Posted On: Jul 23, 2018

The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now come with a custom build of TensorFlow 1.9 optimized for high performance training, the latest Apache MXNet 1.2 that includes several performance and usability improvements, the new Keras 2-MXNet backend with high performance multi-GPU training support, and the new MXBoard tool for improved debugging and visualization of MXNet training models.  

Faster training with optimized TensorFlow 1.9

Deep Learning AMIs include a compute-optimized build of TensorFlow 1.9 custom-built directly from the source code to accelerate training performance on the Intel Xeon Platinum processors powering Amazon EC2 C5 instances. The AMIs also offer a GPU-optimized build of TensorFlow 1.9 configured with NVIDIA CUDA 9 and cuDNN 7 to take advantage of mixed precision training on the Volta V100 GPUs powering Amazon EC2 P3 instances. Deep Learning AMIs automatically deploy the high performance build of TensorFlow optimized for the EC2 instance of your choice when you activate the TensorFlow virtual environment for the first time. To learn more, see our TensorFlow tutorial.

In addition, for developers looking to scale their TensorFlow training from single GPU to multiple GPUs, the AMIs come fully-configured with Horovod, which is a popular open-source distributed training framework. We have released several performance improvements and configurations in this pre-built version of Horovod that make it faster to run distributed training over clusters of Amazon EC2 P3 instances. For details, see our blog post.

Apache MXNet 1.2 Improvements

Deep Learning AMIs support the latest release of Apache MXNet 1.2, offering better ease-of-use and faster performance. MXNet 1.2 includes a new Scala-based, thread-safe, high-level inference API that makes it easier to perform predictions using deep learning models trained with MXNet. MXNet 1.2 also offers the new Intel MKL-DNN integration that accelerates neural network operators such as convolution, deconvolution, and pooling on compute-optimized C5 instances, and support for enhanced FP16 that accelerates mixed precision training on Tensor Cores of NVIDIA Volta V100 GPUs powering Amazon EC2 P3 instances. Lastly, MXNet 1.2 comes with a new Open Neural Network Exchange Format (ONNX) module for importing ONNX models into the MXNet symbolic interface. ONNX is an open format for representing deep learning models that can be used to promote interoperability between deep learning frameworks.

High performance multi-GPU training with MXNet backend for Keras 2

Deep Learning AMIs come pre-installed with the new Keras-MXNet deep learning backend. Keras is a high-level, Python neural network API that is popular for its quick and easy prototyping of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Keras developers can now use MXNet as their backend deep engine for distributed training of CNNs and RNNs, and get higher performance. Developers can design in Keras, train with Keras-MXNet, and run inference with MxNet in large-scale production environments. To learn more see this blog post.

Improved debugging support with MXBoard

With MXBoard, a Python package that provides APIs for logging MXNet data for visualization in TensorBoard, developers can easily debug and visualize their MXNet model training. MXBoard supports a range of visualizations including histograms, convolutional filters, visual embedding, and more.

You can quickly get started with AWS Deep Learning AMIs with our step-by-step tutorial and the developer guide. The latest AMIs are available on the AWS Marketplace. Subscribe to our discussion forum to get launch announcements and post your questions.