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features
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support

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Description

Dropbear is a compact SSH server and client that operates on various Unix-like platforms. It is an open-source program released under an MIT-style license, making it accessible for developers. Its design is particularly advantageous for "embedded" Linux systems, like those found in wireless routers. For those interested in staying updated on new releases or engaging in discussions, a low-traffic mailing list is available for subscriptions. With an efficient memory footprint, Dropbear can be compiled into a statically linked binary of just 110kB using uClibc on x86 architecture, provided that only the essential options are selected. Additionally, the server supports X11 forwarding and authentication-agent forwarding for clients using OpenSSH. Users can compile the server, client, key generator, and key converter into a single executable, similar to busybox, with the ability to disable certain features during compilation to conserve space. The software also includes a multi-hop mode that allows SSH TCP forwarding, enabling users to tunnel through multiple SSH hosts seamlessly in a single command, demonstrating its versatility in various networking scenarios. This flexibility makes Dropbear a favored choice for projects requiring lightweight and efficient SSH solutions.

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 No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

FreeBSD Yes 
Hugging Face No 
Llama 3 No 
NetBSD Yes 
OpenBSD Yes 

Integrations

FreeBSD No 
Hugging Face Yes 
Llama 3 Yes 
NetBSD No 
OpenBSD No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

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

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) No 
In Person No 

Types of Training

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

Vendor Details

Company Name

Matt Johnston

Country

Australia

Website

matt.ucc.asn.au/dropbear/dropbear.html

Vendor Details

Company Name

Luminal

Country

United States

Website

luminalai.com

Product Features

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 

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