PyTorch is a popular open-source Python-based framework for machine learning. PyTorch provides a flexible and dynamic approach to building and training neural networks. It has gained significant popularity in the research community and is widely used in academia and industry. PyTorch currently has over 72.3k stars on GitHub source code.
PyTorch is designed to provide good flexibility and high speeds for deep neural network implementation. PyTorch is different from other deep learning frameworks in that it uses dynamic computation graphs. While static computational graphs (like those used in TensorFlow) are defined prior to runtime, dynamic graphs are defined "on the fly" via the forward computation.
PyTorch offers a user-friendly and intuitive interface, making it easier to understand and implement complex deep learning models. Its dynamic computational graph allows for easy debugging and prototyping.
PyTorch is built using Python, which is a popular and versatile programming language. If you are already familiar with Python, you'll find it relatively easy to get started with PyTorch and leverage your existing Python knowledge.
PyTorch has a vibrant and active community of developers, researchers, and enthusiasts who contribute to its growth and provide support through forums, tutorials, and libraries. The ecosystem around PyTorch is continuously expanding, offering a wide range of pre-trained models, tools, and resources.
PyTorch has gained popularity in the research community due to its flexibility and dynamic nature. Many state-of-the-art models and research papers are implemented using PyTorch, making it an ideal choice if you're interested in exploring cutting-edge techniques.
PyTorch provides a rich set of tools and functionalities for building and training deep neural networks. It supports various types of layers, activation functions, loss functions, and optimization algorithms, allowing you to design and experiment with complex architectures.
PyTorch provides seamless integration with other popular frameworks, such as ONNX and TensorFlow, allowing you to easily deploy your trained models on different platforms and devices.
PyTorch is deeply integrated with Python, so many Python debugging tools can be easily used with it. For example, the Python pdb and ipdb tools can be used to debug PyTorch code. PyCharm’s debugger also works seamlessly with PyTorch code.
Several of the world’s biggest technology companies such as OpenAI (creators of GPT-3), Microsoft, Amazon, Tesla, Meta (Facebook and Instagram), Airbnb, Apple and many more large companies use PyTorch to power their machine learning products.
The computer vision algorithms in Tesla’s self-driving system, Autopilot, are built using PyTorch. The translation, speech-to-text, feed-curation and voice recognition algorithms inside Facebook and Instagram are created with PyTorch.
Machine learning requires performing mathematical operations on a large scale, and these operations require a lot of computer processing power. Graphics processing units (GPUs) or newer tensor processing units (TPUs) are generally faster than computational processing units (CPUs) at matrix multiplication. PyTorch is able to perform accelerated computations on these devices out of the box.
Accelerated computing means you can perform machine learning experiments faster, figure out what doesn’t work faster, and in turn figure out what works faster.
PyTorch originated at Facebook due to the need to process the large amounts of data the company obtained and help target its advertising platform.
Then in 2017, they open sourced PyTorch so that others could benefit from its features. It also means other talented engineers outside of Facebook will start working on it. So the PyTorch ecosystem today has contributors from a variety of people and industries around the world.
Overall, PyTorch is a powerful and versatile library for deep learning that offers a great balance between flexibility and ease of use. Learning PyTorch can open up many opportunities for building and deploying state-of-the-art machine learning models.
So, how do I get started using PyTorch? PyTorch runs on almost any computing system you can think of (Linux, Windows, Mac). There are cheap GPU servers available for rent on GPUMart. All you need to do is install, learn and apply!