Why PyTorch is a Highly Preferred Framework for Deep Learning Projects in 2022

PyTorch is an optimized open-source Machine Learning framework built using Python & Torch. This framework is extensively used for applications such as Natural Language Processing (NLP) and Modern computer vision. It is established by Facebook’s AI Research community and launched in 2016. It uses dynamic computation graphs and allows developers, scientists, and neural network debuggers to execute and test elements of the code in real-time.

Besides, programmers can develop a complex neural network effortlessly using PyTorch as it consists of core data structure, multi-dimensional array (Numpy arrays), and Tensor. PyTorch development is increasingly preferred by the research community and modern industries as it is a faster, flexible, easy-to-use Deep Learning tool to get the project up and running.

Five Basic PyTorch Components used for Deep Learning 

PyTorch is comparatively simple to learn for Data Scientists & programmers who are well-versed with Python. It offers simple APIs, components, easy debugging, and compatibility with a wide series of extensions developed using Python. Besides, PyTorch’s dynamic execution model is also ideal for prototyping. Some of the PyTorch components are:

• Tensors – It is a Multi-dimensional array containing elements of a single data type. Tensors consist of various modules such as torch.CharTen, torch.IntTensor, and torch.FloatTensor.
• Variable – This component is a package around tensors to maintain the gradient. The variable is available as a torch.autograd.Variable
• Functions – This helps perform the transform operations, and it does not have a memory to record any state. For example, log functions will provide output in terms of log value, and linear function is used to store biases and weight value
• Parameters – In essence, parameters are packages around the variable. It is utilized when developers need tensor as a parameter of a certain module which is impossible with a variable as it does not have a gradient. Parameters are available as a torch.nn.Parameter
• Modules – It is the base class for complete neural networks. A module can consist of parameters, modules, and functions. It can store values and states in an understandable way. A module can be implemented as a torch.nn.Module

Benefits of PyTorch for Deep Learning

• Easy to Learn

As mentioned before, PyTorch was developed with Python. Unlike other Deep Learning frameworks like TensorFlow and Keras, PyTorch offers a hybrid front end allowing you to impeccably share the majority of code during the research and prototyping phase. Also, this framework helps you to quickly implement a graph execution approach for production.

• Dynamic Computation Model

With computational graphs, the PyTorch framework allows the users to compute gradient values for the neural networks developed. Dynamic graphs are more scalable when compared to static graphs, where users can make interweaved valuation and construction of the graph. The dynamic computation is debug-friendly as it supports line-by-line code implementation. Besides, PyTorch Dynamic graphs help in identifying complications in code a lot easier than other frameworks like Sci-kit and TensorFlow.

• Affordable for GPUs supported by Azure and AWS 

PyTorch is highly-optimized to make use of GPUs for faster training times (i.e. time taken to estimate the relationship between your inputs and outputs). The leading cloud service providers are also involved with PyTorch development, as AWS currently supports the upgraded version of PyTorch.  This provides Machine Learning researchers and practitioners with experience in Deep Learning in the cloud with appropriate tools and infrastructure. It also has intrinsic data parallelism functionality, allowing you to leverage several GPUs on cloud sources.

• Data Parallelism

Operating with large datasets involves loading entire information into memory all at once. This leads to memory interruption problems, and programs run slowly. Moreover, it’s hard to retain data samples processing with code. To overcome these problems, PyTorch provides dual data primitives like Dataset and DataLoader for synchronizing data loading with programmed batching and enhanced modularity and readability of codes. DataLoader and Datasets allow PyTorch users to exploit their own data and pre-loaded indices. While Dataset stores the respective samples and labels, DataLoader merges sampler and Dataset and executes an iterable so users can seamlessly access samples.

• Rich ecosystem of libraries and tools 

PyTorch also includes several libraries and tools, such as forte, Flair, Torchvision, Torchmetrics, DeepSpeed, etc. PyTorch’s in-built tools are ideal for working with multifaceted image datasets. The PyTorch infrastructure includes tools, projects, libraries, and models from a broad community of researchers in industry, academic circles, ML engineers, and application developers. The objective of this infrastructure is to support data scientists and developers in the assessment and application of Deep Learning with PyTorch.

Wrapping Up

Overall, PyTorch is a crucial Deep Learning framework and an exceptional framework to develop applications. As more and more developers and researchers have started learning and implementing PyTorch, the scope of PyTorch is becoming high. If you’re interested in developing Deep Learning and computer vision projects, team up with the best PyTorch development consulting company to make a transition with Machine Learning.