Definition of PyTorch
PyTorch is an open-source library used in machine learning library developed using Torch library for python program. It is developed by Facebook’s AI Research lab and released in January 2016 as a free and open-source library mainly used in computer vision, deep learning, and natural language processing applications. Programmer can build a complex neural network with ease using PyTorch as it has a core data structure, Tensor, multi-dimensional array like Numpy arrays. PyTorch use is increasing in current industries and in the research community as it is flexible, faster, easy to get the project up and running, due to which PyTorch is one of the top deep learning tools.
Why do we need PyTorch?
The pyTorch framework can be seen as the future of the deep learning framework. There are many deep learning frameworks getting introduced, and the most preferred frameworks are Tensorflow and PyTorch, but among all, PyTorch is emerging as a winner due to its flexibility and computation power. For machine learning and Artificial Intelligence enthusiast, PyTorch is easy to learn and will be very useful to build models.
Here are some of the reasons why developers and researchers learn PyTorch:
1. Easy to Learn
PyTorch has the same structure as traditional programming, and it has been brilliantly documented with the developer community continuously working to improve. Due to this, it is easy to learn for the programmer and non-programmer.
2. Developers Productivity
It has an interface with python and with different powerful APIs and can be implemented in Windows or Linux OS. With some programming, knowledge developer can improve their productivity as most of the tasks from PyTorch can be Automated.
3. Easy to Debug
It can use debugging tools like pdb and ipdb tools of python. As PyTorch develops a computational graph at runtime programmer can use Pythons IDE PyCharm for debugging.
4.7 (11,529 ratings)
View Course
4. Data Parallelism
It can distribute the computational tasks among multiple CPUs or GPU. This is possible using the data parallelism (torch.nn.DataParallel) feature, which wraps any module and helps us do parallel processing.
5. Useful Libraries
It has a large community of developers and researchers who built tools and libraries to extend PyTorch. This community helps in developing computer vision, reinforcement learning, NLP for research and production purposes. some of the popular libraries are GPyTorch, BoTorch, and Allen NLP. The rich set of powerful APIs helps to extend the PyTorch framework.
PyTorch Components
Let’s look into the five major components of PyTorch:
1. Tensors: Tensors are the Multi-Dimensional array similar to the Numpy array, and Tensors are available in Torch as a torch. IntTensor, torch.FloatTensor, torch.CharTen etc.
2. Variable: A variable is basically a wrapper around tensors to hold the gradient. The variable is available under torch.autograd as a torch.autograd.Variable.
3. Parameters: Parameters are basically a wrapper around the variable. It is used when we want tensor as a parameter of some module which is not possible using a variable as it is not a parameter or Tensors it does not have a gradient, so we can use parameters under the torch.nn as a torch.nn.Parameter.
4. Functions: Functions performs the transform operations, and they do not have a memory to store any state. Like log functions will give output as log value, and linear layer can not function as it stores weight and biases value. Examples of functions are torch.log, torch.sum, etc., and functions are implemented under the torch.nn.functional.
5. Modules: Module used as under Torch as a torch.nn.Module is the base class for all neural networks. A module can contain other modules, parameters, and functions. It can store state and learnable weights. Module are types of transformation which can be implemented as torch.nn.Conv2d, torch.nn.Linear etc.
Advantages and disadvantages of PyTorch
Below are some advantages and disadvantages:
Advantages
- It is easy to learn and simpler to code.
- Rich set of powerful APIs to extend the Pytorch Libraries.
- It has computational graph support at runtime.
- It is flexible, faster, and provides optimizations.
- It has support for GPU and CPU.
- Easy to debug using Pythons IDE and debugging tools.
- It supports cloud platforms.
Disadvantages
- It has been released in 2016, so it’s new compared to others and has fewer users, and is not widely known.
- Absence of monitoring and visualization tools like a tensor board.
- The developer community is small compared to other frameworks.
Application of PyTorch
1. Computer Vision
It is used a convolution neural network to develop image classification, object detection, and generative application. Using PyTorch, a programmer can process images and videos to develop a highly accurate and precise computer vision model.
2. Natural Language Processing
It can be used to develop the language translator, language modeling, and to develop a chatbot. It uses RNN, LSTM, etc. Architecture to develop natural language, processing models.
3. Reinforcement Learning
It is used to develop Robotics for automation, Business strategy planning or robot motion control, etc. It uses Deep Q learning architecture to build a model.
Conclusion
It is one of the primary frameworks used for deep learning, Natural Language processing, etc. In the future there will be more and more researchers and developers will be learning and implementing PyTorch. It has a similar syntax as any other standard programming language; hence it is to learn and to make a transition in AI or Machine learning engineer.
Recommended Articles
This is a guide to What is PyTorch?. Here we discuss the Why do we need PyTorch and its Components along with Applications, Advantages, Disadvantages. You can also go through our other suggested articles to learn more –
