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    <h1>PyTorch Domains</h1>
    <h2 class="lead">Learn about the various parts of the PyTorch ecosystem</h2>

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          <a href="https://pytorch.org/audio/stable/index.html">
            <h4>torchaudio</h4>
            <p>Torchaudio is a library for audio and signal processing with PyTorch. It provides I/O, signal and data processing functions, datasets, model implementations and application components.
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          <a href="https://pytorch.org/torchtune/">
            <h4>torchtune</h4>
            <p>A PyTorch-native library designed for fine-tuning large language models (LLMs). torchtune provides supports the full fine-tuning workflow and offers compatibility with popular production inference systems.
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          <a href="https://pytorch.org/vision/stable/index.html">
            <h4>torchvision</h4>
            <p>This library is part of the PyTorch project. PyTorch is an open source machine learning framework. The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.
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          <a href="https://pytorch.org/torcharrow/beta/index.html">
            <h4>torcharrow</h4>
            <p>TorchArrow is a torch.Tensor-like Python DataFrame library for data preprocessing in deep learning. It supports multiple execution runtimes and Arrow as a common format.
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          <a href="https://pytorch.org/data/beta/index.html">
            <h4>TorchData</h4>
            <p>A Beta library of common modular data loading primitives for easily constructing flexible and performant data pipelines. And, there are a few features still in prototype stage.
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          <a href="https://pytorch.org/torchrec/">
            <h4>TorchRec</h4>
            <p>TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems (RecSys). It allows authors to train models with large embedding tables sharded across many GPUs.
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          <a href="https://pytorch.org/serve/">
            <h4>TorchServe</h4>
            <p>TorchServe is a performant, flexible and easy to use tool for serving PyTorch models in production.
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          <a href="https://pytorch.org/xla/release/2.1/index.html">
            <h4>PyTorch on XLA Devices</h4>
            <p>PyTorch runs on XLA devices, like TPUs, with the torch_xla package. This document describes how to run your models on these devices.
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          <a href="https://pytorch.org/rl">
            <h4>torchrl</h4>
            <p>torchrl is an open-source, Python-first Reinforcement Learning library for PyTorch with a focus on high modularity and good runtime performance, providing low and high-level RL abstractions and reusable functionals for cost functions, returns, and data processing.
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          <a href="https://pytorch.org/tensordict">
            <h4>tensordict</h4>
            <p>tensordict is a dictionary-like class that simplifies operations on batches of tensors, enhancing code readability, compactness, and modularity by abstracting tailored operations and reducing errors through automatic operation dispatching on the leaves.
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          <a href="https://pytorch.org/text/stable/index.html">
            <h4>torchtext</h4>
            <p>This library is part of the PyTorch project. PyTorch is an open source machine learning framework. The torchtext package consists of data processing utilities and popular datasets for natural language.
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          <a href="https://pytorch.org/executorch/stable/index.html">
            <h4>ExecuTorch</h4>
            <p>ExecuTorch is a PyTorch platform that provides infrastructure to run PyTorch programs everywhere from AR/VR wearables to standard on-device iOS and Android mobile deployments.
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          <a href="https://pytorch.org/ao/stable/index.html">
            <h4>torchao</h4>
            <p>torchao is a library for custom data types and optimizations. Quantize and sparsify weights, gradients, optimizers and activations for inference and training using native PyTorch.
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