What is PyTorch? Everything we know about this machine learning framework.
Originally developed by Meta, PyTorch has evolved into a fundamental tool for machine learning and for those creating artificial intelligence models.
PyTorch is an open-source machine learning framework, ideal for developing deep learning models. It was initially created by Meta AI in 2016 and is now managed by the PyTorch Foundation, which is part of the Linux Foundation. This framework is known for its flexibility, ease of use, and the acceleration it offers through GPUs, making it a popular tool in both research and industry. Its dynamic computation graph allows developers to build and modify models instantly, making it attractive to artificial intelligence researchers, data scientists, and engineers focused on neural networks.
PyTorch simplifies the development of artificial intelligence models, and unlike other frameworks that use static computation graphs, it facilitates real-time changes to models, improving the debugging process and promoting rapid prototyping. It supports automatic differentiation, making gradient calculations in neural networks easier, and integrates seamlessly with Python, allowing smooth interaction with scientific libraries like NumPy.
This framework is widely used for training artificial intelligence models in various areas, such as computer vision, natural language processing (NLP), and reinforcement learning. The management of PyTorch was established in 2022 under the PyTorch Foundation, ensuring its long-term development under an independent and open-source structure.
The applications of PyTorch are diverse, ranging from facial recognition and object detection in the field of computer vision, to machine translation and chatbots in natural language processing. It is also useful in reinforcement learning, ideal for robotics and artificial intelligence games. PyTorch is used in production environments through frameworks like TorchServe and ONNX, and it is common in cloud AI solutions on platforms like AWS, Google Cloud, and Microsoft Azure. Additionally, it can run on edge devices, allowing AI models to operate on mobile phones, IoT devices, and embedded systems.
However, PyTorch is not designed for general programming or traditional software development, and its use requires a solid understanding of machine learning and deep learning concepts. It is not a no-code AI solution and, unlike high-level platforms like AutoML, it does not automate model building, meaning that users must manually set up and optimize networks. Although it is improving in production environments, TensorFlow remains the preferred choice for large-scale deployments due to its static graph optimizations and enterprise-level support.
PyTorch is completely free and open-source under an MIT-style license, allowing for unrestricted use, modification, and distribution. While there are no paid plans, costs may arise when running it on cloud services, depending on GPU usage.
This framework is compatible with operating systems like Windows, macOS, and Linux, and can run on both CPU and GPU, including Nvidia CUDA, AMD ROCm, and Apple Metal. It integrates well with tools like Jupyter Notebooks and deep learning platforms like Google Colab.
In terms of reputation, PyTorch is one of the most respected deep learning frameworks, especially in academic and research settings, as its dynamic computation graph provides unparalleled flexibility. Researchers appreciate its user-friendly Python interface, which effectively communicates with other AI libraries.
PyTorch is an excellent choice if you are looking for a framework that offers flexibility and support for experimental deep learning, being particularly useful in fields such as computer vision, NLP, and reinforcement learning. However, those new to the AI field who require a user-friendly platform with an automation focus may find TensorFlow more suitable, as it offers a broader range of support for production deployments.
Despite being a powerful tool, there are alternatives that can be considered, including TensorFlow and JAX, which provide significant capabilities for AI development. Additionally, Hugging Face Transformers is a good choice for anyone looking for a high-level library for NLP or computer vision.
