) –
The source of the data.
The source is polled for data (via a call
source()
ornext(source)
) when the pipeline needs input for the next iteration. Depending on the value ofnum_outputs
, the source can supply one or more data items. The data item can be a whole batch (default) or a single batch entry (whenbatch==False
). Ifnum_outputs
is not set, thesource
is expected to return one item (a batch or a sample). If this value is specified (even if its value is 1), the data is expected to a be tuple, or list, where each element corresponds to respective return value of the external_source.The data samples must be in one of the compatible array types:
NumPy ndarray (CPU)
MXNet ndarray (CPU)
PyTorch tensor (CPU or GPU)
CuPy array (GPU)
objects implementing
__cuda_array_interface__
DALI Tensor object
Batch sources must produce entire batches of data. This can be achieved either by adding a new outermost dimension to an array or by returning a list of arrays (in which case they can be of different size, but must have the same rank and element type). A batch source can also produce a DALI TensorList object, which can be an output of another DALI pipeline.
A per-batch source may accept one positional argument. If it does, it is the index of current iteration within epoch and consecutive calls will be
source(0)
,source(1)
, and so on. If batch_info is set to True, instance ofnvidia.dali.types.BatchInfo
will be passed to the source, instead of a plain index.A per-sample source may accept one positional argument of type
nvidia.dali.types.SampleInfo
, which contains index of the sample in current epoch and in the batch, as well as current iteration number.If the source is a generator function, the function is invoked and treated as an iterable. However, unlike a generator, the function can be used with
cycle
. In this case, the function will be called again when the generator reaches the end of iteration.For GPU inputs, it is a user’s responsibility to modify the provided GPU memory content only in the provided stream. DALI schedules a copy on this stream, and all work is properly queued. If no stream is provided, DALI will use a default, with a best-effort approach at correctness. See the
cuda_stream
argument documentation for more information.num_outputs (int, optional) –
If specified, denotes the number of TensorLists that are produced by the source function.
If set, the operator returns a list of
DataNode
objects, otherwise a singleDataNode
object is returned.
- Keyword Arguments
cycle (string or bool, optional) –
Specifies if and how to cycle through the source. It can be one of the following values:
"no"
,False
orNone
- don’t cycle;StopIteration
is raised whe end of data is reached; this is the default behavior"quiet"
orTrue
- the data is repeated indefinitely,"raise"
- when the end of data is reached,StopIteration
is raised, but the iteration is restarted on subsequent call.
This flag requires that the
source
is a collection, for example, an iterable object whereiter(source)
returns a fresh iterator on each call, or a generator function. In the latter case, the generator function is called again when more data than was yielded by the function is requested.Specifying
"raise"
can be used with DALI iterators to create a notion of epoch.name (str, optional) –
The name of the data node.
Used when feeding the data in
iter_setup
and can be omitted if the data is provided bysource
.layout (layout str or list/tuple thereof, optional) –
If provided, sets the layout of the data.
When
num_outputs > 1
, the layout can be a list that contains a distinct layout for each output. If the list has fewer thannum_outputs
elements, only the first outputs have the layout set, the rest of the outputs don’t have a layout set.dtype (nvidia.dali.types.DALIDataType or list/tuple thereof, optional) –
Input data type.
When
num_outputs > 1
, thedtype
can be a list that contains a distinct value for each output.The operator will validate that the fetched data is of the provided type. If the argument is omitted or
DALIDataType.NO_TYPE
is passed, the operator will infer the type from the provided data.This argument will be required starting from DALI 2.0.
ndim (int or list/tuple thereof, optional) –
Number of dimensions in the input data.
When
num_outputs > 1
, thendim
can be a list that contains a distinct value for each output.The dimensionality of the data provided to the operator will be verified against this value. Number of dimensions can be also inferred from the
layout
argument if provided.If the
layout
argument is provided, thendim
must match the number of dimensions in the layout.Specifying the input dimensionality will be required starting from DALI 2.0
cuda_stream (optional,
cudaStream_t
or an object convertible tocudaStream_t
, such ascupy.cuda.Stream
ortorch.cuda.Stream
) –The CUDA stream is used to copy data to the GPU or from a GPU source.
If this parameter is not set, a best-effort will be taken to maintain correctness. That is, if the data is provided as a tensor/array from a recognized library such as CuPy or PyTorch, the library’s current stream is used. Although this approach works in typical scenarios, with advanced use cases, and code that uses unsupported libraries, you might need to explicitly supply the stream handle.
This argument has two special values:
0 - Use the default CUDA stream
1 - Use DALI’s internal stream
If internal stream is used, the call to
feed_input
will block until the copy to internal buffer is complete, since there’s no way to synchronize with this stream to prevent overwriting the array with new data in another stream.use_copy_kernel (bool, optional) –
If set to True, DALI will use a CUDA kernel to feed the data instead of cudaMemcpyAsync (default).
Note
This is applicable only when copying data to and from GPU memory.
blocking (bool, optional) – Determines whether the external source should wait until data is available or just fail when the data is not available.
no_copy (bool, optional) –
Determines whether DALI should copy the buffer when feed_input is called.
If set to True, DALI passes the user memory directly to the pipeline, instead of copying it. It is the user responsibility to keep the buffer alive and unmodified until it is consumed by the pipeline.
The buffer can be modified or freed again after the output of the relevant iterations has been consumed. Effectively, it happens after Pipeline’s
prefetch_queue_depth
orcpu_queue_depth * gpu_queue_depth
(when they are not equal) iterations following thefeed_input
call.The memory location must match the specified
device
parameter of the operator. For the CPU, the provided memory can be one contiguous buffer or a list of contiguous Tensors. For the GPU, to avoid extra copy, the provided buffer must be contiguous. If you provide a list of separate Tensors, there will be an additional copy made internally, consuming both memory and bandwidth.Automatically set to
True
whenparallel=True
batch (bool, optional) –
If set to True or None, the
source
is expected to produce an entire batch at once. If set to False, thesource
is called per-sample.Setting
parallel
to True automatically setsbatch
to False if it was not provided.batch_info (bool, optional, default = False) – Controls if a callable
source
that accepts an argument and returns batches should receiveBatchInfo
instance or just an integer representing the iteration number. If set to False (the default), only the integer is passed. Ifsource
is not callable, does not accept arguments orbatch
is set to False, setting this flag has no effect.parallel (bool, optional, default = False) –
If set to True, the corresponding pipeline will start a pool of Python workers to run the callback in parallel. You can specify the number of workers by passing
py_num_workers
into pipeline’s constructor.When
parallel
is set to True, samples returned bysource
must be NumPy/MXNet/PyTorch CPU arrays or TensorCPU instances.Acceptable sources depend on the value specified for
batch
parameter.If
batch
is set toFalse
, thesource
must be:a callable (a function or an object with
__call__
method) that accepts exactly one argument (SampleInfo
instance that represents the index of the requested sample).
If
batch
is set toTrue
, thesource
can be either:a callable that accepts exactly one argument (either
BatchInfo
instance or an integer - seebatch_info
for details)an iterable,
a generator function.
Warning
Irrespective of
batch
value, callables should be stateless - they should produce requested sample or batch solely based on theSampleInfo
/BatchInfo
instance or index in batch, so that they can be run in parallel in a number of workers.The
source
callback must raise aStopIteration
when the end of the data is reached. Note, that due to prefetching, the callback may be invoked with a few iterations past the end of dataset - make sure it consistently raises aStopIteration
in that case.Note
Callable
source
can be run in parallel by multiple workers. Forbatch=True
multiple batches can be prepared in parallel, withbatch=False
it is possible to parallelize computation within the batch.When
batch=True
, callables performance might especially benefit from increasingprefetch_queue_depth
so that a few next batches can be computed in parallel.Note
Iterator or generator function will be assigned to a single worker that will iterate over them. The main advantage is execution in parallel to the main Python process, but due to their state it is not possible to calculate more than one batch at a time.
prefetch_queue_depth (int, option, default = 1) – When run in
parallel=True
mode, specifies the number of batches to be computed in advance and stored in the internal buffer, otherwise parameter is ignored.