CaffeBAIR
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Related Products
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About
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Check out our web image classification demo! Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices. Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models. Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU.
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About
GPUs bring data in and out quickly, but have little locality of reference because of their small caches. They are geared towards applying a lot of compute to little data, not little compute to a lot of data. The networks designed to run on them therefore execute full layer after full layer in order to saturate their computational pipeline (see Figure 1 below). In order to deal with large models, given their small memory size (tens of gigabytes), GPUs are grouped together and models are distributed across them, creating a complex and painful software stack, complicated by the need to deal with many levels of communication and synchronization among separate machines. CPUs, on the other hand, have large, much faster caches than GPUs, and have an abundance of memory (terabytes). A typical CPU server can have memory equivalent to tens or even hundreds of GPUs. CPUs are perfect for a brain-like ML world in which parts of an extremely large network are executed piecemeal, as needed.
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
Anyone looking for an open-source deep learning framework with expression, speed and modularity
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Audience
Companies doing AI and ML development
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
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Online
Supported
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API
Offers API
Supported
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API
Offers API
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Screenshots and Videos |
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Pricing
No information available.
Free Version
Supported
Free Trial
Not Supported
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Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
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Live Online
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In Person
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Training
Documentation
Supported
Webinars
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Live Online
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In Person
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Company InformationBAIR
United States
caffe.berkeleyvision.org
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Company InformationNeural Magic
Founded: 2018
United States
neuralmagic.com
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Categories |
Categories |
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Deep Learning Features
Convolutional Neural Networks
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Document Classification
Supported
Image Segmentation
Not Supported
ML Algorithm Library
Not Supported
Model Training
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Neural Network Modeling
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Self-Learning
Not Supported
Visualization
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Integrations
AWS Elastic Fabric Adapter (EFA)
Supported
AWS Marketplace
Supported
Amazon Web Services (AWS)
Supported
Docker
Supported
Fabric for Deep Learning (FfDL)
Supported
Lambda
Supported
NVIDIA DIGITS
Supported
OpenVINO
Supported
Polyaxon
Supported
Pop!_OS
Supported
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Integrations
AWS Elastic Fabric Adapter (EFA)
Not Supported
AWS Marketplace
Not Supported
Amazon Web Services (AWS)
Not Supported
Docker
Not Supported
Fabric for Deep Learning (FfDL)
Not Supported
Lambda
Not Supported
NVIDIA DIGITS
Not Supported
OpenVINO
Not Supported
Polyaxon
Not Supported
Pop!_OS
Not Supported
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