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main/performance_analysis/index.html create mode 100644 main/performance_analysis/stage_stats.html create mode 100644 main/py-modindex.html create mode 100644 main/search.html create mode 100644 main/searchindex.js diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 00000000..e69de29b diff --git a/index.html b/index.html new file mode 100644 index 00000000..17ade5bc --- /dev/null +++ b/index.html @@ -0,0 +1,11 @@ + + +
+Redirecting to the main document.
+ + diff --git a/main/_sources/api.rst.txt b/main/_sources/api.rst.txt new file mode 100644 index 00000000..11e22c3b --- /dev/null +++ b/main/_sources/api.rst.txt @@ -0,0 +1,13 @@ +API Reference +============= + +.. autosummary:: + :toctree: generated + :template: _custom_autosummary_module.rst + :caption: API Reference + :recursive: + + spdl.io + spdl.pipeline + spdl.dataloader + spdl.utils diff --git a/main/_sources/best_practice.rst.txt b/main/_sources/best_practice.rst.txt new file mode 100644 index 00000000..64d2d8b7 --- /dev/null +++ b/main/_sources/best_practice.rst.txt @@ -0,0 +1,133 @@ +Best Practices +============== + +Avoid creating intermediate tensors +----------------------------------- + +For efficient and performant data processing, it is advised to not create +an intermediate Tensor for each individual media object (such as single image), +instead create a batch Tensor directly. + +We recommend decoding individual frames, then using :py:func:`spdl.io.convert_frames` +to create a batch Tensor directly without creating an intermediate Tensors. + +If you are decoding a batch of images, and you have pre-determined set of images +that should go together into a same batch, use +:py:func:`spdl.io.load_image_batch` (or its async variant +:py:func:`spdl.io.async_load_image_batch`). + +Otherwise, demux, decode and pre-process multiple media, then combine them with +:py:func:`spdl.io.convert_frames` (or :py:func:`spdl.io.async_convert_frames`). +For example, the following functions implement decoding and tensor creation +separately. + +.. code-block:: + + import spdl.io + from spdl.io import ImageFrames + + def decode_image(src: str) -> ImageFrames: + packets = spdl.io.async_demux_image(src) + return spdl.io.async_decode_packets(packets) + + def batchify(frames: list[ImageFrames]) -> ImageFrames: + buffer = spdl.io.convert_frames(frames) + return spdl.io.to_torch(buffer) + +They can be combined in :py:class:`~spdl.pipeline.Pipeline`, which automatically +discards the items failed to process (for example due to invalid data), and +keep the batch size consistent by using other items successfully processed. + +.. code-block:: + + from spdl.pipeline import PipelineBuilder + + pipeline = ( + PipelineBuilder() + .add_source(...) + .pipe(decode_image, concurrency=...) + .aggregate(32) + .pipe(batchify) + .add_sink(3) + .build(num_threads=...) + ) + +.. seealso:: + + :py:mod:`multi_thread_preprocessing` + +Make Dataset class composable +----------------------------- + +If you are publishing a dataset and providing an implementation of +`Dataset` class, we recommend to make it composable. + +That is, in addition to the conventional ``Dataset`` class that +returns Tensors, make the components of the ``Dataset`` +implementation available by breaking down the implementation into + +* Iterator (or map) interface that returns paths instead of Tensors. +* A helper function that loads the source path into Tensor. + +For example, the interface of a ``Dataset`` for image classification +might look like the following. + +.. code-block:: + + class Dataset: + def __getitem__(self, key: int) -> tuple[Tensor, int]: + ... + +We recommend to separate the source and process and make them additional +public interface. +(Also, as described above, we recommend to not convert each item into +``Tensor`` for the performance reasons.) + +.. code-block:: + + class Source: + def __getitem__(self, key: int) -> tuple[str, int]: + ... + + def load(data: tuple[str, int]) -> tuple[ImageFrames, int]: + ... + +and if the processing is composed of stages with different bounding +factor, then split them further into primitive functions. + +.. code-block:: + + def download(src: tuple[str, int]) -> tuple[bytes, int]: + ... + + def decode_and_preprocess(data: tuple[bytes, int]) -> tuple[ImageFrames, int]: + ... + +then the original ``Dataset`` can be implemented as a composition + +.. code-block:: + + class Dataset: + def __init__(self, ...): + self._src = Source(...) + + def __getitem__(self, key:int) -> tuple[str, int]: + metadata = self._src[key] + item = download(metadata) + frames, cls = decode_and_preprocess(item) + tensor = spdl.io.to_torch(frames) + return tensor, cls + +Such decomposition makes the dataset compatible with SPDL's Pipeline, +and allows users to run them more efficiently + +.. code-block:: + + pipeline = ( + PipelineBuilder() + .add_source(Source(...)) + .pipe(download, concurrency=8) + .pipe(decode_and_preprocess, concurrency=4) + ... + .build(...) + ) diff --git a/main/_sources/cpp.rst.txt b/main/_sources/cpp.rst.txt new file mode 100644 index 00000000..91f63ac6 --- /dev/null +++ b/main/_sources/cpp.rst.txt @@ -0,0 +1,9 @@ +API References (C++) +==================== + +.. toctree:: + :caption: API References (C++) + + Class List