What is PyTorch used for (practical use cases)

August 4, 2022

PyTorch Definition

PyTorch has been developed by the Torch Python program and it is an open-source library that gets utilized in machine learning libraries. It was released in January 2016. 

This open-source library is mostly utilized in natural language processing applications, computer vision, and deep learning. By using PyTorch as the core data structure, developers can easily construct intricate neural networks like Tensor and various multi-dimensional arrays such as Numpy arrays.

Currently, in the computer science and information technology sector the usage of PyTorch is rapidly increasing. The research community is also hyped up about this open-source library because of its efficiency, flexibility, ease of access, and ability to implement and run a project in no time. This is also the reason why PyTorch is considered one of the best deep learning tools.

Why do we need to learn PyTorch?

Right now, the PyTorch framework should be regarded as the successor of the deep learning framework. Although there are multiple deep learning frameworks available, the most preferred ones are PyTorch and Tensorflow. 

Due to its computing capacity and resilience PyTorch is rapidly emerging as the absolute winner among all the other deep learning frameworks. For anyone who has a knack for artificial intelligence and machine learning, PyTorch is very easy to learn and you can use it to construct various models.

Here is a list of reasons why researchers and developers learn PyTorch:

Extremely easy to learn

The structure of PyTorch is very similar to that of traditional programming and the developer community has been continuously working to improve it further. Moreover, PyTorch has also been effectively documented so that everyone can easily access it. Both programmers and non-programmers can easily learn PyTorch.

Productivity of the developer

PyTorch has multiple different APIs and an interface of Python. Moreover, it can also be implemented in Linux OS and Windows. Most of the tasks of PyTorch can easily be automated and that is why programmers can improve their knowledge in this field and increase their productivity at the same time.

Very easy to debug

PyTorch can easily support the debugging tools of Python such as ipdb and pdb.  During its runtime, PyTorch creates a computational graph and that is why developers can easily use PyCharm, the IDE of Pythons for the debugging process.

Parallelism of Data

PyTorch can easily allocate different computational tasks among various GPU and CPUs. PyTorch has a data parallelism feature that can easily wrap any module in parallel processing.

Handy libraries

PyTorch is very popular among developers and programmers and this community has built different tools and libraries to enlarge PyTorch. The entire programming community assists in developing NLP for research and production, reinforcement learning, computer vision, etc. The rich and powerful APIs also help in developing PyTorch Framework. Some of the most popular PyTorch libraries are BoTorch, Allen NLP, and GPyTorch.

Components of PyTorch

Let us find out more about the five different components of PyTorch:

  • Tensors: Tensors are very homogeneous to the Numpy array and it is also multi-dimensional. The Tensors are accessible in PyTorch as a torch. Some examples are torch.CharTen, IntTesnor, torch.FloatTensor, etc.
  • Variable: A variable works as a wrapper around the Tensor to clutch the gradient. You can find variables under the torch.autograd in the form of a torch.autograd.Variable.
  • Parameters: The work of a Parameter is to wrap the variable and we use it when the Tensors of a module do not possess a gradient. We can find parameters under the torch.nn in the form of torch.nn.Parameter.
  • Functions: Functions do not possess any memory and their work is to transform the operations. Some examples of function are torch.sum, torch.log, etc. Functions are implemented using torch.nn.functional.
  • Modules: Modules are the base class of all neural networks and they also can contain different functions, modules, and parameters. It is efficient in storing learnable weights and states. Modules can be applied as torch.nn.Linear, torch.nn.Conv2d, etc.

Advantages and shortcomings of using PyTorch

Here is a list of benefits and shortcomings of using PyTorch:


  • PyTorch is very easy to learn and code for both programmers and non-programmers
  • It has a rich set of APIs that have successfully enlarged the PyTorch libraries.
  • It can support the computational graph during its runtime.
  • PyTorch can provide optimization, while it is quick and flexible.
  • PyTorch supports both the CPU and GPU.
  • You can easily use the debugging tools of Python, including Python IDE.
  • It also reinforces the cloud platforms. 

Shortcomings of PyTorch

  • PyTorch was released in 2016, therefore it is a comparatively new open-source library. As a result, it is not widely known among developers and it also has limited users.
  • PyTorch doesn’t have any visualization and monitoring tool similar to the Tensor board.
  • Compared to the other existing frameworks, the developer community is very small.

Fundamental applications of PyTorch

Computer vision

PyTorch can be utilized in the computer neural network to develop object detection, generative application, and image classification. Furthermore, with the help of this framework developers can process videos and images which will help them in constructing a highly precise and detailed computer vision model.

Natural language processing

You can utilize the PyTorch framework to modify the language modeling, develop a chatbot, and as a language translator. Moreover, it can also use LSTM and RNN architecture to embellish a natural language processing model.

Reinforcement learning

PyTorch can also be used for business development plans, motion control of robots, and robotic automation processes. It makes use of the Deep Q learning architecture to construct a model.

5 practical use cases of PyTorch in artificial intelligence 

With the help of PyTorch (in deep learning tasks) you and your team will be able to construct predictive algorithms from different data sets. For example, you can employ past real estate data to anticipate the future prices of real estate. Similarly, in the case of a production plant, you can utilize the plant’s production history and predict the future failure rate. Some of the common and practical use cases of PyTorch include:

Image categorization

With the help of PyTorch developers can create specific neural network architectures which are also known as CNN or Convolutional Neural Networks. These CNNs are multi-layered and are provided with various images of a specified object, for example, a cat. Similar to the work of a brain, the system will be able to effectively identify a new cat’s picture. Currently, the medical industry is utilizing this application of PyTorch to identify ailments such as skin cancer.

Recognizing handwriting

This particular application of PyTorch implicates the analysis of human handwriting and how the inconsistencies appear in every individual across various languages. The chief AI scientist of Meta, Yann LeCun, has become the harbinger of CNNs that can perceive handwritten numbers.

Forecasting time sequences

Here developers use a Recurrent Neural Network or RNN which is a specific kind of neural algorithm that can provide effective information depending on the past data. For example, using RNNs an airline can anticipate the number of passengers they will receive for a particular month.

Generating texts

Another use case of PyTorch and RNNs is text generation which means you can train the AI model utilizing a specific text (for example, the literary work of Shakespeare), and in the end, it will construct its output according to the things the AI model has learned during the training process.

Transfer of Style

Style Transfer can be considered one of the funniest and most popular applications of PyTorch. In this case, the deep learning algorithm controls the images and videos to embrace the pictorial style of that video or image on another video or image. 

For example, you can use your favorite digital image and change it into art or drawing created by a famous artist or painter such as Van Gogh. Moreover, you can also do the reverse, meaning you can turn the painting into a digital image that looks too good to be true.

Companies using PyTorch

A report from market analytics agency HG Insights shows that companies like ADP, Apple, NVIDIA, PepsiCo, and Walmart have been using PyTorch for developing deep learning models to enhance predictive analytic studies. 

This large-scale adoption of the technology by major corporations has fueled the top three leading cloud providers – Amazon, Google, and Microsoft – to add cloud computing instances having a preinstalled PyTorch 1.1, thus popularizing it globally.


PyTorch is poised for marvelous milestones and targets in the deep learning space and with the pace with which it is performing now, the journey has only begun. Multiple ways this technology can be utilized in daily applications like creating prototype photo filters to adapt style transfer principles, identifying fake goods assisted by image classification, and so on. PyTorch is at present the primary framework being used by corporations for Natural Language Processing, deep learning, etc.

Having a similar syntax to other standard programming languages, PyTorch makes it easier for AI or Machine learning engineers to learn or transition from other alternatives. Going by the recent trends, abundant research and development activity is reserved for PyTorch implementation projects. Thus, adding PyTorch to your technical skills currently is the right decision that you can make with PyTorch Bootcamps and deep learning courses.


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