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<title>PyTorch</title>

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PyTorch

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</style><table class="infobox vevent"><tbody><tr><th colspan="2" class="infobox-above summary">PyTorch</th></tr><tr><td colspan="2" class="infobox-image logo"></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="Programmer" title="Programmer">Original author(s)</a></th><td class="infobox-data"><style data-mw-deduplicate="TemplateStyles:r1126788409">

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</style><div class="plainlist"><ul><li>Adam Paszke</li><li>Sam Gross</li><li>Soumith Chintala</li><li>Gregory Chanan</li></ul></div></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="Programmer" title="Programmer">Developer(s)</a></th><td class="infobox-data"><a href="Meta_AI" title="Meta AI">Meta AI</a></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;">Initial release</th><td class="infobox-data">September 2016 (2016-09)<a href="#cite_note-1">[1]</a></td></tr><tr style="display: none;"><td colspan="2" class="infobox-full-data"></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="Software_release_life_cycle" title="Software release life cycle">Stable release</a></th><td class="infobox-data"><div style="margin:0px;">2.8.0<a href="#cite_note-wikidata-41777efb9679fe2123164822c649df046eb07ade-v20-2">[2]</a>

/ 6 August 2025 (6 August 2025)</div></td></tr><tr style="display:none"><td colspan="2">

</td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="Repository_(version_control)" title="Repository (version control)">Repository</a></th><td class="infobox-data"><a rel="nofollow" class="external text" href="https://github.com/pytorch/pytorch">github.com/pytorch/pytorch</a></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;">Written in</th><td class="infobox-data"><div class="plainlist"><ul><li><a href="Python_(programming_language)" title="Python (programming language)">Python</a></li><li><a href="C%2B%2B" title="C++">C++</a></li><li><a href="CUDA" title="CUDA">CUDA</a></li></ul></div></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="Operating_system" title="Operating system">Operating system</a></th><td class="infobox-data"><div class="plainlist"><ul><li><a href="Linux" title="Linux">Linux</a></li><li><a href="MacOS" title="MacOS">macOS</a></li><li><a href="Windows" class="mw-redirect" title="Windows">Windows</a></li></ul></div></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="Computing_platform" title="Computing platform">Platform</a></th><td class="infobox-data"><a href="IA-32" title="IA-32">IA-32</a>, <a href="X86-64" title="X86-64">x86-64</a>, <a href="ARM64" class="mw-redirect" title="ARM64">ARM64</a></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;">Available in</th><td class="infobox-data">English</td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="Software_categories#Categorization_approaches" title="Software categories">Type</a></th><td class="infobox-data"><a href="Library_(computing)" title="Library (computing)">Library</a> for <a href="Machine_learning" title="Machine learning">machine learning</a> and <a href="Deep_learning" title="Deep learning">deep learning</a></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="Software_license" title="Software license">License</a></th><td class="infobox-data"><a href="BSD-3" class="mw-redirect" title="BSD-3">BSD-3</a><a href="#cite_note-3">[3]</a></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;">Website</th><td class="infobox-data"><a rel="nofollow" class="external text" href="https://pytorch.org/">pytorch.org</a></td></tr></tbody></table>

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</style><table class="sidebar sidebar-collapse nomobile nowraplinks"><tbody><tr><td class="sidebar-pretitle">Part of a series on</td></tr><tr><th class="sidebar-title-with-pretitle"><a href="Machine_learning" title="Machine learning">Machine learning</a> and <a href="Data_mining" title="Data mining">data mining</a></th></tr><tr><td class="sidebar-content">

<ul><li><a href="Supervised_learning" title="Supervised learning">Supervised learning</a></li>

<li><a href="Unsupervised_learning" title="Unsupervised learning">Unsupervised learning</a></li>

<li><a href="Semi-supervised_learning" class="mw-redirect" title="Semi-supervised learning">Semi-supervised learning</a></li>

<li><a href="Self-supervised_learning" title="Self-supervised learning">Self-supervised learning</a></li>

<li><a href="Reinforcement_learning" title="Reinforcement learning">Reinforcement learning</a></li>

<li><a href="Meta-learning_(computer_science)" title="Meta-learning (computer science)">Meta-learning</a></li>

<li><a href="Online_machine_learning" title="Online machine learning">Online learning</a></li>

<li><a href="Batch_learning" class="mw-redirect" title="Batch learning">Batch learning</a></li>

<li><a href="Curriculum_learning" title="Curriculum learning">Curriculum learning</a></li>

<li><a href="Rule-based_machine_learning" title="Rule-based machine learning">Rule-based learning</a></li>

<li><a href="Neuro-symbolic_AI" title="Neuro-symbolic AI">Neuro-symbolic AI</a></li>

<li><a href="Neuromorphic_engineering" class="mw-redirect" title="Neuromorphic engineering">Neuromorphic engineering</a></li>

<li><a href="Quantum_machine_learning" title="Quantum machine learning">Quantum machine learning</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<ul><li><a href="Statistical_classification" title="Statistical classification">Classification</a></li>

<li><a href="Generative_model" title="Generative model">Generative modeling</a></li>

<li><a href="Regression_analysis" title="Regression analysis">Regression</a></li>

<li><a href="Cluster_analysis" title="Cluster analysis">Clustering</a></li>

<li><a href="Dimensionality_reduction" title="Dimensionality reduction">Dimensionality reduction</a></li>

<li><a href="Density_estimation" title="Density estimation">Density estimation</a></li>

<li><a href="Anomaly_detection" title="Anomaly detection">Anomaly detection</a></li>

<li><a href="Data_cleaning" class="mw-redirect" title="Data cleaning">Data cleaning</a></li>

<li><a href="Automated_machine_learning" title="Automated machine learning">AutoML</a></li>

<li><a href="Association_rule_learning" title="Association rule learning">Association rules</a></li>

<li><a href="Semantic_analysis_(machine_learning)" title="Semantic analysis (machine learning)">Semantic analysis</a></li>

<li><a href="Structured_prediction" title="Structured prediction">Structured prediction</a></li>

<li><a href="Feature_engineering" title="Feature engineering">Feature engineering</a></li>

<li><a href="Feature_learning" title="Feature learning">Feature learning</a></li>

<li><a href="Learning_to_rank" title="Learning to rank">Learning to rank</a></li>

<li><a href="Grammar_induction" title="Grammar induction">Grammar induction</a></li>

<li><a href="Ontology_learning" title="Ontology learning">Ontology learning</a></li>

<li><a href="Multimodal_learning" title="Multimodal learning">Multimodal learning</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><div style="display: inline-block; line-height: 1.2em; padding: .1em 0;"><a href="Supervised_learning" title="Supervised learning">Supervised learning</a> (<a href="Statistical_classification" title="Statistical classification">classification</a> • <a href="Regression_analysis" title="Regression analysis">regression</a>) </div></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Apprenticeship_learning" title="Apprenticeship learning">Apprenticeship learning</a></li>

<li><a href="Decision_tree_learning" title="Decision tree learning">Decision trees</a></li>

<li><a href="Ensemble_learning" title="Ensemble learning">Ensembles</a>

<ul><li><a href="Bootstrap_aggregating" title="Bootstrap aggregating">Bagging</a></li>

<li><a href="Boosting_(machine_learning)" title="Boosting (machine learning)">Boosting</a></li>

<li><a href="Random_forest" title="Random forest">Random forest</a></li></ul></li>

<li><a href="Linear_regression" title="Linear regression">Linear regression</a></li>

<li><a href="Naive_Bayes_classifier" title="Naive Bayes classifier">Naive Bayes</a></li>

<li><a href="Artificial_neural_network" class="mw-redirect" title="Artificial neural network">Artificial neural networks</a></li>

<li><a href="Logistic_regression" title="Logistic regression">Logistic regression</a></li>

<li><a href="Perceptron" title="Perceptron">Perceptron</a></li>

<li><a href="Relevance_vector_machine" title="Relevance vector machine">Relevance vector machine (RVM)</a></li>

<li><a href="Support_vector_machine" title="Support vector machine">Support vector machine (SVM)</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="Cluster_analysis" title="Cluster analysis">Clustering</a></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="BIRCH" title="BIRCH">BIRCH</a></li>

<li><a href="Hierarchical_clustering" title="Hierarchical clustering">Hierarchical</a></li>

<li><a href="K-means_clustering" title="K-means clustering">k-means</a></li>

<li><a href="Fuzzy_clustering" title="Fuzzy clustering">Fuzzy</a></li>

<li><a href="Expectation%E2%80%93maximization_algorithm" title="Expectation–maximization algorithm">Expectation–maximization (EM)</a></li>

<li> <a href="DBSCAN" title="DBSCAN">DBSCAN</a></li>

<li><a href="OPTICS_algorithm" title="OPTICS algorithm">OPTICS</a></li>

<li><a href="Mean_shift" title="Mean shift">Mean shift</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="Dimensionality_reduction" title="Dimensionality reduction">Dimensionality reduction</a></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Factor_analysis" title="Factor analysis">Factor analysis</a></li>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="Structured_prediction" title="Structured prediction">Structured prediction</a></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Graphical_model" title="Graphical model">Graphical models</a>

<ul><li><a href="Bayesian_network" title="Bayesian network">Bayes net</a></li>

<li><a href="Conditional_random_field" title="Conditional random field">Conditional random field</a></li>

<li><a href="Hidden_Markov_model" title="Hidden Markov model">Hidden Markov</a></li></ul></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="Anomaly_detection" title="Anomaly detection">Anomaly detection</a></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Random_sample_consensus" title="Random sample consensus">RANSAC</a></li>

<li><a href="Local_outlier_factor" title="Local outlier factor">Local outlier factor</a></li>

<li><a href="Isolation_forest" title="Isolation forest">Isolation forest</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="Neural_network_(machine_learning)" title="Neural network (machine learning)">Neural networks</a></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Autoencoder" title="Autoencoder">Autoencoder</a></li>

<li><a href="Deep_learning" title="Deep learning">Deep learning</a></li>

<li><a href="Feedforward_neural_network" title="Feedforward neural network">Feedforward neural network</a></li>

<li><a href="Recurrent_neural_network" title="Recurrent neural network">Recurrent neural network</a>

<li><a href="Reservoir_computing" title="Reservoir computing">reservoir computing</a></li></ul></li>

<li><a href="Boltzmann_machine" title="Boltzmann machine">Boltzmann machine</a>

<ul><li><a href="Restricted_Boltzmann_machine" title="Restricted Boltzmann machine">Restricted</a></li></ul></li>

<li><a href="Diffusion_model" title="Diffusion model">Diffusion model</a></li>

<li><a href="Convolutional_neural_network" title="Convolutional neural network">Convolutional neural network</a>

<li><a href="LeNet" title="LeNet">LeNet</a></li>

<li><a href="AlexNet" title="AlexNet">AlexNet</a></li>

<li><a href="DeepDream" title="DeepDream">DeepDream</a></li></ul></li>

<li><a href="Neural_field" title="Neural field">Neural field</a>

<ul><li><a href="Neural_radiance_field" title="Neural radiance field">Neural radiance field</a></li>

<li><a href="Physics-informed_neural_networks" title="Physics-informed neural networks">Physics-informed neural networks</a></li></ul></li>

<li><a href="Transformer_(deep_learning_architecture)" title="Transformer (deep learning architecture)">Transformer</a>

<ul><li><a href="Vision_transformer" title="Vision transformer">Vision</a></li></ul></li>

<li><a href="Mamba_(deep_learning_architecture)" title="Mamba (deep learning architecture)">Mamba</a></li>

<li><a href="Spiking_neural_network" title="Spiking neural network">Spiking neural network</a></li>

<li><a href="Memtransistor" title="Memtransistor">Memtransistor</a></li>

<li><a href="Electrochemical_RAM" title="Electrochemical RAM">Electrochemical RAM</a> (ECRAM)</li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="Reinforcement_learning" title="Reinforcement learning">Reinforcement learning</a></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Q-learning" title="Q-learning">Q-learning</a></li>

<li><a href="Policy_gradient_method" title="Policy gradient method">Policy gradient</a></li>

<li><a href="State%E2%80%93action%E2%80%93reward%E2%80%93state%E2%80%93action" title="State–action–reward–state–action">SARSA</a></li>

<li><a href="Temporal_difference_learning" title="Temporal difference learning">Temporal difference (TD)</a></li>

<li><a href="Multi-agent_reinforcement_learning" title="Multi-agent reinforcement learning">Multi-agent</a>

</tr><tr><td class="sidebar-content">

<ul><li><a href="Active_learning_(machine_learning)" title="Active learning (machine learning)">Active learning</a></li>

<li><a href="Crowdsourcing" title="Crowdsourcing">Crowdsourcing</a></li>

<li><a href="Human-in-the-loop" title="Human-in-the-loop">Human-in-the-loop</a></li>

<li><a href="Mechanistic_interpretability" title="Mechanistic interpretability">Mechanistic interpretability</a></li>

</tr><tr><td class="sidebar-content">

<ul><li><a href="Coefficient_of_determination" title="Coefficient of determination">Coefficient of determination</a></li>

<li><a href="Confusion_matrix" title="Confusion matrix">Confusion matrix</a></li>

<li><a href="Learning_curve_(machine_learning)" title="Learning curve (machine learning)">Learning curve</a></li>

<li><a href="Receiver_operating_characteristic" title="Receiver operating characteristic">ROC curve</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)">Mathematical foundations</div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Kernel_machines" class="mw-redirect" title="Kernel machines">Kernel machines</a></li>

<li><a href="Bias%E2%80%93variance_tradeoff" title="Bias–variance tradeoff">Bias–variance tradeoff</a></li>

<li><a href="Computational_learning_theory" title="Computational learning theory">Computational learning theory</a></li>

<li><a href="Empirical_risk_minimization" title="Empirical risk minimization">Empirical risk minimization</a></li>

<li><a href="Occam_learning" title="Occam learning">Occam learning</a></li>

<li><a href="Probably_approximately_correct_learning" title="Probably approximately correct learning">PAC learning</a></li>

<li><a href="Statistical_learning_theory" title="Statistical learning theory">Statistical learning</a></li>

<li><a href="Vapnik%E2%80%93Chervonenkis_theory" title="Vapnik–Chervonenkis theory">VC theory</a></li>

<li><a href="Topological_deep_learning" title="Topological deep learning">Topological deep learning</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)">Journals and conferences</div><div class="sidebar-list-content mw-collapsible-content hlist">

<li><a href="Conference_on_Neural_Information_Processing_Systems" title="Conference on Neural Information Processing Systems">NeurIPS</a></li>

<li><a href="International_Joint_Conference_on_Artificial_Intelligence" title="International Joint Conference on Artificial Intelligence">IJCAI</a></li>

</tr><tr><td class="sidebar-content">

<ul><li><a href="Glossary_of_artificial_intelligence" title="Glossary of artificial intelligence">Glossary of artificial intelligence</a></li>

<li><a href="List_of_datasets_for_machine-learning_research" title="List of datasets for machine-learning research">List of datasets for machine-learning research</a>

<ul><li><a href="List_of_datasets_in_computer_vision_and_image_processing" title="List of datasets in computer vision and image processing">List of datasets in computer vision and image processing</a></li></ul></li>

<li><a href="Outline_of_machine_learning" title="Outline of machine learning">Outline of machine learning</a></li></ul></div></div></td>

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PyTorch is an <a href="Open_source" title="Open source">open-source</a> <a href="Machine_learning" title="Machine learning">machine learning</a> <a href="Library_(computing)" title="Library (computing)">library</a> based on the <a href="Torch_(machine_learning)" title="Torch (machine learning)">Torch</a> library,<a href="#cite_note-4">[4]</a><a href="#cite_note-5">[5]</a><a href="#cite_note-6">[6]</a> used for applications such as <a href="Computer_vision" title="Computer vision">computer vision</a>, deep learning research<a href="#cite_note-:0-7">[7]</a> and <a href="Natural_language_processing" title="Natural language processing">natural language processing</a>,<a href="#cite_note-:0-7">[7]</a> originally developed by <a href="Meta_AI" title="Meta AI">Meta AI</a> and now part of the <a href="Linux_Foundation" title="Linux Foundation">Linux Foundation</a> umbrella.<a href="#cite_note-8">[8]</a><a href="#cite_note-:1-9">[9]</a><a href="#cite_note-10">[10]</a><a href="#cite_note-11">[11]</a> It is one of the most popular <a href="Deep_learning" title="Deep learning">deep learning</a> frameworks, alongside others such as <a href="TensorFlow" title="TensorFlow">TensorFlow</a>,<a href="#cite_note-12">[12]</a> offering <a href="Free_and_open-source_software" title="Free and open-source software">free and open-source software</a> released under the <a href="Modified_BSD_license" class="mw-redirect" title="Modified BSD license">modified BSD license</a>. Although the <a href="Python_(programming_language)" title="Python (programming language)">Python</a> interface is more polished and the primary focus of development, PyTorch also has a <a href="C%2B%2B" title="C++">C++</a> interface.<a href="#cite_note-13">[13]</a>

PyTorch utilises <a href="Tensor" title="Tensor">tensors</a> as a intrinsic datatype, very similar to <a href="NumPy" title="NumPy">NumPy</a>. Model training is handled by an <a href="Automatic_differentiation" title="Automatic differentiation">automatic differentiation</a> system, Autograd, which constructs a <a href="Directed_acyclic_graph" title="Directed acyclic graph">directed acyclic graph</a> of a forward pass of a model for a given input, for which automatic differentiation utilising the <a href="Chain_rule" title="Chain rule">chain rule</a>, computes model-wide gradients.<a href="#cite_note-14">[14]</a> PyTorch is capable of transparant leveraging of <a href="SIMD" class="mw-redirect" title="SIMD">SIMD</a> units, such as <a href="General-purpose_computing_on_graphics_processing_units" class="mw-disambig" title="General-purpose computing on graphics processing units">GPGPUs</a>.

A number of commercial <a href="Deep_learning" title="Deep learning">deep learning</a> archetectures are built on top of PyTorch, including <a href="Tesla_Autopilot" title="Tesla Autopilot">Tesla Autopilot</a>,<a href="#cite_note-15">[15]</a> <a href="Uber" title="Uber">Uber</a>'s Pyro,<a href="#cite_note-16">[16]</a> <a href="Hugging_Face" title="Hugging Face">Hugging Face</a>'s Transformers,<a href="#cite_note-17">[17]</a><a href="#cite_note-18">[18]</a> and Catalyst.<a href="#cite_note-19">[19]</a><a href="#cite_note-20">[20]</a>

<div class="mw-heading mw-heading2"><h2 id="History">History</h2></div>

In 2001, Torch was written and released under a <a href="GNU_General_Public_License" title="GNU General Public License">GPL license</a>. It was a machine-learning library written in C++, supporting methods including neural networks, <a href="Support_vector_machine" title="Support vector machine">SVM</a>, <a href="Hidden_Markov_model" title="Hidden Markov model">hidden Markov models</a>, etc.<a href="#cite_note-21">[21]</a><a href="#cite_note-22">[22]</a><a href="#cite_note-23">[23]</a> It was improved to Torch7 in 2012.<a href="#cite_note-24">[24]</a> Development on Torch ceased in 2018 and was subsumed by the PyTorch project.<a href="#cite_note-25">[25]</a>

Meta (formerly known as Facebook) operates both PyTorch and Convolutional Architecture for Fast Feature Embedding (<a href="Caffe_(software)" title="Caffe (software)">Caffe2</a>), but models defined by the two frameworks were mutually incompatible. The Open Neural Network Exchange (<a href="Open_Neural_Network_Exchange" title="Open Neural Network Exchange">ONNX</a>) project was created by Meta and <a href="Microsoft" title="Microsoft">Microsoft</a> in September 2017 for converting models between frameworks. Caffe2 was merged into PyTorch at the end of March 2018.<a href="#cite_note-26">[26]</a> In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the <a href="Linux_Foundation" title="Linux Foundation">Linux Foundation</a>.<a href="#cite_note-27">[27]</a>

PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo, a Python-level <a href="Compiler" title="Compiler">compiler</a> that makes code run up to 2x faster, along with significant improvements in training and inference performance across major <a href="Cloud_computing" title="Cloud computing">cloud platforms</a>.<a href="#cite_note-28">[28]</a><a href="#cite_note-29">[29]</a>

<div class="mw-heading mw-heading2"><h2 id="PyTorch_tensors">PyTorch tensors</h2></div>

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</style><div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Tensor_(machine_learning)" title="Tensor (machine learning)">Tensor (machine learning)</a></div>

PyTorch defines a class called Tensor (<code>torch.Tensor</code>) to store and operate on homogeneous multidimensional rectangular arrays of numbers. PyTorch Tensors are similar to <a href="NumPy" title="NumPy">NumPy</a> Arrays, but can also be operated on a <a href="CUDA" title="CUDA">CUDA</a>-capable <a href="Nvidia" title="Nvidia">NVIDIA</a> GPU. PyTorch has also been developing support for other GPU platforms, for example, AMD's <a href="ROCm" title="ROCm">ROCm</a><a href="#cite_note-30">[30]</a> and Apple's <a href="Metal_(API)" title="Metal (API)">Metal Framework.</a><a href="#cite_note-31">[31]</a>

PyTorch supports various sub-types of Tensors.<a href="#cite_note-32">[32]</a>

Note that the term "tensor" here does not carry the same meaning as tensor in mathematics or physics. The meaning of the word in machine learning is only superficially related to its original meaning as a certain kind of object in <a href="Linear_algebra" title="Linear algebra">linear algebra</a>. Tensors in PyTorch are simply multi-dimensional arrays.

<div class="mw-heading mw-heading2"><h2 id="PyTorch_neural_networks">PyTorch neural networks</h2></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Neural_network_(machine_learning)" title="Neural network (machine learning)">Neural network (machine learning)</a></div>

PyTorch defines a module called nn (<code>torch.nn</code>) to describe neural networks and to support training. This module offers a comprehensive collection of building blocks for neural networks, including various layers and activation functions, enabling the construction of complex models. Networks are built by inheriting from the <code>torch.nn</code> module and defining the sequence of operations in the <code>forward()</code> function.

<div class="mw-heading mw-heading2"><h2 id="Example">Example</h2></div>

The following program shows the low-level functionality of the library with a simple example.

<div class="mw-highlight mw-highlight-lang-numpy mw-content-ltr mw-highlight-lines" dir="ltr"><pre>import torch

dtype = torch.float

device = torch.device("cpu") # Execute all calculations on the CPU

# device = torch.device("cuda:0") # Executes all calculations on the GPU

# Create a tensor and fill it with random numbers

a = torch.randn(2, 3, device=device, dtype=dtype)

print(a)

# Output: tensor([[-1.1884, 0.8498, -1.7129],

# [-0.8816, 0.1944, 0.5847]])

b = torch.randn(2, 3, device=device, dtype=dtype)

print(b)

# Output: tensor([[ 0.7178, -0.8453, -1.3403],

# [ 1.3262, 1.1512, -1.7070]])

print(a * b)

# Output: tensor([[-0.8530, -0.7183, 2.58],

# [-1.1692, 0.2238, -0.9981]])

print(a.sum())

# Output: tensor(-2.1540)

print(a[1, 2]) # Output of the element in the third column of the second row (zero-based)

# Output: tensor(0.5847)

print(a.max())

# Output: tensor(0.8498)

</pre></div>

The following code-block defines a neural network with linear layers using the <code>nn</code> module.

<div class="mw-highlight mw-highlight-lang-python3 mw-content-ltr mw-highlight-lines" dir="ltr"><pre>from torch import nn # Import the nn sub-module from PyTorch

class NeuralNetwork(nn.Module): # Neural networks are defined as classes

def __init__(self): # Layers and variables are defined in the __init__ method

super().__init__() # Must be in every network.

self.flatten = nn.Flatten() # Construct a flattening layer.

self.linear_relu_stack = nn.Sequential( # Construct a stack of layers.

nn.Linear(28 * 28, 512), # Linear Layers have an input and output shape

nn.ReLU(), # ReLU is one of many activation functions provided by nn

nn.Linear(512, 512),

nn.ReLU(),

)

def forward(self, x): # This function defines the forward pass.

x = self.flatten(x)

logits = self.linear_relu_stack(x)

return logits

</pre></div>

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<ul><li><a href="Comparison_of_deep_learning_software" title="Comparison of deep learning software">Comparison of deep learning software</a></li>

<li><a href="Differentiable_programming" title="Differentiable programming">Differentiable programming</a></li>

<li><a href="DeepSpeed" title="DeepSpeed">DeepSpeed</a></li></ul>

<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

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</ol></div></div>

<div class="mw-heading mw-heading2"><h2 id="External_links">External links</h2></div>

<ul><li><a rel="nofollow" class="external text" href="https://pytorch.org">Official website</a></li></ul>

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<ul><li><a href="Comparison_of_deep_learning_software" title="Comparison of deep learning software">Comparison</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Open-source_software" title="Open-source software">Open source</a></th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Apache_MXNet" title="Apache MXNet">Apache MXNet</a></li>

<li><a href="Apache_SINGA" title="Apache SINGA">Apache SINGA</a></li>

<li><a href="Caffe_(software)" title="Caffe (software)">Caffe</a></li>

<li><a href="Deeplearning4j" title="Deeplearning4j">Deeplearning4j</a></li>

<li><a href="DeepSpeed" title="DeepSpeed">DeepSpeed</a></li>

<li><a href="Keras" title="Keras">Keras</a></li>

<li><a href="Microsoft_Cognitive_Toolkit" title="Microsoft Cognitive Toolkit">Microsoft Cognitive Toolkit</a></li>

<li><a href="OpenNN" title="OpenNN">OpenNN</a></li>

<li><a href="TensorFlow" title="TensorFlow">TensorFlow</a></li>

<li><a href="Theano_(software)" title="Theano (software)">Theano</a></li>

<li><a href="Torch_(machine_learning)" title="Torch (machine learning)">Torch</a></li>

<li><a href="OpenVINO" title="OpenVINO">OpenVINO</a></li>

<li><a href="MindSpore" title="MindSpore">MindSpore</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Proprietary_software" title="Proprietary software">Proprietary</a></th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Apple_Inc." title="Apple Inc.">Apple</a> <a href="Core_ML" class="mw-redirect" title="Core ML">Core ML</a></li>

<li><a href="Watson_(computer)" class="mw-redirect" title="Watson (computer)">IBM Watson</a></li>

<li><a href="Neural_Designer" title="Neural Designer">Neural Designer</a></li>

<li><a href="Wolfram_Mathematica" class="mw-redirect" title="Wolfram Mathematica">Wolfram Mathematica</a></li>

<li><a href="MATLAB" title="MATLAB">MATLAB</a> Deep Learning Toolbox</li></ul>

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<ul><li> Category</li></ul>

</div></td></tr></tbody></table></div>

<div class="navbox-styles"></div><div role="navigation" class="navbox" aria-labelledby="Differentiable_computing254" style="padding:3px"><table class="nowraplinks hlist mw-collapsible autocollapse navbox-inner" style="border-spacing:0;background:transparent;color:inherit"><tbody><tr><th scope="col" class="navbox-title" colspan="2"><div id="Differentiable_computing254" style="font-size:114%;margin:0 4em">Differentiable computing</div></th></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Differentiable_function" title="Differentiable function">General</a></th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Differentiable_programming" title="Differentiable programming">Differentiable programming</a></li>

<li><a href="Information_geometry" title="Information geometry">Information geometry</a></li>

<li><a href="Statistical_manifold" title="Statistical manifold">Statistical manifold</a></li>

<li><a href="Automatic_differentiation" title="Automatic differentiation">Automatic differentiation</a></li>

<li><a href="Neuromorphic_computing" title="Neuromorphic computing">Neuromorphic computing</a></li>

<li><a href="Pattern_recognition" title="Pattern recognition">Pattern recognition</a></li>

<li><a href="Ricci_calculus" title="Ricci calculus">Ricci calculus</a></li>

<li><a href="Computational_learning_theory" title="Computational learning theory">Computational learning theory</a></li>

<li><a href="Inductive_bias" title="Inductive bias">Inductive bias</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Hardware</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em">

<li><a href="Memristor" title="Memristor">Memristor</a></li>

<li><a href="SpiNNaker" title="SpiNNaker">SpiNNaker</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Software libraries</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="TensorFlow" title="TensorFlow">TensorFlow</a></li>

<li><a href="Keras" title="Keras">Keras</a></li>

<li><a href="Scikit-learn" title="Scikit-learn">scikit-learn</a></li>

<li><a href="Theano_(software)" title="Theano (software)">Theano</a></li>

<li><a href="MindSpore" title="MindSpore">MindSpore</a></li></ul>

</div></td></tr><tr><td class="navbox-abovebelow" colspan="2"><div>

<ul><li> Portals

<ul><li><a href="Portal%3AComputer_programming" title="Portal:Computer programming">Computer programming</a></li>

<li><a href="Portal%3ATechnology" title="Portal:Technology">Technology</a></li></ul></li></ul>

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