Develop Sophisticated Systems with the Most Popular Deep Learning Frameworks
The AWS Deep Learning AMI (available for Amazon Linux and Ubuntu) and the AWS Deep Learning CloudFormation Template let you quickly deploy and run any of the major deep learning frameworks at any scale. The AWS Deep Learning AMI lets you create managed, auto-scaling Clusters of GPUs for large scale training, and run inference on trained models. It is pre-installed with Apache MXNet, TensorFlow, Caffe2 (and Caffe), Theano, Torch, CNTK, and Keras. The AWS Deep Learning AMI is provided and supported By Amazon Web Services, for use on Amazon EC2. There is no additional charge for the AWS Deep Learning AMI – you only pay for the AWS resources needed to store and run your applications.
Apache MXNet is Amazon’s deep learning framework of choice and is the platform for our AI services, as well as many AI projects within Amazon.com. It is a flexible, efficient, portable and and scalable open source library for deep learning that supports declarative and Imperative programming models across a wide variety of programming languages and use cases.
Apache MXNet features a single implementation of backend system and common operators with support for a large number of frontend languages, including Python, C ++, Scala, and R. Due to Apache MXNet’s architecture, performance remains consistent of the frontend language used
Unique memory optimizations allow Apache MXNet to be used across a wide variety of use cases. After taking advantage of the cloud to train your model, they can be deployed in connected devices at the edge, mobile phones, browsers, industrial and consumer drones, Or simply remain in the cloud.
Apache MXNet Genesis with automatic scheduling of the source code that can be parallelized over a distributed environment. Paired with Amazon EC2 P2 instances, Apache MXNet applications scale across GPUs with up to 91% efficiency, and across cluster nodes with up to 88% Efficiency.
TensorFlow is an open source software library for numerical methods using stateful dataflow graphs.
Caffe2 is a lightweight, modular, and scalable deep learning framework designed to help researchers train large machine learning models and deliver AI on mobile devices.
Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation.
Microsoft Cognitive Toolkit
The Microsoft Cognitive Toolkit is a unified deep-learning toolkit by Microsoft Research that describes neural networks as a series of computational steps via a directed graph.
Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
Theano is a Python library that allows you to define, optimize, and e v a luate mathematical expressions involving multi-dimensional arrays efficiently.
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Amazon Machine Image for Deep Learning
Amazon Machine Images are a great way to quickly start using deep learning technologies on AWS. The AWS Deep Learning AMIs come pre-installed with popular open source deep learning frameworks (Apache MXNet, TensorFlow, Theano, Torch, CNTK and Caffe) Acceleration through pre-configured CUDA drivers, and supporting tools such as Anaconda and Jupyter.
AWS CloudFormation for Deep Learning
AWS CloudFormation templates are an easy way to scale up multiple instances of EC2 instances for big compute jobs such as training deep tennis networks. Developers can use the distributed Deep Learning CloudFormation template to spin up a scaled-out, Using the Deep Learning AMI for their larger training requirements