The AWS Deep Learning AMIs now come with MXNet 1.4.0, Chainer 5.3.0, and TensorFlow 1.13.1, which is custom-built directly from source and tuned for high-performance training across Amazon EC2 instances.
AWS Deep Learning AMIs are now available on Amazon Linux 2
Developers can now use the AWS Deep Learning AMIs and Deep Learning Base AMI on Amazon Linux 2, the next generation of Amazon Linux. This version brings long term support (LTS) until June 30, 2023 and access to the latest innovations from the Linux ecosystem. The Deep Learning AMIs on Amazon Linux 2 have prebuilt and optimized virtual environments for TensorFlow (with Keras), MXNet, PyTorch, and Chainer on Python 3.6 and Python 2.7. Developers can continue using the AWS Deep Learning AMI and Deep Learning Base AMI on Ubuntu and Amazon Linux.
Amazon Linux 2 offers extended availability for software updates. The core operating system has 5 years of long-term support and provides access to the latest software packages through the Amazon Linux Extras repository. Amazon Linux 2 provides a modern execution environment with LTS Kernel (4.14) tuned for optimal performance on AWS, systemd support, and newer tooling (gcc 7.3.1, glibc 2.26, Binutils 2.29.1). Customers can also use Amazon Linux 2 virtual machine images for on-premises development and testing.
Faster training with TensorFlow 1.13
The Deep Learning AMI on Ubuntu, Amazon Linux, and Amazon Linux 2 now come with an optimized build of TensorFlow 1.13.1 and CUDA 10. On CPU instances, TensorFlow 1.13 is custom-built directly from source to accelerate performance on Intel Xeon Platinum processors that power EC2 C5 instances. Training a ResNet-50 model with synthetic ImageNet data using the Deep Learning AMI results in 9.4X faster throughput than stock TensorFlow 1.13 binaries. GPU instances come with an optimized build of TensorFlow 1.13 that is configured with NVIDIA CUDA 10 and cuDNN 7.4 to take advantage of mixed precision training on Volta V100 GPUs powering EC2 P3 instances. The Deep Learning AMI automatically deploys the most performant build of TensorFlow optimized for the EC2 instance of your choice when you activate the TensorFlow virtual environment for the first time.
For developers looking to scale their TensorFlow training to multiple GPUs, the Deep Learning AMIs come with the Horovod distributed training framework. The framework is fully optimized to efficiently use distributed training cluster topologies composed of Amazon EC2 P3 instances. Horovod is an open source distributed training framework based on the Message Passing Interface (MPI) model. This is a popular standard for passing messages and managing communication between nodes in a high-performance distributed computing environment. Training a ResNet-50 model using TensorFlow 1.13 and Horovod in the Deep Learning AMI results in 27% faster throughput than stock TensorFlow 1.13 on 8 nodes.
Better performance and ease-of-use with MXNet 1.4
AWS Deep Learning AMIs now come with the latest release of Apache MXNet 1.4 that bring improvements to performance and ease-of-use. MXNet 1.4 adds Java bindings for inference, Julia bindings, experimental control flow operators, JVM memory management, and many more under-the-hood enhancements. This release also improves MXNet support for Intel MKL-DNN with improved graph optimization and quantization. This feature reduces memory usage and improves inference time without a significant loss in accuracy.
AWS Deep Learning AMIs now support Chainer 5.3.0. 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.
Getting started with AWS Deep Learning AMIs
You can quickly get started with the AWS Deep Learning AMIs by using our getting started tutorial. For more tutorials, go to our developer guide for more resources and release notes. The latest AMIs are now available on the AWS Marketplace. You can also subscribe to our discussion forum to get new launch announcements and post your questions.
About the Authors
Aditya Bindal is a Senior Product Manager for AWS Deep Learning. He works on products that make it easier for customers to use deep learning engines. In his spare team, he enjoys playing tennis, reading historical fiction, and traveling.
Bhavin Thaker is a Software Development Manager in the AWS Deep Learning group, working on products that helps customers use deep learning tools efficiently, with a specific focus on the AWS Deep Learning AMI. He enjoys working with people and computers to make this happen. In his spare time, he enjoys reading and spending time with his family and friends.
Kalyanee Chendke is a Software Engineer for AWS Deep Learning. She works on products that make it easier for customers to get started with deep learning. Outside of work, she enjoys playing badminton, painting and spending time with friends and family.