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<section id="examples">
<h1>Examples<a class="headerlink" href="#examples" title="Permalink to this heading">¶</a></h1>
<p>In this section, you will find the data loading implementations (using DataPipes) of various
popular datasets across different research domains. Some of the examples are implements by the PyTorch team and the
implementation codes are maintained within PyTorch libraries. Others are created by members of the PyTorch community.</p>
<section id="audio">
<h2>Audio<a class="headerlink" href="#audio" title="Permalink to this heading">¶</a></h2>
<section id="librispeech">
<h3>LibriSpeech<a class="headerlink" href="#librispeech" title="Permalink to this heading">¶</a></h3>
<p><a class="reference external" href="https://www.openslr.org/12/">LibriSpeech dataset</a> is corpus of approximately 1000 hours of 16kHz read
English speech. Here is the
<a class="reference external" href="https://github.com/pytorch/data/blob/main/examples/audio/librispeech.py">DataPipe implementation of LibriSpeech</a>
to load the data.</p>
</section>
</section>
<section id="text">
<h2>Text<a class="headerlink" href="#text" title="Permalink to this heading">¶</a></h2>
<section id="amazon-review-polarity">
<h3>Amazon Review Polarity<a class="headerlink" href="#amazon-review-polarity" title="Permalink to this heading">¶</a></h3>
<p>The Amazon reviews dataset contains reviews from Amazon. Its purpose is to train text/sentiment classification models.
In our DataPipe
<a class="reference external" href="https://github.com/pytorch/data/blob/main/examples/text/amazonreviewpolarity.py">implementation of the dataset</a>,
we described every step with detailed comments to help you understand what each DataPipe is doing. We recommend
having a look at this example.</p>
</section>
<section id="imdb">
<h3>IMDB<a class="headerlink" href="#imdb" title="Permalink to this heading">¶</a></h3>
<p>This is a <a class="reference external" href="http://ai.stanford.edu/~amaas/data/sentiment/">large movie review dataset</a> for binary sentiment
classification containing 25,000 highly polar movie reviews for training and 25,00 for testing. Here is the
<a class="reference external" href="https://github.com/pytorch/data/blob/main/examples/text/imdb.py">DataPipe implementation to load the data</a>.</p>
</section>
<section id="squad">
<h3>SQuAD<a class="headerlink" href="#squad" title="Permalink to this heading">¶</a></h3>
<p><a class="reference external" href="https://rajpurkar.github.io/SQuAD-explorer/">SQuAD (Stanford Question Answering Dataset)</a> is a dataset for
reading comprehension. It consists of a list of questions by crowdworkers on a set of Wikipedia articles. Here are the
DataPipe implementations for <a class="reference external" href="https://github.com/pytorch/data/blob/main/examples/text/squad1.py">version 1.1</a>
is here and <a class="reference external" href="https://github.com/pytorch/data/blob/main/examples/text/squad2.py">version 2.0</a>.</p>
</section>
<section id="additional-datasets-in-torchtext">
<h3>Additional Datasets in TorchText<a class="headerlink" href="#additional-datasets-in-torchtext" title="Permalink to this heading">¶</a></h3>
<p>In a separate PyTorch domain library <a class="reference external" href="https://github.com/pytorch/text">TorchText</a>, you will find some of the most
popular datasets in the NLP field implemented as loadable datasets using DataPipes. You can find
all of those <a class="reference external" href="https://github.com/pytorch/text/tree/main/torchtext/datasets">NLP datasets here</a>.</p>
</section>
</section>
<section id="vision">
<h2>Vision<a class="headerlink" href="#vision" title="Permalink to this heading">¶</a></h2>
<section id="caltech-101">
<h3>Caltech 101<a class="headerlink" href="#caltech-101" title="Permalink to this heading">¶</a></h3>
<p>The <a class="reference external" href="https://data.caltech.edu/records/20086">Caltech 101 dataset</a> contains pictures of objects
belonging to 101 categories. Here is the
<a class="reference external" href="https://github.com/pytorch/data/blob/main/examples/vision/caltech101.py">DataPipe implementation of Caltech 101</a>.</p>
</section>
<section id="caltech-256">
<h3>Caltech 256<a class="headerlink" href="#caltech-256" title="Permalink to this heading">¶</a></h3>
<p>The <a class="reference external" href="https://data.caltech.edu/records/20087">Caltech 256 dataset</a> contains 30607 images
from 256 categories. Here is the
<a class="reference external" href="https://github.com/pytorch/data/blob/main/examples/vision/caltech256.py">DataPipe implementation of Caltech 256</a>.</p>
</section>
<section id="camvid-semantic-segmentation-community-example">
<h3>CamVid - Semantic Segmentation (community example)<a class="headerlink" href="#camvid-semantic-segmentation-community-example" title="Permalink to this heading">¶</a></h3>
<p>The <a class="reference external" href="http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/">Cambridge-driving Labeled Video Database (CamVid)</a> is a collection of videos with object class semantic
labels, complete with metadata. The database provides ground truth labels that associate each pixel with one of 32
semantic classes. Here is a
<a class="reference external" href="https://github.com/tcapelle/torchdata/blob/main/01_Camvid_segmentation_with_datapipes.ipynb">DataPipe implementation of CamVid</a>
created by our community.</p>
</section>
<section id="laion2b-en-joined">
<h3>laion2B-en-joined<a class="headerlink" href="#laion2b-en-joined" title="Permalink to this heading">¶</a></h3>
<p>The <a class="reference external" href="https://huggingface.co/datasets/laion/laion2B-en-joined">laion2B-en-joined dataset</a> is a subset of the <a class="reference external" href="https://laion.ai/blog/laion-5b/">LAION-5B dataset</a> containing english captions, URls pointing to images,
and other metadata. It contains around 2.32 billion entries.
Currently (February 2023) around 86% of the URLs still point to valid images. Here is a <a class="reference external" href="https://github.com/pytorch/data/blob/main/examples/vision/laion5b.py">DataPipe implementation of laion2B-en-joined</a> that filters out unsafe images and images with watermarks and loads the images from the URLs.</p>
</section>
<section id="additional-datasets-in-torchvision">
<h3>Additional Datasets in TorchVision<a class="headerlink" href="#additional-datasets-in-torchvision" title="Permalink to this heading">¶</a></h3>
<p>In a separate PyTorch domain library <a class="reference external" href="https://github.com/pytorch/vision">TorchVision</a>, you will find some of the most
popular datasets in the computer vision field implemented as loadable datasets using DataPipes. You can find all of
those <a class="reference external" href="https://github.com/pytorch/vision/tree/main/torchvision/prototype/datasets/_builtin">vision datasets here</a>.</p>
<p>Note that these implementations are currently in the prototype phase, but they should be fully supported
in the coming months. Nonetheless, they demonstrate the different ways DataPipes can be used for data loading.</p>
</section>
</section>
<section id="recommender-system">
<h2>Recommender System<a class="headerlink" href="#recommender-system" title="Permalink to this heading">¶</a></h2>
<section id="criteo-1tb-click-logs">
<h3>Criteo 1TB Click Logs<a class="headerlink" href="#criteo-1tb-click-logs" title="Permalink to this heading">¶</a></h3>
<p>The <a class="reference external" href="https://ailab.criteo.com/download-criteo-1tb-click-logs-dataset">Criteo dataset</a> contains feature values
and click feedback for millions of display advertisements. It aims to benchmark algorithms for
click through rate (CTR) prediction. You can find a prototype stage implementation of the
<a class="reference external" href="https://github.com/pytorch/torchrec/blob/main/torchrec/datasets/criteo.py">dataset with DataPipes in TorchRec</a>.</p>
</section>
</section>
<section id="graphs-meshes-and-point-clouds">
<h2>Graphs, Meshes and Point Clouds<a class="headerlink" href="#graphs-meshes-and-point-clouds" title="Permalink to this heading">¶</a></h2>
<section id="tigergraph-community-example">
<h3>TigerGraph (community example)<a class="headerlink" href="#tigergraph-community-example" title="Permalink to this heading">¶</a></h3>
<p>TigerGraph is a scalable graph data platform for AI and ML. You can find an <a class="reference external" href="https://github.com/TigerGraph-DevLabs/torchdata_tutorial/blob/main/torchdata_example.ipynb">implementation</a> of graph feature engineering and machine learning with DataPipes in TorchData and data stored in a TigerGraph database, which includes computing PageRank scores in-database, pulling graph data and features with multiple DataPipes, and training a neural network using graph features in PyTorch.</p>
</section>
<section id="moleculenet-community-example">
<h3>MoleculeNet (community example)<a class="headerlink" href="#moleculenet-community-example" title="Permalink to this heading">¶</a></h3>
<p><a class="reference external" href="https://moleculenet.org/">MoleculeNet</a> is a benchmark specially designed for testing machine learning methods of
molecular properties. You can find an implementation of the
<a class="reference external" href="https://github.com/pyg-team/pytorch_geometric/blob/master/examples/datapipe.py">HIV dataset with DataPipes in PyTorch Geometric</a>,
which includes converting SMILES strings into molecular graph representations.</p>
</section>
<section id="princeton-modelnet-community-example">
<h3>Princeton ModelNet (community example)<a class="headerlink" href="#princeton-modelnet-community-example" title="Permalink to this heading">¶</a></h3>
<p>The Princeton ModelNet project provides a comprehensive and clean collection of 3D CAD models across various object types.
You can find an implementation of the
<a class="reference external" href="https://github.com/pyg-team/pytorch_geometric/blob/master/examples/datapipe.py">ModelNet10 dataset with DataPipes in PyTorch Geometric</a>,
which includes reading in meshes via <a class="reference external" href="https://github.com/nschloe/meshio">meshio</a>, and sampling of points from object surfaces and dynamic
graph generation via <a class="reference external" href="https://pytorch-geometric.readthedocs.io/en/latest/modules/transforms.html">PyG’s functional transformations</a>.</p>
</section>
</section>
<section id="timeseries">
<h2>Timeseries<a class="headerlink" href="#timeseries" title="Permalink to this heading">¶</a></h2>
<section id="custom-datapipe-for-timeseries-rolling-window-community-example">
<h3>Custom DataPipe for Timeseries rolling window (community example)<a class="headerlink" href="#custom-datapipe-for-timeseries-rolling-window-community-example" title="Permalink to this heading">¶</a></h3>
<p>Implementing a rolling window custom <cite>DataPipe</cite> for timeseries forecasting tasks.
Here is the
<a class="reference external" href="https://github.com/tcapelle/torchdata/blob/main/02_Custom_timeseries_datapipe.ipynb">DataPipe implementation of a rolling window</a>.</p>
</section>
</section>
<section id="using-aistore">
<h2>Using AIStore<a class="headerlink" href="#using-aistore" title="Permalink to this heading">¶</a></h2>
<section id="caltech-256-and-microsoft-coco-community-example">
<h3>Caltech 256 and Microsoft COCO (community example)<a class="headerlink" href="#caltech-256-and-microsoft-coco-community-example" title="Permalink to this heading">¶</a></h3>
<p>Listing and loading data from AIS buckets (buckets that are not 3rd party backend-based) and remote cloud buckets (3rd party
backend-based cloud buckets) using <a class="reference external" href="https://pytorch.org/data/main/generated/torchdata.datapipes.iter.AISFileLister.html#aisfilelister">AISFileLister</a> and <a class="reference external" href="https://pytorch.org/data/main/generated/torchdata.datapipes.iter.AISFileLoader.html#torchdata.datapipes.iter.AISFileLoader">AISFileLoader</a>.</p>
<p>Here is an <a class="reference external" href="https://github.com/pytorch/data/blob/main/examples/aistore/aisio_usage_example.ipynb">example which uses AISIO DataPipe</a> for the <a class="reference external" href="https://data.caltech.edu/records/20087">Caltech-256 Object Category Dataset</a> containing 256 object categories and a total
of 30607 images stored on an AIS bucket and the <a class="reference external" href="https://cocodataset.org/#home">Microsoft COCO Dataset</a> which has 330K images with over 200K
labels of more than 1.5 million object instances across 80 object categories stored on Google Cloud.</p>
</section>
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