Try the Deep Learning AMIs

The AWS Deep Learning AMIs and the AWS Deep Learning CloudFormation Template provide the foundation for data scientists, machine learning practitioners, and research scientists to accelerate work in Deep Learning. The Deep Learning AMIs let you run deep learning in the cloud, at any scale. You can quickly launch instances of the AMIs, pre-installed with open source deep learning frameworks to train sophisticated, custom AI models, experiment with new algorithms and learn new deep learning skills and techniques. The Deep Learning AMIs let you create managed, auto-scaling clusters of GPUs for large scale training, or run inference on trained models using Apache MXNet, TensorFlow, Caffe, Theano, Torch and Keras. The AMIs are provided and supported by Amazon Web Services, for use on Amazon EC2. There is no additional charge for the Deep Learning AMIs – you only pay for the AWS resources needed to store and run your applications.

To train over multiple instances, the Deep Learning CloudFormation Template provides a simple way to launch these resources quickly and use the Deep Learning AMIs. AWS CloudFormation templates are an easy way to scale up multiple instances of EC2 instances for big compute jobs such as training deep neural networks. Developers can use the distributed Deep Learning CloudFormation template to spin up a scaled-out, elastic cluster of P2 or G2 instances using the Deep Learning AMI for their larger training requirements.To learn more, visit the AWS EC2 Compute Blog for information on CloudFormation usage for Deep Learning.

Offered for both Linux and Ubuntu, the Deep Learning AMIs are provisioned with multiple deep learning frameworks and easy to launch tutorials to get you started quickly.  The Deep Learning AMIs install dependencies, tracks library versions, and validates code compatibility. See below for the key libraries prebuilt and preconfigured with all dependencies included:

Deep Learning in the AWS Marketplace

The AWS Deep Learning AMIs are available in the AWS Marketplace for Amazon Linux and Ubuntu 14.04, supporting Python 2 & 3.

Available AWS Regions:

EU (Ireland)

US East (N. Virginia)

US West (Oregon)


- Jupyter with python2.7 and python3.4 kernel, awscli, matplotlib, scikit-image, cpplint, pylint, pandas, graphviz, boto, boto3, bokeh and seaborn python packages
- The Anaconda2 and Anaconda3 Data Science platform

Each Framework supports a single command bash script to run MNist training out of the box illustrating proper installation, config and model accuracy, found in the following directories:

Ubuntu Linux: /home/ubuntu/src/bin

Amazon Linux: /home/ec2-user/src/bin

- Intel MKL (Currently limited to MXNet)
- Nvidia Cuda and CuDNN (All Frameworks supported)

Apache MXNet

MXNet is a flexible,efficient, portable and scalable open source library for deep learning. It supports declarative and imperative programming models, across a wide variety of programming languages, making it powerful yet simple to code deep learning applications. MXNet is efficient, inherently supporting automatic parallel scheduling of portions of source code that can be parallelized over a distributed environment. MXNet is also portable, using memory optimizations that allow it to run on mobile phones to full servers.


Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.


Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.


TensorFlow is an open source software library for numerical computation using data flow graphs.


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.


CNTK - The Microsoft Cognitive Toolkit - is a unified deep-learning toolkit by Microsoft Research.


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.

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