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PyTorch is an open-source deep learning framework that offers flexibility and speed, enabling developers and researchers to design, train, and deploy AI models with ease. It is backed by the Linux Foundation and supported by AWS, Meta Platforms, Microsoft Corp, and Nvidia. PyTorch has become the go-to framework for cutting-edge AI research and enterprise-scale applications. Its popularity stems from a Pythonic, intuitive design, dynamic computation graphs that make experimentation seamless, and strong ecosystem support.
What sets PyTorch apart from other popular neural network-based deep learning frameworks, such as TensorFlow, is that it uses static computation graphs. On the contrary, PyTorch uses dynamic computation graphs, allowing greater flexibility when building complex architectures. So, let’s dive deep into the nitty-gritty of PyTorch, understand its features, benefits, and major applications, where it enables faster experimentation and prototyping.
What is PyTorch?
PyTorch is an open-source deep learning framework that supports Python, C++, and Java. It is often used for building machine learning models for computer vision, Natural Language Processing (NLP), and other neural network tasks. It was developed by Facebook AI Research (now Meta), but since 2022, it has been under the stewardship of the PyTorch Foundation (part of the Linux Foundation).
Despite being a relatively young deep learning framework, PyTorch has become a developer favourite for its ease of use, dynamic computational graph, and efficient memory usage.
Understanding How PyTorch Works
Two core components of PyTorch are tensors and graphs. Let’s understand them:
Tensors
Tensors are an essential PyTorch data type that are similar to multidimensional arrays. They are used to store and manipulate a model’s inputs, outputs, and other parameters. In addition, tensors resemble NumPy’s ndarrays, but unlike them, tensors can run on GPUs to accelerate computing for large workloads.
Graphs
Graphs are data structures consisting of connected nodes (vertices) and edges that help the framework track computations and calculate gradients during training. With two processes: forward propagation, where the neural network carries the input and delivers a confidence score to the nodes in the next layer, until the output layer, where the ‘error’ of the score is calculated. Whereas, in the backpropagation process, gradients of the loss function are computed and sent backward to update parameters, PyTorch keeps a record of tensors and executed operations in a directed acyclic graph (DAG) consisting of Function objects.
In this DAG, the leaves are the input tensors, and the roots are the output tensors. Unlike static frameworks like TensorFlow, PyTorch builds its graph at runtime (i.e., dynamically as the code runs). This makes the framework more flexible, easier to debug, and an ideal choice for accelerating innovation while supporting enterprise-grade deployment.
PyTorch can use debugging tools of Python. Since PyTorch creates a computational graph at runtime, developers can use PyCharm, the IDE from Python, for debugging.
Why Do We Need PyTorch?
PyTorch works very well with Python, and uses its core concepts like classes, structures, and loops, and is therefore more intuitive to understand.
The PyTorch framework is seen as the future of deep learning. There are many deep learning frameworks available to developers today, with TensorFlow and PyTorch among the most preferred. PyTorch, however, offers more flexibility and computing power. For machine learning and AI developers, PyTorch is easier to learn and work with.
5 Key Features of PyTorch
Here are some capabilities that make PyTorch suitable for research, prototyping, and dynamic projects.
1. Easy to Learn
PyTorch follows a traditional programming structure, making it more accessible to developers and enthusiasts. It has been well documented, and the developer community is continuously improving the documentation and support. This makes it easy for programmers and non-programmers alike to learn.
2. Developer Productivity
It works seamlessly with Python, and with many powerful APIs, can be easily deployed on Windows or Linux. Most PyTorch tasks can be automated. Which means with just some basic programming skills, developers can easily boost their productivity.
3. Easy to Debug
PyTorch can use Python’s debugging tools. Since PyTorch creates a computational graph at runtime, developers can use PyCharm, the IDE for Python, for debugging.
4. Data Parallelism
PyTorch can assign computational tasks amongst multiple CPUs or GPUs. This is made possible by its data-parallelism feature, which wraps any module and enables parallel processing.
5. Useful Libraries
PyTorch is supported by a large community of developers and researchers who have built tools and libraries to expand PyTorch’s accessibility. This developer community actively contributes to developing computer vision, reinforcement learning, and NLP for research and production. GPyTorch, BoTorch, and AllenNLP are some of the libraries used with PyTorch. This provides access to a robust set of APIs that further extend the PyTorch framework.
Top Benefits of PyTorch
- Python-friendly: PyTorch was created with Python in mind (hence the prefix), unlike other deep learning frameworks that were ported to Python. PyTorch provides a hybrid front end that enables programmers to easily move most of the code from research to prototyping to production.
- Optimized for GPUs: PyTorch is optimized for GPUs to accelerate training cycles. PyTorch is supported by the largest cloud service providers, including AWS, which currently supports the latest version. AWS includes its Deep Learning AMI (Amazon Machine Image), which is optimized for GPUs. Microsoft also plans to support PyTorch on Azure, its cloud platform. PyTorch includes built-in data parallelism, enabling developers to leverage multiple GPUs on leading cloud platforms.
- Plethora of tools and libraries: PyTorch comes with a rich ecosystem of tools and libraries that extend its capabilities and availability. For instance, Torchvision, PyTorch’s built-in set of tools, allows developers to work on large and complex image datasets.
- Large Network & Community Support: The PyTorch community, comprising researchers across academia and industry, programmers, and ML developers, has created a rich ecosystem of tools, models, and libraries to extend PyTorch. The objective of this community is to support programmers, engineers, and data scientists to further the application of deep learning with PyTorch.
Production & Deployment Challenges with PyTorch
- Scaling Models Efficiently: Sometimes, scaling applications at production requires optimization to support a large user base.
- System Integration: PyTorch models don’t usually integrate with the overall enterprise workflow, so APIs or conversion tools are frequently required.
- Performance Optimization: To reduce latency and boost throughput in real-time applications requires tuning and, in some instances, specialized hardware.
- Lifecycle Management: Model drift may occur, so PyTorch models need monitoring, retraining, and version control to ensure reliability.
5 Ways in Which AI Apps Can Use PyTorch
With PyTorch, engineering teams can build deep learning predictive algorithms from datasets. For instance, developers can leverage historical housing data to predict future housing prices or use a manufacturing unit’s past production data to predict the success rates of new parts. Other common uses of PyTorch include:
Image Classification
PyTorch can be used to build complex neural network architectures, such as Convolutional Neural Networks (CNNs). These multilayer CNNs are fed thousands of images of a specific object, say, a tree, and, much like how our brains work, once trained on a data set of tree images, they can identify a new picture of a tree they have never seen before. This application can be particularly useful in healthcare for detecting illnesses or spotting patterns much faster than the human eye can. Recently, a CNN was used in a study to detect skin cancer.
Handwriting Recognition
PyTorch makes it easier to build systems that can recognize human writing across people and regions. These systems can even account for inconsistencies in human handwriting across people and languages by leveraging flexible tools for image processing and neural networks. Developers can train these models to learn and understand the shapes and strokes of handwritten characters, enabling apps to convert notes or forms into digital text automatically. It is a useful capability for digitized entries, smart pens, or educational tools.
Forecast Time Series
Another type of neural network is a Recurrent Neural Network (RNN). They are designed for sequence modelling and are particularly useful for training an algorithm on past data. It can make predictions based on historical data, allowing it to make decisions based on the past. For instance, an airline operator can forecast the number of passengers it will have in 3 months, based on data from previous months.
Text Generation
RNNs and PyTorch are also used to generate human-like text, from simple word structures to advanced chatbot responses. In text generation, by training RNNS and AI models on large datasets, developers can build AI applications that produce content, answer questions, or provide customized communication. For instance, AI assistants such as Siri, Google Assistant, and other AI-powered chatbots rely on similar systems to enhance and personalize user interaction.
Style Transfer
One of the most exciting and popular applications of PyTorch. It uses a set of deep learning algorithms to manipulate images and transfer their visual styles onto other images, creating new images that combine the data of one with the style of another. For example, you can use your vacation album images, apply a style-transfer app, and make them look like paintings by a famous artist. And as you would expect, it can do the reverse as well. Convert paintings into contemporary photos.
Closing Statement
Undoubtedly, PyTorch has made deep learning more accessible than ever before. Since its architecture is uniquely suited to support both rapid research experimentation and the scalability required for production development, it’s becoming a trusted framework in the research and development industry. Therefore, its growing influence reflects how advances built on PyTorch are moving beyond research to shape enterprise platforms.
Salesforce, the world’s leading CRM platform, is one such example. The CRM platform embeds trusted AI across all its product offerings, from predictive analytics and SMS apps to Voice Agents that enhance customer engagement. As a Gold Salesforce Partner, Girikon helps organizations leverage these advances in Salesforce CRM and bring AI innovation and tangible business outcomes.
Get in touch today to know how we can help enterprises embed trusted AI into CRM workflows and transform how you interact with customers.
About Author
Indranil is a technology enthusiast with over 25 years of experience in project management, operations, technology and business development. Indranil has led project teams in egovernance, business process re-engineering, product development and worked with Government and Corporate customers. Indranil truly believes in the power of technology to drive productivity and growth for teams and businesses.
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