Updated AWS Deep Learning AMIs: New Versions of TensorFlow, Apache MXNet, Keras, and PyTorch
This release of the AWS Deep Learning AMIs support Apache MXNet 1.0, including a new model serving capability for MXNet that packages, runs, and serves deep learning models with just a few lines of code. Also included are a new gradient compression capability, and a new model converter that converts neural network code written with the Caffe framework to MXNet code, making it easier for developers to take advantage of MXNet’s scalability and performance.
This release of the AWS Deep Learning AMIs support Apache MXNet 1.0, including a new model serving capability for MXNet that packages, runs, and serves deep learning models with just a few lines of code. Also included are a new gradient compression capability, and a new model converter that converts neural network code written with the Caffe framework to MXNet code, making it easier for developers to take advantage of MXNet’s scalability and performance.
The AMIs also come with improved framework support for NVIDIA Volta. They include PyTorch v0.3.0, and support NVIDIA CUDA 9 and cuDNN 7, with significant performance improvements for training models on NVIDIA Volta GPUs. As well, they include a version of TensorFlow built from the master and merged with NVIDIA processors for Volta support. We’ve also added Keras 2.0 support on the CUDA 9 version of the AWS Deep Learning AMIs to work with TensorFlow as the default backend.
Our AMI selection guide helps you pick the right AMI for your deep learning project and gets you going with just one click. We’ve also provided many quick tutorials and developer resources to help you accelerate model training.