Lizard logic, Abbyy NeoML open source library adds Python
Abbyy didn’t let the summer slowdown ‘silly season’ dampen its news cycle too much, the ‘digital intelligence company’ announced an update for NeoML, its cross-platform open source machine learning library.
With a heritage in document management and data intelligence, Abbyy has in recent years extends its (application and service) products to what it hopes is now viewed as a more fully-fledged platform play level.
As such, the company is now working to extend and finesse its work in areas like machine learning model (ML) development.
By bringing support for the Python programming language, Abbyy is the one of the most popular language for machine learning and AI.
A 2020 survey from CodinGame showed that Python tied with Java in RedMonk’s quarterly rankings and suggested that it is the “most loved” programming language.
As many readers will know, Python is widely used in all industries for tasks like automation, web development, scripting, web scraping and data analysis by companies like Google, Pinterest, Spotify, Dropbox etc.
The NeoML framework also offers x5-10 speed improvements as well as 20+ new ML methods including 10 network layers and optimisation methods. Additionally, NeoML now supports Apple M1 chips, GPU on Linux-based machines and Intel GPU.
Learn the lizard
Python is also commonly used in academia with students to learn programming, data science and machine learning. Its
With the added Python support, Abbyy hopes more developers and organisations will be able to utilise NeoML to build, train and deploy models for specific core ML tasks including:
- object identification
- semantic segmentation
- predictive modeling
For example, healthcare organisations can streamline administrative processes, map infectious diseases and personalise medical treatments; insurers – predict premiums and losses for their policies.
High inference speed
“Open source is a powerful driver of technological innovation. We aim to support advancements in artificial intelligence by working together with the developer community to further grow and improve our open-source library,” commented Bruce Orcutt, senior vice president of product marketing at ABBYY.
Orcutt says that NeoML offers high inference speed, platform independence and support for mobile devices.
“We invite all developers, data scientists and academia to use and contribute to NeoML on GitHub,” said said.
The speed improvements have made NeoML fast. It offers up to 10 times faster performance for classical algorithms and up to 30% faster neural network training and inference than the previous version.
NeoML is designed as a universal tool to process and analyse data in a variety of formats including text, image, video and others. Users can deploy models anywhere: in the cloud, on-premises, in the browser or on-device.
The library supports C++, Java and Objective C programming languages and offers 20+ traditional ML algorithms such as classification, regression and clustering frameworks.
Its neural network models support over 100 layer types. The library is fully cross-platform – a single code base that can be run on all popular operating systems including Windows, Linux, macOS, iOS and Android – and optimized for both CPU and GPU processors.