Originally created by Meta, PyTorch has become an important tool for machine learning and people developing AI models
PyTorch is an open source machine learning framework used for developing deep learning models.
Originally created by Meta AI (the Facebook owner's AI research arm) in 2016, it is now maintained under the PyTorch Foundation within the Linux Foundation.
Known for its flexibility, ease of use, and GPU acceleration, PyTorch is widely adopted in both research and industry. Its dynamic computation graph helps developers build and modify models on the fly, making it a preferred choice for AI researchers, data scientists, and engineers working in neural networks.
This article was correct as of February 2025. AI tools are updated regularly and it is possible that some features have changed since this article was written. Some features may also only be available in certain countries.
PyTorch is a deep learning framework designed to simplify AI model development. First released by Meta AI, it was built to improve the flexibility of deep learning research.
Unlike frameworks that use static computation graphs, PyTorch uses a dynamic computation graph, allowing for real-time model changes, easier debugging, and faster prototyping, making PyTorch highly suitable for research and experimentation.
The framework supports automatic differentiation, making gradient calculations for neural networks seamless, and also integrates well with Python, enabling smooth interaction with NumPy and other scientific computing libraries.
PyTorch is widely used for training AI models in fields such as computer vision, natural language processing (NLP), and reinforcement learning.
In 2022, governance of PyTorch shifted to the PyTorch Foundation, ensuring long-term development under an independent, open source structure.
PyTorch is used for building and training deep learning models across multiple domains. In computer vision, it enables applications like facial recognition, object detection, and medical imaging, while in natural language processing, it powers machine translation, text summarisation, and chatbots.
The tool also supports reinforcement learning, making it ideal for robotics and AI-driven gaming.
Beyond research, PyTorch is deployed in production environments through frameworks like TorchServe and ONNX, and is widely used in cloud-based AI solutions on AWS, Google Cloud, and Microsoft Azure.
Additionally, PyTorch can run on edge devices, allowing AI models to function on mobile phones, IoT devices, and embedded systems.
PyTorch is not designed for general-purpose programming or traditional software development. Importantly, it requires knowledge of machine learning and deep learning concepts, making it unsuitable for those looking for a no-code AI solution.
Unlike high-level platforms like AutoML, PyTorch does not automate model building, requiring users to manually configure and optimise networks.
While it is improving in production environments, TensorFlow remains the preferred choice for large-scale deployments due to its static graph optimisations and enterprise-level support.
PyTorch is completely free and open source under an MIT-style licence, allowing unrestricted use, modification, and distribution.
There are no paid plans, and all features, including GPU acceleration and model training capabilities, are accessible at no cost.
However, running PyTorch on cloud services like AWS, Google Cloud, or Microsoft Azure incurs infrastructure costs. Depending on usage, GPU-based training can range from a few cents to hundreds of dollars per hour.
PyTorch runs on Windows, macOS, and Linux, supporting both CPUs and GPUs with Nvidia CUDA, AMD ROCm, and Apple Metal.
It integrates seamlessly with Python, Jupyter Notebooks, and deep learning platforms like Google Colab. PyTorch models can also be deployed on cloud platforms, mobile applications, and edge computing devices.
PyTorch is one of the most widely respected deep learning frameworks, particularly in academic and research settings. Its dynamic computation graph provides unmatched flexibility, making it ideal for rapid prototyping and debugging.
Researchers appreciate its Pythonic interface, which integrates well with other AI libraries, such as Hugging Face Transformers.
While PyTorch excels in research, its deployment tools are less mature than TensorFlow's, which is often preferred for enterprise AI applications.
Overall, it is a powerful and evolving framework that balances usability with deep customisation, making it a top choice for AI professionals.
You should use PyTorch if you are a machine learning researcher or AI engineer who values flexibility and a Python-friendly interface.
PyTorch is ideal for tasks that require dynamic computation graphs, such as experimental deep learning models and real-time AI applications. If you work in computer vision, NLP, or reinforcement learning, PyTorch provides strong tools and community support.
It is also a great choice if you need a framework that integrates well with cloud platforms and offers extensive GPU acceleration for training AI models efficiently.
PyTorch may not be the best choice if you are new to AI and need a beginner-friendly platform with built-in automation.
If you require enterprise-level AI deployment, TensorFlow is often preferred due to its static graph optimisations and broader production support.
PyTorch also lacks low-code and no-code AI tools, meaning users must be comfortable with Python and deep learning concepts.
While PyTorch is an excellent deep learning framework, there are other options worth exploring. TensorFlow, developed by Google, is a strong alternative, particularly for large-scale AI deployments and mobile applications.
JAX, another Google-backed framework, offers cutting-edge performance for deep learning and differentiable programming.
If you need a high-level library for natural language processing or computer vision, Hugging Face Transformers provides pre-trained models and APIs that simplify AI development while still using PyTorch and TensorFlow.
