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Description
Caffe is a deep learning framework designed with a focus on expressiveness, efficiency, and modularity, developed by Berkeley AI Research (BAIR) alongside numerous community contributors. The project was initiated by Yangqing Jia during his doctoral studies at UC Berkeley and is available under the BSD 2-Clause license. For those interested, there is an engaging web image classification demo available for viewing! The framework’s expressive architecture promotes innovation and application development. Users can define models and optimizations through configuration files without the need for hard-coded elements. By simply toggling a flag, users can seamlessly switch between CPU and GPU, allowing for training on powerful GPU machines followed by deployment on standard clusters or mobile devices. The extensible nature of Caffe's codebase supports ongoing development and enhancement. In its inaugural year, Caffe was forked by more than 1,000 developers, who contributed numerous significant changes back to the project. Thanks to these community contributions, the framework remains at the forefront of state-of-the-art code and models. Caffe's speed makes it an ideal choice for both research experiments and industrial applications, with the capability to process upwards of 60 million images daily using a single NVIDIA K40 GPU, demonstrating its robustness and efficacy in handling large-scale tasks. This performance ensures that users can rely on Caffe for both experimentation and deployment in various scenarios.
Description
GhostBSD is a user-friendly, desktop-focused operating system that is derived from FreeBSD, featuring MATE, OpenRC, and a collection of OS packages to streamline the user experience. It comes preloaded with a variety of essential software, enabling users to maximize its capabilities right from the start. Utilizing the GTK environment, GhostBSD offers an aesthetically pleasing interface and a comfortable experience that aligns with modern BSD platforms, fostering an authentic Unix work environment. Built upon FreeBSD's foundational code, its lineage traces back to the Unix Research at the University of California, Berkeley, where it was historically known as "BSD Unix" or "Berkeley Unix." In contemporary terms, it is commonly referred to as BSD, which stands for Berkeley Software Distribution. The main objective of the project is to merge security, privacy, stability, usability, openness, and freedom, ensuring that it remains accessible to all at no cost. Additionally, the user experience is further enriched by tools such as Networkmgr, which are specifically developed as part of the GhostBSD initiative, contributing to its seamless functionality and user satisfaction. This commitment to continuous improvement and user-centric design sets GhostBSD apart in the realm of operating systems.
API Access
Has API
API Access
Has API
Integrations
AWS Elastic Fabric Adapter (EFA)
AWS Marketplace
Amazon Web Services (AWS)
Docker
Fabric for Deep Learning (FfDL)
Lambda
NVIDIA DIGITS
OpenVINO
Polyaxon
Pop!_OS
Integrations
AWS Elastic Fabric Adapter (EFA)
AWS Marketplace
Amazon Web Services (AWS)
Docker
Fabric for Deep Learning (FfDL)
Lambda
NVIDIA DIGITS
OpenVINO
Polyaxon
Pop!_OS
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
BAIR
Country
United States
Website
caffe.berkeleyvision.org
Vendor Details
Company Name
GhostBSD
Founded
2009
Country
Canada
Website
www.ghostbsd.org
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization