PyTorch is a defined framework also called as Python-based scientific computing package which uses the power of graphics processing units. It is considered as one of the best deep learning research platforms built to provide maximum flexibility and speed and develop the output as the way it is required. It is known for providing two of the most high-level features which are mentioned below:
1. Tensor computations with strong GPU acceleration support
2. Building and evaluating deep neural networks on a tape-based autograd systems.
There are many existing Python libraries which include the potential to change how deep learning and artificial intelligence are performed and one of them is PyTorch framework. One of the key reasons behind PyTorch’s success is it is completely based on Python and user can build neural network models effortlessly with much ease. It is still a young player in comparison with other frameworks, however, it is gaining momentum fast.
History of PyTorch:
Since its first release in January 2016, many researchers have continued to adopt PyTorch in a faster rate. It has quickly become the mandatory go-to library because of its ease in building extremely complex neural networks. It is giving a tough competition to the existing frameworks such as TensorFlow especially when used for research work. However, it is still considered by the masses as it is in testing period due to its still “new” and “under construction” tags.
PyTorch creators kept a vision that this library to be highly imperative which can allow them to run all the numerical computations quickly. It follows ideal methodology which fits perfectly with the Python programming style. PyTorch has allowed deep learning scientists, machine learning developers, and neural network debuggers to run and test part of the code in real time. Thus, users do not have to wait for the entire code to be executed to check whether it works or not.
The user can use any of the Python favourite packages such as NumPy, SciPy, and Cython to extend PyTorch functionalities and services when required. Now, the question might arise that why do you need PyTorch framework?
The answer is quite simple, PyTorch is a dynamic library ,very flexible and can be used as per your requirements and changes which is currently adopted by many of the researchers, students, and artificial intelligence developers.
Some of the key highlights or features of PyTorch Framework includes:
Easy Interface: It offers easy to use API, thus it is very simple to operate and run like Python. The code execution is quite simple.
Usage of Python: This library, being Pythonic, smoothly integrates with the Python data science stack. Thus, it can leverage all the services and functionalities offered by the Python environment.
Implementing Computational graphs: In addition to this, PyTorch provides an excellent platform which offers dynamic computational graphs, thus a user change them during runtime. This is highly useful when a developer has no idea how much memory will be required for creating a neural network model.
PyTorch Community
PyTorch community is growing in geometric proportion. It is observed that great amount of developments that have led to its citations in many research papers and groups. More and more people are bringing PyTorch within their artificial intelligence research labs for the provision of quality driven deep learning models.
The interesting fact is that, PyTorch is still in beta release, but the way developers especially with developers is adopting this deep learning framework at a faster rate shows its real potential and power in the community. Even though it is in the beta release, the number of contributors on the official GitHub repository is 743 who are working on enhancing and providing improvements to the existing PyTorch functionalities.
PyTorch does not limit to specific applications because of its flexibility and modular design. It has seen with heavy use by leading tech giants such as Facebook, Twitter, NVIDIA, Uber and more in multiple research domains such as NLP (Natural Language Processing), machine translation, image recognition, neural networks, and other key aspects.
Why use PyTorch in research?
Anyone who is working in the field of deep learning and artificial intelligence specially when it comes with data scientists has likely worked with TensorFlow before, Google’s most popular open source library. However, the latest deep learning framework – PyTorch solves major problems in terms of research work. PyTorch is TensorFlow’s biggest competitor to date, and it is currently a much favored deep learning and artificial intelligence library in the research community.
Why PyTorch?
A design driver for PyTorch is expressivity, that is allowing a developer to implement complicated models without extra complexities imposed by the framework. When a new paper comes out and a practitioner sets out to implement it, the most desirable thing for a tool is for it to stay out of the way. The less overhead there is in the process, the quickest and most successful will be the implementation and the experimentation that will eventually follow. PyTorch arguably offers one of the most seamless translations of ideas into Python code available in the deep learning landscape, and it does so without sacrificing performance. While featuring an expressive and user-friendly high-level layer, PyTorch is not a high-level wrapper on top of a lower-level library, so it does not require the beginner to learn another tool, like Theano or TensorFlow, when models become complicated. Even in the case new low-level kernels need to be introduced, say convolutions on hexagonal lattices, PyTorch offers a low-overhead pathway to achieve that goal.
From an ecosystem perspective, PyTorch embraces Python, the emergent programming language for data science. PyTorch compensates the impact of the Python interpreter on performance through an advanced execution engine, but it does so in a way that is fully transparent to the user, both during development and during debugging. PyTorch also features a seamless interoperation with NumPy. On the CPU, NumPy arrays and Torch tensors can even share the same underlying memory and be converted back and forth at no cost.
Conclusion:
Deep learning is about automatically learning representations from examples using deep neural networks Neural networks consist in a composition of simple operations Neural networks learn through weight updates by back-propagation of errors Libraries like PyTorch allow to build and train neural networks efficiently, moving computations to the GPU and automatically computing derivatives for back-propagating errors PyTorch focuses of minimizing cognitive overhead, while focusing on flexibility and speed.
