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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
Traditional CPUs are struggling to meet the growing demands for enhanced computing capabilities, while GPU processors can outperform them by a factor of 100 to 200 in terms of data processing speed. We offer specialized servers tailored for machine learning and deep learning, featuring unique capabilities. Our advanced hardware incorporates the NVIDIA® GPU chipset, renowned for its exceptional operational speed. Among our offerings are the latest Tesla® V100 cards, which boast remarkable processing power. Our systems are optimized for popular deep learning frameworks such as TensorFlow™, Caffe2, Torch, Theano, CNTK, and MXNet™. We provide development tools that support programming languages including Python 2, Python 3, and C++. Additionally, we do not impose extra fees for additional services, meaning that disk space and traffic are fully integrated into the basic service package. Moreover, our servers are versatile enough to handle a range of tasks, including video processing and rendering. Customers of LeaderGPU® can easily access a graphical interface through RDP right from the start, ensuring a seamless user experience. This comprehensive approach positions us as a leading choice for those seeking powerful computational solutions.
API Access
Has API
API Access
Has API
Integrations
AWS Elastic Fabric Adapter (EFA)
AWS Marketplace
Amazon Web Services (AWS)
C++
Docker
Fabric for Deep Learning (FfDL)
Lambda
MXNet
NVIDIA DIGITS
NVIDIA DRIVE
Integrations
AWS Elastic Fabric Adapter (EFA)
AWS Marketplace
Amazon Web Services (AWS)
C++
Docker
Fabric for Deep Learning (FfDL)
Lambda
MXNet
NVIDIA DIGITS
NVIDIA DRIVE
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
€0.14 per minute
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
LeaderGPU
Country
Netherlands
Website
www.leadergpu.com
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization