Python API¶

Pipeline¶

class nvidia.dali.pipeline.Pipeline(batch_size=-1, num_threads=-1, device_id=-1, seed=-1, exec_pipelined=True, prefetch_queue_depth=2, exec_async=True, bytes_per_sample=0, set_affinity=False, max_streams=-1)¶

Pipeline class encapsulates all data required to define and run DALI input pipeline.

Parameters

batch_size (int, optional, default = -1) – Batch size of the pipeline. Negative values for this parameter are invalid - the default value may only be used with serialized pipeline (the value stored in serialized pipeline is used instead).
num_threads (int, optional, default = -1) – Number of CPU threads used by the pipeline. Negative values for this parameter are invalid - the default value may only be used with serialized pipeline (the value stored in serialized pipeline is used instead).
device_id (int, optional, default = -1) – Id of GPU used by the pipeline. Negative values for this parameter are invalid - the default value may only be used with serialized pipeline (the value stored in serialized pipeline is used instead).
seed (int, optional, default = -1) – Seed used for random number generation. Leaving the default value for this parameter results in random seed.
exec_pipelined (bool, optional, default = True) – 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.
exec_async (bool, optional, default = True) – Whether to execute the pipeline asynchronously. This makes nvidia.dali.pipeline.Pipeline.run() method run asynchronously with respect to the calling Python thread. In order to synchronize with the pipeline one needs to call nvidia.dali.pipeline.Pipeline.outputs() method.
bytes_per_sample (int, optional, default = 0) – A hint for DALI for how much memory to use for its tensors.
set_affinity (bool, optional, default = False) – Whether to set CPU core affinity to the one closest to the GPU being used.
max_streams (int, optional, default = -1) – Limit the number of CUDA streams used by the executor. Value of -1 does not impose a limit. This parameter is currently unused (and behavior of unrestricted number of streams is assumed).
prefetch_queue_depth (int or {"cpu_size": int, "gpu_size": int}, optional, default = 2) – Depth of the executor pipeline. Deeper pipeline makes DALI more resistant to uneven execution time of each batch, but it also consumes more memory for internal buffers. Specifying a dict: { "cpu_size": x, "gpu_size": y } instead of integer will cause the pipeline to use separated queues executor, with buffer queue size x for cpu stage and y for mixed and gpu stages. It is not supported when both exec_async and exec_pipelined are set to False. Executor will buffer cpu and gpu stages separatelly, and will fill the buffer queues when the first nvidia.dali.pipeline.Pipeline.run() is issued.

batch_size¶: Batch size.

build()¶

Build the pipeline.

Pipeline needs to be built in order to run it standalone. Framework-specific plugins handle this step automatically.

define_graph()¶

This function is defined by the user to construct the graph of operations for their pipeline.

It returns a list of output EdgeReference.

deserialize_and_build(serialized_pipeline)¶

Deserialize and build the pipeline given in serialized form.

Parameters: serialized_pipeline (str) – Serialized pipeline.

device_id¶: Id of the GPU used by the pipeline.

epoch_size(name=None)¶

Epoch size of a pipeline.

If the name parameter is None, returns a dictionary of pairs (reader name, epoch size for that reader). If the name parameter is not None, returns epoch size for that reader.

Parameters: name (str, optional, default = None) – The reader which should be used to obtain epoch size.

feed_input(ref, data)¶: Bind the NumPy array to a tensor produced by ExternalSource operator.

iter_setup()¶: This function can be overriden by user-defined pipeline to perform any needed setup for each iteration. For example, one can use this function to feed the input data from NumPy arrays.

num_threads¶: Number of CPU threads used by the pipeline.

outputs()¶

Returns the outputs of the pipeline and releases previous buffer.

If the pipeline is executed asynchronously, this function blocks until the results become available. It rises StopIteration if data set reached its end - usually when iter_setup cannot produce any more data

reset()¶

Resets pipeline iterator

If pipeline iterator reached the end then reset its state to the beggining.

run()¶

Run the pipeline and return the result.

If the pipeline was created with exec_async option set to True, this function will also start prefetching the next iteration for faster execution.

save_graph_to_dot_file(filename)¶

Saves the pipeline graph to a file.

Parameters: filename (str) – Name of the file to which the graph is written.

serialize()¶: Serialize the pipeline to a Protobuf string.

Tensor¶

class nvidia.dali.backend.TensorCPU¶

copy_to_external(self: nvidia.dali.backend_impl.TensorCPU, ptr: object) → None¶

Copy to external pointer in the CPU memory.

Parameters: ptr (ctypes.c_void_p) – Destination of the copy.

dtype(self: nvidia.dali.backend_impl.TensorCPU) → str¶: String representing NumPy type of the Tensor.

shape(self: nvidia.dali.backend_impl.TensorCPU) → List[int]¶: Shape of the tensor.

squeeze(self: nvidia.dali.backend_impl.TensorCPU) → None¶: Remove single-dimensional entries from the shape of the Tensor.

class nvidia.dali.backend.TensorGPU¶

copy_to_external(self: nvidia.dali.backend_impl.TensorGPU, ptr: object) → None¶

Copy to external pointer in the GPU memory.

Parameters: ptr (ctypes.c_void_p) – Destination of the copy.

dtype(self: nvidia.dali.backend_impl.TensorGPU) → str¶: String representing NumPy type of the Tensor.

shape(self: nvidia.dali.backend_impl.TensorGPU) → List[int]¶: Shape of the tensor.

squeeze(self: nvidia.dali.backend_impl.TensorGPU) → None¶: Remove single-dimensional entries from the shape of the Tensor.

TensorList¶

class nvidia.dali.backend.TensorListCPU¶

as_array(self: nvidia.dali.backend_impl.TensorListCPU) → array¶: Returns TensorList as a numpy array. TensorList must be dense.

as_tensor(self: nvidia.dali.backend_impl.TensorListCPU) → nvidia.dali.backend_impl.TensorCPU¶

Returns a tensor that is a view of this TensorList.

This function can only be called if is_dense_tensor returns True.

at(self: nvidia.dali.backend_impl.TensorListCPU, arg0: int) → array¶: Returns tensor at given position in the list.

copy_to_external(self: nvidia.dali.backend_impl.TensorListCPU, arg0: object) → None¶

Copy the contents of this TensorList to an external pointer (of type ctypes.c_void_p) residing in CPU memory.

This function is used internally by plugins to interface with tensors from supported Deep Learning frameworks.

is_dense_tensor(self: nvidia.dali.backend_impl.TensorListCPU) → bool¶

Checks whether all tensors in this TensorList have the same shape (and so the list itself can be viewed as a tensor).

For example, if TensorList contains N tensors, each with shape (H,W,C) (with the same values of H, W and C), then the list may be viewed as a tensor of shape (N, H, W, C).

class nvidia.dali.backend.TensorListGPU¶

as_cpu(self: nvidia.dali.backend_impl.TensorListGPU) → nvidia.dali.backend_impl.TensorListCPU¶: Returns a TensorListCPU object being a copy of this TensorListGPU.

as_tensor(self: nvidia.dali.backend_impl.TensorListGPU) → nvidia.dali.backend_impl.TensorGPU¶

Returns a tensor that is a view of this TensorList.

This function can only be called if is_dense_tensor returns True.

at(self: nvidia.dali.backend_impl.TensorListGPU, arg0: int) → nvidia.dali.backend_impl.TensorGPU¶: Returns a tensor at given position in the list.

copy_to_external(self: nvidia.dali.backend_impl.TensorListGPU, arg0: object) → None¶

Copy the contents of this TensorList to an external pointer (of type ctypes.c_void_p) residing in CPU memory.

This function is used internally by plugins to interface with tensors from supported Deep Learning frameworks.

is_dense_tensor(self: nvidia.dali.backend_impl.TensorListGPU) → bool¶

Checks whether all tensors in this TensorList have the same shape (and so the list itself can be viewed as a tensor).

For example, if TensorList contains N tensors, each with shape (H,W,C) (with the same values of H, W and C), then the list may be viewed as a tensor of shape (N, H, W, C).

Enums¶

class nvidia.dali.types.DALIDataType¶

Data type of image

BOOL = DALIDataType.BOOL¶

DATA_TYPE = DALIDataType.DATA_TYPE¶

FEATURE = DALIDataType.FEATURE¶

FLOAT = DALIDataType.FLOAT¶

FLOAT16 = DALIDataType.FLOAT16¶

IMAGE_TYPE = DALIDataType.IMAGE_TYPE¶

INT16 = DALIDataType.INT16¶

INT32 = DALIDataType.INT32¶

INT64 = DALIDataType.INT64¶

INTERP_TYPE = DALIDataType.INTERP_TYPE¶

NO_TYPE = DALIDataType.NO_TYPE¶

STRING = DALIDataType.STRING¶

TENSOR_LAYOUT = DALIDataType.TENSOR_LAYOUT¶

UINT8 = DALIDataType.UINT8¶

class nvidia.dali.types.DALIInterpType¶

Interpolation mode

INTERP_CUBIC = DALIInterpType.INTERP_CUBIC¶

INTERP_GAUSSIAN = DALIInterpType.INTERP_GAUSSIAN¶

INTERP_LANCZOS3 = DALIInterpType.INTERP_LANCZOS3¶

INTERP_LINEAR = DALIInterpType.INTERP_LINEAR¶

INTERP_NN = DALIInterpType.INTERP_NN¶

INTERP_TRIANGULAR = DALIInterpType.INTERP_TRIANGULAR¶

class nvidia.dali.types.DALIImageType¶

Image type

BGR = DALIImageType.BGR¶

GRAY = DALIImageType.GRAY¶

RGB = DALIImageType.RGB¶

YCbCr = DALIImageType.YCbCr¶

class nvidia.dali.types.DALITensorLayout¶

Tensor layout

NCHW = DALITensorLayout.NCHW¶

NFCHW = DALITensorLayout.NFCHW¶

NFHWC = DALITensorLayout.NFHWC¶

NHWC = DALITensorLayout.NHWC¶

SAME = DALITensorLayout.SAME¶