What is PyTorch?

Open source project

The ML library developed by the Facebook AI Research lab (FAIR) is an open-source solution used in a growing range of applications. The PyTorch ecosystem is accomplished by serial libraries like Torchvision (for computer vision), Torchtext (for natural language processing), or even Torchaudio (for sound processing).

The packages provide ready-made models and popular datasets, complementing the whole ecosystem. Much of PyTorch's strength results from the open-source character, as it's a sum of countless contributions of machine learning developers and researchers worldwide. Growing as the community behind it grows, PyTorch is nearly unrestricted in building DL/ML solutions.

From research to production

The TorchScript ties up the unified research to production framework. Transforming PyTorch modules into a production-friendly form with TorchScript enables faster execution of models, becoming independent of Python runtime and increasing performance.

Pytorch

Why you should consider PyTorch

Pythonic style

It is designed to work smoothly with the Python ecosystem and can be used with popular Python packages.

Mixed-precision training

PyTorch supports methodology for mixed-precision training, combining single-precision and half-precision formats.

Libtorch library core

It is written mostly in C++ to achieve higher performance.

CUDA support

GPUs enable 50x or greater speed-ups in comparison to CPU calculations.

Distributed Data Parallelism

The feature enables running models across multiple machines to scale projects.

Fast & easy execution

PyTorch strives to make writing and using models as easy and productive as possible.

High speed of development

It provides a strong and constantly growing ecosystem.

Widespread adoption

Adoption of PyTorch by e.g. Microsoft and OpenAI assures its further development.

Cloud partners

There’s a possibility to set PyTorch on a vast amount of cloud-based environments from renowned providers.

circle
Let’s talk and see if we are a match for your next IT project.
Tomasz Kowalczyk CEO at NeuroSYS
Tomasz Kowalczyk
CEO at NeuroSYS
models
strzalaka 3 sekcja

Features of PyTorch

Dynamic computational graphs

The network’s behavior can be modified at runtime in a dynamic manner, improving efficient model optimization. This allows for great flexibility and facilitates the implementation of a lot of new architectures.

Modular design

Representing the neural networks, modules are fundamental to PyTorch. Modules are individual operations, representing the building blocks of a neural network, in the DL domain called layers. Modules are easy to transform, allowing fast construction of any model.

Tensors

PyTorch uses special data structures called Tensors to store and operate on multidimensional number arrays. They are similar to NumPy arrays but can be operated on GPUs which significantly speeds up the calculations.

Automatic differentiation for training and evaluating neural networks

Automatic differentiation evaluates functions' derivatives in neural networks. PyTorch contains the Autograd package, providing this functionality to automate processes and create computational graphs with nodes corresponding to mathematical operations.

Benefits of PyTorch

With PyTorch, you can build productive models to process various kinds of data for your benefits

debug

Easy debugging

PyTorch’s relationship with Python results in the possibility to use debugging tools of the latter. PyTorch offers the option of viewing any variable in the debugger or simply printing its state.

Document digitization

Convenient access to data

The possibility to load practically any type of data – the user can easily use pre-loaded datasets as well as own data using custom DataLoaders.

predictive-models

Pre-trained models

Instead of breaking the open door each time commonly used models are needed, researchers can freely adapt existing pre-trained neural networks.

Recruitment

Effortless adoption

A reasonable learning curve and intuitive API make PyTorch a solution easy to adopt among your Python developers team. Fast to introduce in place of similar solutions – an all built-in “plug and play” with minimum configuration.

Customer satisfaction

Reduced development time

Thanks to the close relations with Python and a similar syntax, PyTorch supports productivity. Equipped with a simple interface and API, the environment runs smoothly on Windows and Linux. Less hassle, more work done.

Customers benefit NeuroSYS

PyTorch's community

The community of PyTorch users is known as a friendly and helpful environment for developers and researchers. The forum behind it is full of tips on using various architectures, making it the right place for your team to search for answers and support.

PyTorch open-source machine learning library

Where to use PyTorch

Computer vision

Neural networks can be used in object detection, tracking, and image segmentation. The Torchvision package, a part of the PyTorch ecosystem, provides necessary functionalities. The library consists of pre-trained models, popular datasets, and common image transformations for computer vision.

Autonomous vehicles

Tesla and Uber adopt PyTorch in building neural networks, changing the automotive industry. The revolution is possible thanks to multitasking models that collect loads of traffic data. Engineers integrate neural networks to run efficiently in cars, providing real-time reactions in all scenarios.

Robotic solutions in industrial applications

Solutions built using PyTorch support various industries. For one, smart machines let farmers get rid of weeds, reduce costs and pesticide usage. In such cases, PyTorch is the base of a computer vision and machine learning system, recognizing crops from weeds and targeting the latter for spraying.

Artificial data generation

Generative Adversarial Network (GAN) models are trained with two opposing neural networks, one generating new data samples, and the other recognizing real examples. These models are capable of generating images, performing text-to-image translations, or even reconstructing videos from photos.

Natural language processing (NLP)

Training machines to identify and understand human languages enables e.g. gathering information from documents. In automatic text classification and translation, the Torchtext package is used, thanks to pre-built, generic loaders for popular datasets and text resources, including vocabulary objects.

Pytorch machine learning

They use PyTorch

Global companies building large, scalable products benefit from using PyTorch. These are among others:

facebook
Capgemini
Tesla
Uber
Comcast
IBM
Intel
Twitter
Nvidia

At NeuroSYS, we specialize in research and development, harnessing science to benefit business

Our team develops state-of-the-art projects, incorporating the newest technologies like PyTorch, to fully address our clients’ needs. We use PyTorch to create neural networks models, train, validate and test them in the process of building solutions such as computer vision and natural language processing.
Looking for mature IT partner
It's free, no strings attached. Let's hear ourselves and see if we are a match.

Don’t know how to start?

Check out how you can start working with us with minimum risk and no commitment, getting the maximum value for your business.

Two women are talking and holding a tablet
Two laptops on a busy desk
development-process-audit
A woman is holding a notebook and a man is holding a laptop.

A short, free consultation will help you gain new knowledge about your digital product and get to know us better, no strings attached.
Explore

It is an exhaustive assessment of your application, paying special attention to code quality, key functionalities, proper documentation, and security issues.
Explore

A development process audit will assess a variety of your processes, such as communication and project management – just in 2 weeks.
Explore

A development trial helps you to lower the risk of hiring an unsuitable IT company.
Explore

This site uses cookies. By continuing to navigate on this website, you accept the use of cookies.
icon
Done!
Thank you for your application!
icon
Let's get in touch!
We want to get to know you a little bit, but we need some help from your side. Let's start with filling gaps below.
Full name
Please provide us with your full name
Email
Please provide us your current Email
Telephone
Please provide us with your Phone number
Your LinkedIn profile
Please show us your professional social side :)
Link to your portfolio / GitHub
Please insert your Portfolio / GitHub URL correctly
Message
Nothing to say? Maybe just a little bit? Even "Hi" will work - thanks!
CV file
Please upload your CV
Select file
Please choose one of the following
I hereby authorize the processing of my personal data included in this form for the present recruitment-related purposes by NeuroSYS Sp. z o.o. (Rybacka 7 Street, 53-565 Wrocław) (in accordance with the General Data Protection Regulation (EU) 2016/679 of 27.04.2018 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, as well as repealing Directive 95/46/EC (Data Protection Directive)). I acknowledge that submitting my personal data is voluntary, I have the right to access my data and rectify it.
Read and accept
I hereby authorize the processing of my personal data included in my job application for the needs of future recruitment processes by NeuroSYS Sp. z o.o. (Rybacka 7 Street, 53-565 Wrocław).
Read and accept
Captcha is required