A new lightweight, modular, and scalable deep learning framework

Training and deploying AI models is often associated with massive data centers or super computers, with good reason. The ability to continually process, create, and improve models from all kinds of information: images, video, text, and voice, at massive scale, is no small computing feat. Deploying these models on mobile devices so they’re fast and lightweight can be equally daunting. Overcoming these challenges requires, a robust, flexible, and portable deep learning framework. We’ve built Caffe2 with this goal in mind.

Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI on mobile devices. This release provides access to many of the same tools, allowing you to run large-scale distributed training scenarios and build machine learning applications for mobile.

What is Caffe2?

Caffe2 aims to provide an easy and straightforward way to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with cross- platform libraries.

How do you use it?

Try out our quickstart tutorials, peruse the documentation, or jump straight in and start developing. Caffe2 comes with native Python and C++ APIs that work interchangeably so you can prototype quickly now, easily optimize later. Use cloud services or Docker images, or install it on your Mac, Windows or Ubuntu computer. Caffe2 also integrates with Android Studio, Microsoft Visual Studio, or XCode for mobile development.

Model Zoo

One of the greatest things about Caffe2 is the vibrant community of developers and researchers that share their work in the Caffe2 model zoo. You can use these models to quickly build demo applications and explore deep learning capabilities without doing any time-consuming and resource-intensive training. You can recreate and evaluate the results from others’ projects, hack together new uses, or improve upon the previously posted models.

Deep Learning Applications

Deep learning and neural networks can be applied to any problem. Caffe2 excels at handling large data sets, facilitating automation, image processing, and statistical and mathematical operations, just to name a few areas. It can be applied to any kind of operation and can help find opportunities, solutions, and insights.