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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
API Access
Has API
Integrations
Hugging Face
Llama 3
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
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
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization