PyTorch is a Python machine learning package that is based on Torch. It is considered as a framework of Deep Learning. It is used in image processing, natural language processing, and other deep learning concepts. It is built alongside Uber’s “Pyro” software to support the idea of in-built probabilistic programming.

PyTorch is a machine learning library and is open-sourced. It was developed as the Python wrapper based on the Torch framework. It is a redesigned framework that implements Torch in Python while running the same core C libraries for the backend code. You can find two PyTorch Variants.  

This backend code helps the developers run Python efficiently. They also built it to support the GPU hardware acceleration and the extensible features that helped develop Lua-based Torch.

The significant features of PyTorch include:

• Easy and understandable Interface

PyTorch provides easy to use API. It is easy to run on Python as it is straightforward to operate and run. This framework allows for more effortless code execution.

• Python Supportive

PyTorch framework easily integrates with the Python data science pack. Hence, it is widely utilized for the better usage of the Python environment functionalities and services.

• Supports Dynamic Computational Graphs

PyTorch is an excellent platform for Dynamic Computational Graphs. This allows you to change them during the runtime as per the requirements as a user. It is highly advantageous for developers who sometimes have no idea about the memory requirements while creating a neural network model.

PyTorch is known for its three levels of abstraction:

• Tensor

It runs on GPU and is an imperative n-dimensional array.

• Variable

 It helps store the data and gradient. It is a Node in a computational graph.

• Module

It is a Neural network layer that allows you to store state or learnable weights.

PyTorch is easy to understand and debug the code. It involves lots of loss functions and has many layers like Torch. It is also considered as the Numpy extension to GPUs. It supports the building of networks that are dependent only on the computation.

PyTorch is closely related to the framework called Lua-based Torch that Facebook actively utilizes. It is a comparatively new technology and supports imperative and dynamic methods. It supports computational graphs and can be defined during the runtime. PyTorch supports deployment features for mobile and embedded frameworks.

PyTorch is highly known for its features like:

• It makes use of GPU for tensor computing with solid acceleration.
• It provides the Deep Neural Network that is built on a tape-based auto diff system.

It is a Python library. It provides high flexibility and speed during the building and implementation of deep neural networks. Thus, it is easy to understand, install, and run. PyTorch is integrated with Python and is highly Pythonic because it allows you to build neural network models faster.

PyTorch is used explicitly by data scientists who wish to build various Data Science models. It is used for its flexible library that can be modified as per your requirements and changes. PyTorch provides you with a simple interface, hybrid frontend, distributed training, C++ frontend, Python-First, Native ONNX support, extensive tools and libraries, and cloud partners.  

PyTorch supports a new hybrid frontend that is flexible and easy to use in eager mode. It also involves a C++ environment for transition to graph mode for better functionality, speed, and optimization. 

PyTorch is preferred by data scientists as it allows them to train their neural network models in a distributed manner. It is known for its optimized performance in both production and research with the help of asynchronous execution of the collective operation from C++ and Python and peer-to-peer communication.

Learn more concepts of PyTorch with the help of Great Learning Academy’s free PyTorch courses. Enroll in these PyTorch courses and get hold of the free PyTorch certificates.