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Description

Luminal is a high-performance machine-learning framework designed with an emphasis on speed, simplicity, and composability, which utilizes static graphs and compiler-driven optimization to effectively manage complex neural networks. By transforming models into a set of minimal "primops"—comprising only 12 fundamental operations—Luminal can then implement compiler passes that swap these with optimized kernels tailored for specific devices, facilitating efficient execution across GPUs and other hardware. The framework incorporates modules, which serve as the foundational components of networks equipped with a standardized forward API, as well as the GraphTensor interface, allowing for typed tensors and graphs to be defined and executed at compile time. Maintaining a deliberately compact and modifiable core, Luminal encourages extensibility through the integration of external compilers that cater to various datatypes, devices, training methods, and quantization techniques. A quick-start guide is available to assist users in cloning the repository, constructing a simple "Hello World" model, or executing larger models like LLaMA 3 with GPU capabilities, thereby making it easier for developers to harness its potential. With its versatile design, Luminal stands out as a powerful tool for both novice and experienced practitioners in machine learning.

Description

A worldwide device knowledge graph delivers organized and comprehensive insights regarding electronic devices, their functionalities, and the services they provide, along with the interconnections among them. Each device found in a household possesses an array of properties, including its brand, model, series number, manufacturer, present features and services, both physical and software attributes, compatible devices, regional data, and much more. This vast assortment of information about nearly every audiovisual device globally is housed within QuickSet’s device knowledge graph. QuickSet utilizes this knowledge graph to offer an extensive suite of functionalities for any given device. In addition to basic control, this knowledge graph infuses essential context into all user commands and actions, facilitating the dynamic identification of nearby devices. The algorithms employed by QuickSet depend on the knowledge graph that encompasses devices with diverse control capabilities, communication interfaces, and protocols, ensuring seamless interaction among devices. Ultimately, this interconnected system enhances user experience by making device management more intuitive and efficient.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Hugging Face Yes 
Llama 3 Yes 

Integrations

Hugging Face No 
Llama 3 No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Luminal

Country

United States

Website

luminalai.com

Vendor Details

Company Name

QuickSet Cloud

Country

United States

Website

quicksetcloud.com/device-knowledge-graph/

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

Product Features

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