What is PyTorch?

In this tutorial, we will learn an introduction to PyTorch, what it is, its need, history and features. Let’s start!!!

What is PyTorch?

PyTorch is a deep learning framework that enables us to make very complex models with ease. Owing to its simplicity, it is beginner-friendly and has also proved its mettle in complex industrial applications.

Before the PyTorch era, Deep Learning was dominated mostly by feature engineering and rigid representations of models. A rigid model is one in which its representation has to be handcrafted for every other task. The advent of PyTorch allowed researchers to design dynamic representations of a model conveniently, i.e the process of representing a model became automated which also improved their performance.

Why PyTorch?

PyTorch has a data structure called “tensor” which are basically multidimensional arrays. Tensors keep a track on the operations that are performed on them. They can also compute the derivative of any output with respect to any input analytically. This comes in handy in many scientific operations making it valuable to a wide spectrum of scientific domains.

PyTorch is very easy to use just like Python. The commands are straightforward and not too complicated and we don’t have to spend any time learning the syntax. We can easily deploy and implement the code written in PyTorch at scale.

History of PyTorch

PyTorch can be thought of as a descendent of Torch, coded in Lua, a programming language that was not as popular as other languages. There was rigidity in the networks and models that were built which became a hurdle for the researchers as the applications of deep learning expanded rapidly and now they wanted flexibility. All these factors culminated and thus was born PyTorch at Facebook’s AI research lab in 2016.

Features of PyTorch

a. ONNX Support:

ONNX stands for Open Neural Network Exchange. It is an intermediary framework that is useful for interconversion between different Machine Learning frameworks. For example, if you have built a model in PyTorch and you want to convert it to Tensorflow framework you can do this by exporting and deploying your model using ONNX.

b. C++ Frontend:

The python API sits atop C++ code which provides various functionalities like tensor and automatic differentiation and it also decreases the execution time.

c. Cloud Support:

Cloud platforms such as Google Cloud Platform, Amazon Web Services provide powerful hardware and infrastructure which we can use to train and deploy our deep learning models.

d. Robust Ecosystem:

Many researchers and programmers have built models which can be used by others making development easier as some common models are already available for us to use and we do not have to build it from scratch.

e. Torchserve:

Developed by Amazon Web Services, torchserve helps developers deploy their models for serving predictions. We can load multiple models and run them parallely and we can also load two different versions of a model simultaneously to compare their functionality.

f. Distributed Data-Parallel(DDP):

If we have a model that is small enough to fit in one GPU, we can train it on very large datasets. We can copy the model on multiple GPUs and run them in parallel. Each GPU trains on a subset of the dataset and then computes the gradients and synchronizes them.

Advantages of PyTorch

1. PyTorch is easy to use as it uses Python as it’s development language and is therefore simple to implement. Also, it has a very easy to understand syntax making it Pythonic.

2. PyTorch uses dynamic computational graphs making it very flexible and robust.

3. There are many models which are already trained and available for us to use on the cloud servers reducing our work and increasing efficiency.

4. It is easy to debug as we can use Python’s debugging tools.

Prerequisites for Learning PyTorch

To learn PyTorch, one must be comfortable in any programming language preferably Python. A basic understanding of neural networks is good.

Applications of PyTorch

It is highly probable that you reached this page using some virtual assistant like SIRI, ALEXA, GOOGLE ASSISTANT etc. These beings owe their existence to deep learning. The automotive industry is working on self-driving cars and they are also employing deep learning to fulfil these tasks.

Deep learning is also useful in computer vision and facial recognition. It would not be an overstatement to say that for the future of our imagination Deep Learning could prove to be the road. Easy implementation of Deep Learning can enhance our productivity and take us further towards the future and PyTorch does just that.

Summary

PyTorch is easy to learn and makes Deep Learning flexible with it’s easy to use libraries and dynamic models. It also speeds up the implementation with the help of GPUs. Training a model becomes easier when multiple GPUs are there to train the model on a very large dataset which would take a lot of time if done without using PyTorch. It also has the added advantage of a very active community making our job easier as many models are already available.

PyTorch is growing at a rapid rate and many organizations are switching to it for constructing their deep learning models. With the help of GPUs it has increased the training speed thereby speeding up the development process. Developers are adding more functionalities and libraries to it with every passing moment and it would not be wrong to say that it is a big leap in technology.