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ease
features
design
support

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Write a Review

Description

Enhance the efficiency of your deep learning projects and reduce the time it takes to realize value through AI model training and inference. As technology continues to improve in areas like computation, algorithms, and data accessibility, more businesses are embracing deep learning to derive and expand insights in fields such as speech recognition, natural language processing, and image classification. This powerful technology is capable of analyzing text, images, audio, and video on a large scale, allowing for the generation of patterns used in recommendation systems, sentiment analysis, financial risk assessments, and anomaly detection. The significant computational resources needed to handle neural networks stem from their complexity, including multiple layers and substantial training data requirements. Additionally, organizations face challenges in demonstrating the effectiveness of deep learning initiatives that are executed in isolation, which can hinder broader adoption and integration. The shift towards more collaborative approaches may help mitigate these issues and enhance the overall impact of deep learning strategies within companies.

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.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

AUSIS
Hugging Face
IBM Intelligent Video Analytics
Llama 3

Integrations

AUSIS
Hugging Face
IBM Intelligent Video Analytics
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

IBM

Founded

1911

Country

United States

Website

www.ibm.com/products/deep-learning-platform

Vendor Details

Company Name

Luminal

Country

United States

Website

luminalai.com

Product Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Product Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

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