AWS Deep Learning AMIs now support Chainer and latest versions of PyTorch and Apache MXNet
The AWS Deep Learning AMIs provide fully-configured environments so that artificial intelligence (AI) developers and data scientists can quickly get started with deep learning models. The Amazon Machine Images (AMIs) now include Chainer (v3.4.0), a flexible and intuitive deep learning (DL) framework, as well as the latest versions of Apache MXNet and PyTorch.
The Chainer define-by-run approach allows developers to modify computational graphs on the fly during training. This provides greater flexibility in implementing dynamic neural networks like recurrent neural networks (RNNs) used for natural language processing (NLP) tasks such as sequence-to-sequence translation and question answering systems. Chainer comes fully-configured to take advantage of CuPy with NVIDIA CUDA 9 and cuDNN 7 drivers for accelerating computations on NVIDIA Volta GPUs powering Amazon EC2 P3 instances. You can quickly get started with Chainer using our step-by-step tutorial.
The AMIs, which are available for Ubuntu and Amazon Linux, provide prebuilt pip binaries for the latest official versions of deep learning frameworks in separate Conda-based virtual environments. Each framework is preconfigured with the latest version of NVIDIA CUDA and cuDNN that it supports. The AMIs now come with MXNet 1.1 and PyTorch 0.3.1 with several new bug fixes, performance, and usability improvements.
The following frameworks with CUDA 9 and cuDNN 7 support are included in the AMIs:
- Apache MXNet 1.1 (with Gluon)
- Caffe2 0.8.1
- Microsoft Cognitive Toolkit (CNTK) 2.4
- PyTorch 0.3.1
- TensorFlow 1.5
- Chainer 3.4.0
- Theano 1.0
- Keras 1.2.2 and Keras 2.1.3
- Caffe 1.0 with CUDA 8 and cuDNN 6
The AMIs also include model serving and debugging capabilities, which are provided by the following tools:
- Apache MXNet Model Server 0.1
- TensorFlow Serving 1.4.0
- TensorBoard 1.0.0
Getting started with the Deep Learning AMIs
The latest releases of the AWS Deep Learning AMIs are available in the AWS Marketplace. Our AMI selection topic helps you pick the right AMI for your deep learning project. We’ve also provided many tutorials and developer resources to help you quickly deploy your first deep learning model on AWS.
About the Author
Sumit Thakur is a Senior Product Manager for AWS Deep Learning. He works on products that make it easy for customers to get started with deep learning on cloud, with a specific focus on making it easy to use engines on Deep Learning AMI. In his spare time, he likes connecting with nature and watching sci-fi TV series.