PyTorch

PyTorch is an open-source machine learning framework designed to accelerate the path from research prototyping to production deployment. PyTorch was created to provide flexibility and speed during the development and implementation of deep learningdeep-learning neural networks. Examples of deep learning software built on top of PyTorch include Tesla's Autopilot, Uber’s Pyro, HuggingFace’s Transformers, PyTorch Lightning, and Catalyst.

PyTorch is based on the Python programming language and Torch, an open-source machine learning library, written in the Lua scripting language, used for creating deep neural networks. PyTorch is pythonic in nature, itnature—it follows a coding style that uses Python's unique features to write readable code. It enables developers to run and test a portion of code in real time instead of waiting for the entire program to be written. PyTorch supports over 200 different mathematical operations. The framework simplifies the creation of artificial neural network models and is mainly used by data scientists for research and artificial intelligence (AI) applications. PyTorch is released under a modified BSD license.

PyTorch was developed by Facebook’s AI Research lab (FAIR), which is now Meta. PyTorch development began in 2016 as an internship project by Adam Paszke while working under one of Torch's core developers, Soumith Chintala. PyTorch's original authors were its founder Adam Paszke and Soumith Chintala, as well as Sam Gross and Gregory Chanan.

The framework became popular among AI researchers, and Facebook announced plans for a new version, PyTorch 1.0, on Day 2 of F8 (Facebook’s annual developer’s conference) in May 2018. PyTorch 1.0 was released at the NeurIPS conference on December 7, 2018. The new version of the framework allowed developers to experiment rapidly and transition to graph-based modes for deployment.

On September 12, 2022, PyTorch moved to the Linux Foundation as a top-level project under the name PyTorch Foundation with a governing board of leaders, including AMD, AWS, Google Cloud, Meta, Microsoft Azure, and NVIDIA. The creation of the PyTorch Foundation aims to ensure business decisions are made in a transparent and open manner by a diverse group of members as well as improving the project's technical governance. The Linux Foundation was chosen due to its experience hosting large multi-stakeholder open-source projects. The PyTorch Foundation acts as a steward for the technology and supports PyTorch through conferences, training courses, and other initiatives. Its mission is to drive the adoption of AI tooling through an ecosystem of open-source, vendor-neutral projects with PyTorch. The foundation also focuses on the business and product marketing of PyTorch. The transition will not entail any changes to PyTorch’s code and core project, including its separate technical governance structure.

At the time of the move to the Linux Foundation, Pytorch had over 2,400 contributors and had been used as the basis for nearly 154,000 projects, becoming one of the primary platforms for AI research. Over 80% percent of researchers submitting work at major ML conferences, such as NeurIPS or ICML, utilize Pytorch. While Meta is the largest contributor to Pytorch, many companies have made foundational investments, including AMD, Amazon Web Services (AWS), Google Cloud, HuggingFace, Lightning AI, Microsoft Azure, Nvidia, and others.

PyTorch is an open-source machine learning framework designed to accelerate the path from research prototyping to production deployment. PyTorch was created to provide flexibility and speed during the development and implementation of deep learning neural networks. Examples of deep learning software built on top of PyTorch include Tesla's Autopilot, Uber’s Pyro, HuggingFace’s Transformers, PyTorch Lightning, and Catalyst.

PyTorch began development at Facebook (now Meta) in 2016. In September 2022, PyTorch moved to the Linux Foundation as a top-level project under the name PyTorch Foundation. Members and the governing board of the PyTorch Foundation include Meta, Amazon Web Services (AWS), Google Cloud, AMD, Microsoft Azure, and NVIDIA.

PyTorch is based on the Python programming language and Torch, an open-source machine learning library, written in the Lua scripting language, used for creating deep neural networks. PyTorch is pythonic in nature, it follows a coding style that uses Python's unique features to write readable code. It enables developers to run and test a portion of code in real time instead of waiting for the entire program to be written. PyTorch supports over 200 different mathematical operations. The framework simplifies the creation of artificial neural network models and is mainly used by data scientists for research and artificial intelligence (AI) applications. PyTorch is released under a modified BSD license.

PyTorch was developed by Facebook’s AI Research lab (FAIR), which is now Meta. PyTorch development began in 2016 as an internship project by Adam Paszke. He waswhile working under one of Torch's core developers Soumith Chintala. As PyTorch grew, it got two more core developers and around one hundred alpha testers from different companies and universities. PyTorch's original authors were its founder Adam Paszke and Soumith Chintala, as well as Sam Gross and Gregory Chanan.

The initial group of Meta AI researchers aimed to create a single, standardized interface for their end-to-end workflows while fixing the time-consuming research-to-production pipeline of the AI field. They experimented with machine learning frameworks such as Theano and Torch as well as advanced concepts from Lua Torch, Chainer, and HIPS Autograd. The team released the PyTorch beta to the public in January 2017.

The framework became popular among AI researchers and Facebook announced plans for a new version, PyTorch 1.0, on Day 2 of F8 (Facebook’s annual developer’s conference) in May 2018. PyTorch 1.0 was released at the NeurIPS conference on December 7, 2018. The new version of the framework allowed developers to experiment rapidly and transition to graph-based modes for deployment.

On September 12, 2022, PyTorch moved to the Linux Foundation as a top-level project under the name PyTorch Foundation with a governing board of leaders including AMD, AWS, Google Cloud, Meta, Microsoft Azure, and NVIDIA. The creation of the PyTorch Foundation aims to ensure business decisions are made in a transparent and open manner by a diverse group of members as well as improving the project's technical governance. The Linux Foundation was chosen due to its experience hosting large multi-stakeholder open-source projects The PyTorch Foundation acts as a steward for the technology and supports PyTorch through conferences, training courses, and other initiatives. Its mission is to drive the adoption of AI tooling through an ecosystem of open-source, vendor-neutral projects with PyTorch. The foundation also focuses on the business and product marketing of PyTorch. The transition will not entail any changes to PyTorch’s code and core project, including its separate technical governance structure.

At the time of the move to the Linux Foundation, Pytorch had over 2,400 contributors and had been used as the basis for nearly 154,000 projects, becoming one of the primary platforms for AI research. Over 80% of researchers submitting work at major ML conferences, such as NeurIPS or ICML, utilize Pytorch. While Meta is the largest contributor to Pytorch, many companies have made foundational investments including AMD, Amazon Web Services (AWS), Google Cloud, HuggingFace, Lightning AI, Microsoft Azure, Nvidia, and others.

PyTorch is an open-source machine learning framework designed to accelerate the path from research prototyping to production deployment. PyTorch was created to provide flexibility and speed during the development and implementation of deep learning neural networks.Examplesnetworks. Examples of deep learning software built on top of PyTorch include Tesla's Autopilot, Uber’s Pyro, HuggingFace’s Transformers, PyTorch Lightning, and Catalyst.

PyTorch is an optimized tensor library for deep learning that uses GPUs and CPUs to greatly accelerate computation speed. It is a Python-based package that provides two high-level features: tensor computation (like NumPy) with strong GPU acceleration and deep neural networks built on a tape-based autograd system. PyTorch provides a wide variety of tensor routines to accelerate and fit scientific computation needs, such as slicing, indexing, mathematical operations, linear algebra, and reductions.

In September 2022, PyTorch moved to the Linux Foundation as a top-level project under the name PyTorch Foundation. Members and the governing board of the PyTorch foundationFoundation include Meta, Amazon Web Services (AWS), Google Cloud, AMD, Microsoft Azure, and NVIDIA.

PyTorch is an open-source machine learning framework thatdesigned acceleratesto accelerate the path from research prototyping to production deployment. PyTorch was developedcreated to provide high flexibility and speed during implementingthe development and buildingimplementation of deep learning neural networksnetworks.Examples of deep learning software built on top of PyTorch include Tesla's Autopilot, Uber’s Pyro, HuggingFace’s Transformers, PyTorch Lightning, and Catalyst.

PyTorch is used for applications such as computer vision and natural language processing and was primarily developed by Facebook’s AI Research lab (FAIR). Several pieces of deep learning software are built on top of PyTorch, including Tesla's Autopilot, Uber’s Pyro, HuggingFace’s Transformers, PyTorch Lightning, and Catalyst.

In September 2022, PyTorch moved to the Linux Foundation as a top-level project under the name PyTorch Foundation. Members and the governing board of the PyTorch foundation include Meta, Amazon Web Services (AWS), Google Cloud, AMD, Microsoft Azure, and NVIDIA.

PyTorch is an optimized tensor library for deep learning usingthat uses GPUs and CPUs to greatly accelerate computation speed. It is a PythonPython-based package that provides two high-level features: tensor computation (like NumPy) with strong GPU acceleration and deep neural networks built on a tape-based autograd system. PyTorch provides tensors that can live either on the CPU or the GPU and massively accelerate computation. It provides a wide variety of tensor routines to accelerate and fit scientific computation needs such as slicing, indexing, mathematical operations, linear algebra, and reductions.

PyTorch was developed by Facebook’s AI Research lab (FAIR), which is now Meta. PyTorch development began in 2016 as an internship project by Adam Paszke in 2016. He was at Facebook, now Meta, working under one of Torch's core developers Soumith Chintala. As PyTorch grew, it got two more core developers and around one hundred alpha testers from different companies and universities. PyTorch's original authors were its founder Adam Paszke and Soumith Chintala, as well as Sam Gross, Soumith Chintala, and Gregory Chanan.

PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. It is a Python package that provides two high-level features: tensor computation (like NumPy) with strong GPU acceleration and deep neural networks built on a tape-based autograd system. PyTorch provides tensors that can live either on the CPU or the GPU and massively accelerate computation. It provides a wide variety of tensor routines to accelerate and fit scientific computation needs such as slicing, indexing, mathematicalmathematical operations, linear algebra, and reductions.

PyTorch is used for applications such as computer vision and natural language processing and was primarily developed by Facebook’s AI Research lab (FAIR). Several pieces of deep learning software are built on top of PyTorch, including Tesla's Autopilot, Uber’s Pyro, HuggingFace’s Transformers, PyTorch LightningPyTorch Lightning, and CatalystCatalyst.

In September 2022, PyTorch moved to the Linux Foundation as a top-level project under the name PyTorch FoundationPyTorch Foundation.

PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. It is a Python package that provides two high-level features: tensor computation (like NumPy) with strong GPU acceleration and deep neural networks built on a tape-based autograd system. PyTorch provides tensors that can live either on the CPU or the GPU and massively acceleratesaccelerate computation. It provides a wide variety of tensor routines to accelerate and fit scientific computation needs such as slicing, indexing, mathematical operations, linear algebra, and reductions.

PyTorch began as an internship project by Adam PaszkeAdam Paszke in 2016. He was at Facebook, now Meta, working under one of Torch's core developers Soumith ChintalaSoumith Chintala. As PyTorch grew, it got two more core developers and around one hundred alpha testers from different companies and universities. PyTorch's original authors were its founder Adam Paszke and Sam GrossSam Gross, Soumith Chintala, and Gregory ChananGregory Chanan.

PyTorch is an open-sourceopen-source machine learningmachine learning framework that accelerates the path from researchresearch prototypingprototyping to production deployment. PyTorch was developed to provide high flexibility and speed during implementing and building deep learningdeep learning neural networks.

PyTorch is used for applications such as computer visioncomputer vision and natural language processingnatural language processing and was primarily developed by Facebook’s AI Research lab (FAIR)Facebook’s AI Research lab (FAIR). A number ofSeveral pieces of deep learning software are built on top of PyTorch, including Tesla's AutopilotTesla's Autopilot, Uber’s PyroUber’s Pyro, HuggingFace’s TransformersHuggingFace’s Transformers, PyTorch Lightning, and Catalyst.

In September 2022, PyTorch moved to the Linux FoundationLinux Foundation as a top-level project under the name PyTorch Foundation.

PyTorch is an optimized tensortensor library for deep learning using GPUsGPUs and CPUsCPUs. It is a PythonPython package that provides two high-level features: tensor computation (like NumPyNumPy) with strong GPU acceleration and deep neural networks built on a tape-based autogradautograd system. PyTorch provides Tensorstensors that can live either on the CPU or the GPU and massively accelerates the computation by a huge amount. It provides a wide variety of tensor routines to accelerate and fit scientificscientific computation needs such as slicing, indexing, mathematical operations, linear algebralinear algebra, and reductions.

PyTorch Structure

PyTorch began as an internship project by Adam Paszke in October 2016. He was at Facebook, now Meta, working under one of TorchTorch working under one of its's core developers Soumith Chintala. As PyTorch grew, it got two more core developers and around one hundred alpha testers from different companies and universities. PyTorch's original authors were its founder Adam Paszke and Sam Gross, Soumith Chintala, and Gregory Chanan.

PyTorch is deep learning framework and an open source machine learning library for Python. It provides Tensor computation with strong GPU acceleration and deep neural networks built on Autograd system. PyTorch is used for applications such as natural language processing.

PyTorch is composed of the following, Tensor library like NumPy with strong Graphics processing unit (GPU) support , a tape based automatic differentiation library that supports all differentiable Tensor operations in Torch, a neural networks library deeply integrated with Autograd which is designed for maximum flexibility , an optimization package to be used with neural networks with standard optimization methods such as SGD, RMSProp, LBFGS, Adam and other methods, Python multiprocessing with memory sharing of Torch Tensors across processes for data loading and Hogwild training, DataLoader and other utility functions and a legacy code ported from Torch for backward compatibility.

PyTorch is an open-source machine learning framework that accelerates the path from research prototyping to production deployment. PyTorch was developed to provide high flexibility and speed during implementing and building deep learning neural networks.

PyTorch is used for applications such as computer vision and natural language processing and was primarily developed by Facebook’s AI Research lab (FAIR). A number of pieces of deep learning software are built on top of PyTorch, including Tesla's Autopilot, Uber’s Pyro, HuggingFace’s Transformers, PyTorch Lightning, and Catalyst.

In September 2022, PyTorch moved to the Linux Foundation as a top-level project under the name PyTorch Foundation.

What is PyTorch?

PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. It is a Python package that provides two high-level features: tensor computation (like NumPy) with strong GPU acceleration and deep neural networks built on a tape-based autograd system. PyTorch provides Tensors that can live either on the CPU or the GPU and accelerates the computation by a huge amount. It provides a wide variety of tensor routines to accelerate and fit scientific computation needs such as slicing, indexing, mathematical operations, linear algebra, and reductions.

PyTorch began as an internship project by Adam Paszke in October 2016. He was at Torch working under one of its core developers Soumith Chintala. As PyTorch grew, it got two more core developers and around one hundred alpha testers from different companies and universities. PyTorch's original authors were its founder Adam Paszke and Sam Gross, Soumith Chintala, and Gregory Chanan.