PyTorch is one of the most popular deep learning Python libraries. In this article, you will learn about how to use PyTorch; what the differences are between PyTorch vs. TensorFlow, and PyTorch vs. Keras; the best PyTorch projects for beginners and also for social good.

Introduction

Many deep learning frameworks are being used by machine learning and AI enthusiasts. Among all, PyTorch is the emerging winner due to its computational power and flexibility. It is quite helpful to build models, and its use is increasing in the research community and current industries.

Let’s dive into what PyTorch is and what it is used for.

What is PyTorch?

PyTorch is an open-source deep learning tensor library developed using the Torch library for Python programs. It is mainly used for applications that use CPUs and GPUs. Compared to other deep learning frameworks like Keras and Tensorflow, PyTorch is optimized as it uses dynamic computation graphs. It allows developers, neural network debuggers, and scientists to run and test code portions in real-time. Therefore, users don’t have to wait for the whole code to be implemented to check if a certain piece of code works or not.

Below are the two main features of PyTorch.

• It provides automatic differentiation to create and train deep neural networks.
• Similar to NumPy, it has tensor computation with strong GPU (Graphical Processing Unit) acceleration support.

What is PyTorch Used For?

PyTorch is an optimized and easy-to-use platform compared to other Deep Learning platforms as it uses a simplified API. Below are some reasons to choose PyTorch over other DL platforms.

• Python Support – PyTorch is entirely Pythonic (based on Python) and is the most popular coding language among DL engineers and data scientists. The data analysts use PyTorch to integrate the Python data science stack smoothly.
• Increase in Productivity – PyTorch is an optimized framework that is simple to code. The developers can use it to automate the processes so they can make fewer errors and become more productive.
• Performance Improvement – PyTorch uses GPU accelerated libraries for delivering high-performance model training.

You can use PyTorch to do the following fantastic things.

Easier Debugging

PyTorch uses debugging tools such as pdb and ipdb of Python. The programmers can set the PyTorch environment variable to CUDA_LAUNCH_BLOCKING=1 before running the script. This way, PyTorch synchronously executes the CUDA code and reports errors on the fly.

Further, PyTorch develops a computational graph at runtime, making it easier to use other Python tools. Thus, the programmers can use PyCharm to debug their PyCharm code.

Transfer Computations Between CPU and GPU

PyTorch allows you to move computations between processing units using a simple command easily. For instance, you can use the command model.cup() to deliver data to CPU and model.cuda() to deliver data to GPU.

Build Deep Learning Applications

PyTorch allows you to build your own computational graphs and change them on the fly. You can use it to display any metric that is relevant to you. 

Further, you can use PyTorch to distribute the computational tasks among multiple GPU or CPUs. The data parallelism feature of PyTorch wraps the modules and helps us do parallel processing.

What is PyTorch Lightning?

PyTorch Lightning is an open-source Python wrapper built on top of PyTorch for machine learning researchers. It possesses additional features that allow users to deploy complex models. You can install it using the pip command. Following are some advantages of PyTorch Lightning. 

• The framework is clean, simple, and easy to reproduce.
• PyTorch Lightning supports 16-bit precision that helps to speed up model training.
• PyTorch integrates easily with machine learning tools. For instance, it supports Google’s Tensorboard.
• The models of PyTorch are hardware agnostic. You can run the code on any GPU, CPU, or TPU machine.

Some key features are given below:

• Making the code readable by separating the engineering code from the main code
• Scaling the machine learning and deep learning models to run on any hardware
• Computing metrics such as precision, accuracy, recall, etc. across multiple Graphical Processing Units

PyTorch vs. Tensorflow

We discussed some important features of PyTorch above. Let’s look into some features of Tensorflow now.

• Tensorflow is an open-source deep-learning library for differential programming and dataflow across various tasks.
• You can use the Tensorflow library to build and train machine learning models with easy debugging and quick model iteration.
• Tensorflow allows the users to train and deploy the models in the cloud, in the browser, on-device, or on-premises, irrespective of the language they use.
• Tensorflow has a straightforward and flexible architecture that helps robust research experimentation and allows faster publishing.

Now, let’s discuss the differences between PyTorch and Tensorflow.

• Facebook developed PyTorch using the Torch library, whereas Google developed Tensorflow.
• In Tensorflow, the user has to first define the computational graph and then run the machine learning model. It means Tensorflow takes a static graph approach. On the other hand, PyTorch works on a dynamic graph approach and allows the user to manipulate the graph on the go.
• Tensorflow offers a broad spectrum of options to work with and provides operations like:
  • Support for Fourier transforms.
  • Use of a package named contrib to create models
  • Checking the Tensor for NaN and infinity

PyTorch, on the other hand, supports fewer features compared to Tensorflow.

PyTorch vs. Keras

First, let’s look into what Keras is before discussing the differences between PyTorch and Keras.

Keras is a framework to interact with high-level APIs in machine learning backends like Tensorflow. It provides rapid experimentation and acts as an interface for the Tensorflow library.

Some essential features of Keras are given below.

• It is easy and simple to use.
• It has an active community and robust documentation.
• It includes pre-trained models that can be used for fast deployment.
• It provides superior debugging utilities as compared to Tensorflow.

Some significant differences between PyTorch and Keras are mentioned below.

• PyTorch allows the users to expose low-level operations and use Python to customize their applications. On the other hand, Keras enables the users to expose high-level operations and provides a more shallow experience.
• PyTorch works with large datasets and depicts high performance. On the other hand, Keras works with small datasets, and performance is low compared to PyTorch.
• PyTorch provides dynamic computation graphs, whereas Keras provides static computation graphs.
• PyTorch has no backend implementation, whereas Keras has a backend implementation that includes Tensorflow.
• PyTorch allows the user to easily debug the code, whereas debugging in Keras is difficult due to the presence of computational junks.

3 Best PyTorch Projects for Beginners

Below are the best projects that beginners must try to understand the working of PyTorch.

Image Classification Model using Transfer Learning in PyTorch

Transfer learning is a machine learning algorithm that allows us to reuse a pre-trained model on a new task. This project aims to:

• Understand the ResNet and transfer learning model
• Build a transfer learning model for image classification in PyTorch

The dataset of this project includes the images of driving licenses, social security, and other categories. All the images are of different shapes and sizes and are preprocessed before modeling.

You can go through this link to learn more about the project.

Build a Logistic Regression Model in PyTorch

This project aims to build a logistic regression model in PyTorch from scratch. Logistic regression is a probabilistic model that models the probabilities of discrete outcomes given the input variables. The end goal of the project is to:

• Learn the basic building blocks of a logistic regression model
• Apply the logistic regression model to a binary image classification problem

Following are the steps are taken to carry out this project.

• Data cleaning and preprocessing
• Building the logistic regression model
• Pretesting the model
• Training the model

You can go through this link to learn more about the project.

Hyperparameter Tuning For Neural Networks With PyTorch

This project aims to optimally tune the hyperparameters like epochs, learning rate, early stopping, and dropout of a neural network to improve the model performance using PyTorch. Hyperparameters are a set of values that control the learning process of a model. You can significantly improve the model performance by tuning the hyperparameters. 

The end goal of this project is to:

• Understand the hyperparameters of neural networks
• Understand the hyperparameter tuning to improve the model performance using Pytorch

You can learn more about the project using this link. 

5 Best PyTorch Projects for Social Good

Below are the best PyTorch projects for social good.

AI Uses PyTorch to Empower Cotton Farmers

We can use artificial intelligence to solve problems for social good. For instance, we can address specific pest issues by providing detailed spray recommendations to farmers. 

This project uses PyTorch to create a model that accurately predicts pests’ location within cotton crops. The captured image is passed through a multi-task network that verifies whether the image is valid or not. If it is valid, the detection branch identifies the potential location of the detected pests. 

You can learn more about the project using this link.

Toyota Saving Lives on the Roads

Toyota, the largest car manufacturer in the world, is investing in technologies like autonomous driving cars and predictive driver assistance to prevent cars from crashing.

Toyota collects real-world examples of crashes and simulates the data using PyTorch.

You can learn more about the project using this link.

Detecting Pathologies Through Computer Vision in Ultrasound

This project identifies the presence of a specific pathology on the ultrasound image and provides its location with mask and bounding box coordinates. Ultrasound is a portable and inexpensive modality to diagnose life-threatening diseases. 

The project creates an offline pathology mobile application that can be used in places without a stable internet connection. The main steps to carry out the project are given below:

• Deploy a model with a flask
• Create a Docker container to be deployed in the cloud
• Create an offline mobile app

You can learn more about: Deploying a Model Using Docker as Endpoint in a Pathology Mobile App

Instant Monitoring System Combating Human Rights Abuses

A group of collaborators undertook this project and used PyTorch to interpret their model of war crime prediction. They carried out the following tasks.

• Annotation of social media posts datasets
• Solving the related classification problems
• Building a Streamlit app dashboard
• Deploying the Streamlit app on cloud AWS to visualize and summarize the human rights abuses related to the war crime

Read more full article here: Instant Monitoring System Combating Human Rights Abuses Through NLP

Adaptive Relation Modeling Network For Structured Data

This project uses PyTorch and supports log-based anomaly detection. It aims to analyze the log sequences generated by the system at runtime to discover abnormal system behaviors (binary classification). 

You can learn more about the project using this link. 

Best Resources to Learn PyTorch

Below are some great resources for beginners and intermediate programmers to learn PyTorch.

• Udacity – Introduction to PyTorch | Deep Learning: https://www.udacity.com/course/deep-learning-pytorch–ud188
• PyTorch Tutorials and documentation: https://pytorch.org/tutorials/
• Coursera – Deep Neural Networks with PyTorch: https://www.coursera.org/learn/deep-neural-networks-with-pytorch#instructors
• Udemy – Modern Deep Learning in Python: https://www.udemy.com/course/data-science-deep-learning-in-theano-tensorflow/
• EdX – IBM’s Deep Learning with Python and PyTorch: https://www.edx.org/course/deep-learning-with-python-and-pytorch

Conclusion

We discussed that PyTorch is one of the most popular deep learning libraries specializing in automatic differentiation, tensor computations, and GPU acceleration. PyTorch is popular among researchers due to its Pythonic nature and flexibility. 

PyTorch can perform the following functions:

• Assembling a graph of neural networks
• Completing a forward pass and making predictions
• Computing the loss/error

Ready to test your skills?

If you’re interested in collaborating, apply to join an Omdena project at: https://www.omdena.com/projects