Ultimate PyTorch Guide: Features, Pros & Cons

PyTorch is a Python-based machine learning framework that provides developers and researchers with a versatile toolkit for building and training models. It is widely used in fields like natural language processing (NLP), computer vision, and other AI-driven applications.

Originally derived from Torch (a Lua-based ML library), PyTorch was redesigned to align with Python’s intuitive syntax and philosophy. Many consider it one of the most “Pythonic” ML frameworks due to its flexibility and ease of use.

As an open-source project, PyTorch is freely available and primarily maintained by Meta’s AI research team*. Over time, it has grown into a robust ecosystem supported by specialized libraries, making it a comprehensive solution for deep learning challenges.

Who Uses PyTorch and Why?

PyTorch is favored by ML practitioners and researchers who work with Python—the dominant language in AI/ML development. Its focus on deep learning sets it apart, enabling the creation of multi-layered, trainable neural networks. Unlike traditional ML models, deep learning systems can self-optimize by processing raw data, allowing them to tackle more complex and diverse problems.

Key industries leveraging PyTorch include:

• Pattern recognition in images;
• Computer vision, detection of moving objects;
• Sarch for patterns, data analysis, including unstructured data;
• Natural language processing, speech recognition and machine translation;
• Creation of machine descriptions for images;
• Text analysis and information search in them;
• Generation of text content and images.

All of the listed tasks are relevant and needed in many industries, from analytics to creativity.

How PyTorch is different from other ML frameworks

There are many machine learning solutions, and they differ in their approach. PyTorch has two important features that make it stand out from the crowd.

Dynamic calculations. PyTorch dynamically recalculates the computation graph “on the fly”, and this is convenient. Let’s explain what this means.

Understanding Neural Networks as Computational Graphs

At their core, neural networks are mathematical graphs that mirror aspects of biological nervous systems. These computational structures consist of:

1. Nodes (Neurons): Function as processing units that apply mathematical operations to incoming data. Each node contains activation functions that transform inputs non-linearly.

2. Edges (Synapses): Carry weighted connections between nodes, where the weights represent the “strength” or importance of each connection. During training, these weights are continuously adjusted through backpropagation.

The graph structure enables information to flow through the network in both forward passes (for predictions) and backward passes (for learning). What makes this architecture powerful is its dynamic nature – at each iteration during training, the weights are recalculated through optimization algorithms like gradient descent, allowing the network to progressively improve its performance.

This graphical representation isn’t just theoretical – frameworks like PyTorch and TensorFlow actually implement these models as directed acyclic graphs (DAGs) under the hood, enabling efficient computation and automatic differentiation.

In most available solutions, the computation graph can only be changed before compilation. When the program is compiled, the developer can only run a pass through the graph in the forward or backward direction. If the task is complex, this approach seriously complicates debugging.

PyTorch has a different approach: the graph does not exist as a fixed structure, but is dynamically built directly during calculations. With each pass, the graph is rebuilt according to new conditions. As a result, the developer has fewer restrictions, and he can implement more complex and non-standard solutions.

Automatic differentiation:
Differentiation is the process of finding the derivative of a function. This is a mathematical operation that is important for machine learning — differentiation is used to recalculate weights. This is how the backpropagation algorithm works, which is key to training neural networks.

Finding the derivative is a fairly complex task for a computer. Sometimes ML specialists even solve it manually, calculating the derivative on paper. Another way is to use approximations, but there is a high risk of losing accuracy due to rounding. There are also languages that search for the derivative according to rules, but instead of an exact result, they can get cumbersome and uninformative expressions.

Automatic differentiation solves this problem. The essence of the solution is to define a large and complex function through small, “base” ones. If the values of the base functions at some point are known, then you can automatically calculate both the value and the derivative of a complex function based on them.

PyTorch supports automatic differentiation. Together with dynamic calculations, it turns out that the framework builds a graph and calculates weights “on the fly”, and a person only needs to write a couple of lines of code. This is convenient and allows you to implement more complex projects.

CUDA: A technology that allows you to run calculations on both the processor and the video card. A feature of deep learning is its serious resource intensity: models take up a lot of computer resources during training and operation. Therefore, they began to use video card GPUs to run them – they are powerful and suitable for complex calculations.

Usually, all calculations can be run either on the central processor or on the video card. Some frameworks have two versions – for CPU and GPU. And moving calculations from one “platform” to another is difficult and inconvenient. The solutions are quite cumbersome.

Not so in PyTorch. Thanks to the support of CUDA technology, entities can easily be moved to the video card or created directly on it. With one line, you can transfer calculations to the video card, calculate something complex, and then return them back to the processor. All this is available directly from the interpreter.

However, there is a limitation. CUDA only works with NVIDIA video cards, and not with all of them. So not every computer configuration is suitable for its use.

What’s in PyTorch

The structure of PyTorch and its capabilities are reminiscent of another Python framework, NumPy, designed for complex calculations. But PyTorch has its own special features: it works dynamically, can use the power of the GPU, and makes the task easier for a specialist.

Here are the capabilities of PyTorch. This is not an exhaustive list – just a few examples.

Tensors: Tensors are multidimensional arrays – data structures that most closely resemble mathematical matrices. They are used to build models for machine learning, store and process the information that the model works with.

Tensors in PyTorch are similar to those in NumPy. They can be created from Python arrays or generated using a special function. The values inside the matrix can be integer, signed, unsigned, and fractional with a floating point. There is no automatic type conversion here, unlike NumPy, so you don’t have to worry about the program automatically assigning values a type that takes up too much memory.

Two features of tensors in PyTorch:

• They are optimized for automatic differentiation, i.e. finding the derivative “at each step”.
• They can be run both on the main processor power and on the video card, moved from a regular processor to a graphics one and back.

Datasets: A dataset is a data set for machine learning. There are usually two types of datasets: for training and for testing how the trained model works. Most ML frameworks, including PyTorch, have built-in datasets. These are sets of popular and well-known data packages in the community.

Such datasets are unlikely to be suitable for solving serious problems, but they are sometimes needed for prototyping or comparative analysis of models. It is faster and more convenient to load test datasets into the compared models and look at the results than to create and load real ones. In addition, real datasets are often larger, take longer to process, and put a greater load on the capacities.

Data Loader: Data Loader is a module that allows you to create mini-packages within one dataset and load them into the model. With its help, you can mix data, determine the size of the mini-package, and perform other actions.

PyTorch Modules

PyTorch has several built-in modules for solving various specific problems. There are three main ones, and here’s what they are for.

Autograd: This module is directly designed for automatic differentiation. Derivatives are calculated during the forward traversal of the graph. Then the backward traversal is launched, during which gradients are calculated – in neural networks, this is the name for the vectors that show the direction and steepness of the derivatives. It is the gradients that are used in the backpropagation algorithm. With the help of the Autograd module, derivatives and gradients are calculated automatically.

Optim: This module contains optimization tools for neural networks. It includes implementations of all the most commonly used methods, so there is no need to write them from scratch. This way, the model can work more efficiently without unnecessary labor costs on the part of a specialist.

nn: This module includes higher-level abstractions than in Autograd – that is, they are more distant from the hardware and calculations, but closer to human understanding. This is necessary for building complex neural networks that can be difficult to understand and describe using low-level tools.

PyTorch Ecosystem Programs

A whole ecosystem has developed around the framework, consisting of additional and auxiliary libraries, routines, and other solutions. The components of this ecosystem are usually needed only by advanced PyTorch users who work with the framework professionally. But knowing about their existence is also useful at the initial levels of study.

Creating and running projects: Classy Vision and PyTorch Lightning are frameworks that simplify and expand working with PyTorch. They are implemented at a higher level of abstraction and provide an environment for running projects on PyTorch.

It is easier to work with these frameworks. They implement useful functions, utilities, and “syntactic sugar” – innovations that simplify the syntax of commands. As a result, the same actions can be performed easier and faster, in a couple of lines of code.

Deployment: Specialists may face the task of deploying PyTorch programs in production — on servers accessible to end users. This is usually necessary when the neural network itself is a commercial product, for example, a “smart” photo editor.

PyTorch does not have its own module for deployment, so TorchScript, ONNX, and Caffe2 tools are used for it. Caffe2 was originally a separate machine learning framework, but then merged with PyTorch.

Repositories: There are several repositories on GitHub with PyTorch libraries and extensions for specific tasks. For example, Transformers is a solution for natural language recognition: text, sounds, reading from pictures. Or Detectron2 is a computer vision library that can recognize moving objects, segment them, and highlight key points.

Other tools: The PyTorch ecosystem has dozens of libraries that help you interpret results faster and more conveniently, visualize and track them, create datasets for training, and do much more. Here are a few examples.

• Weights and Biases is a scientific tool for tracking the results of experiments.
• Clear ML is also an experiment manager and version control tool.
• Tensorboard is a set of visualization tools that was originally created for TensorFlow, but was later integrated into PyTorch.
• Torchgeo is a set of datasets, models, and tools for programs working with geospatial information.
• MONAI is a solution for optimizing PyTorch for medical tasks.

These are just a few examples. In reality, there are many more additional tools for PyTorch: they are available for almost any task.

Advantages of PyTorch

Flexibility: The framework allows you to create even complex models and solve various problems. This is possible due to direct access to mathematical functions and the ability to write your own. There are solutions that do not allow you to build something non-standard – PyTorch is not one of them. On the contrary: it is well suited for complex and non-standard models. Additional advantages are CUDA support and optimization tools.

Easy to make changes: Thanks to the dynamic construction of the computation graph, PyTorch is free from the disadvantages that some alternative options have. You can make changes to the graph “on the fly”, this simplifies debugging and rewriting the neural network if necessary.

Easy to adapt with NumPy: Some functions and capabilities of PyTorch resemble those of NumPy, although PyTorch is more powerful and functional. Nevertheless, NumPy projects can be easily converted to the PyTorch format – and vice versa. Tensors and other entities in these two tools are similar in structure: it is enough to change them a little for a full conversion. A special function for converting tensors from NumPy to PyTorch format and back allows you to easily adapt the code to each other.

Detailed documentation: One of the advantages of PyTorch for beginners is detailed, extensive and clear documentation, which describes all the important points. Everything you need is documented: if problems arise, you can always refer to the current documents. However, in full, they only exist in English, and are not translated into Russian.

Large community: Previously, PyTorch was less popular than some other solutions, but recently it has caught up with TensorFlow in terms of frequency of mentions. The framework has a large ecosystem, a wide and active community: this is both help for beginners and the constant emergence of new convenient solutions for development.

Disadvantages of PyTorch

Lack of visualization tools: PyTorch has no built-in tools for plotting graphs, charts, and other visualization methods. You have to use third-party solutions, such as Tensorboard, although it was not originally intended for PyTorch.

Lack of deployment tools: The same applies to deploying a PyTorch program on production servers – this is how end users get access to the product. PyTorch has no built-in tools for easy launch on servers, so you have to use third-party standards, protocols, and tools.

Need for additional libraries: Due to all of the above, when working professionally with PyTorch, you will need to use additional libraries. This means that the developer must understand not one tool, but several in order to fully complete tasks. But this drawback becomes relevant only with professional use – beginners usually have enough of the capabilities of “pure” PyTorch.

How to install PyTorch

You can install PyTorch on your local system using pip, the package manager included in Python. It allows you to install an extension, library, or framework for the language using one or two commands in the console. In addition to pip, you can use the Anaconda software package for computing in Python, which also has a command for installing add-ons.

The second method, suitable for owners of weak computers, is to use a service for machine learning or research, such as Google Colab. Such platforms provide computing power and environments with pre-installed tools for ML. It is enough to create a program and write a command at the top to import the desired package – in our case, this is PyTorch and its various modules.

How to start working with PyTorch

If the framework is installed, it is enough to launch the development environment and connect the necessary packages in the first lines of the program. In Python, this is done using the import command. After that, PyTorch modules and commands will be available, and you can write code with them.

It is worth studying PyTorch after getting acquainted with Python: it is important to understand the language and its features. You need to start with simple tasks – creating tensors, working with simple neural networks. Over time, you can move on to more complex and interesting tasks.