New in AWS Deep Learning AMIs: PyTorch 1.1, Chainer 5.4, and CUDA 10 support for MXNet
The AWS Deep Learning AMIs for Ubuntu, Amazon Linux, and Amazon Linux 2 now come with newer versions of the following deep learning frameworks: PyTorch 1.1 and Chainer 5.4. PyTorch 1.1 brings native TensorBoard support for model visualization and debugging, improvements to just-in-time (JIT) compiler, and better support for model parallelism in distributed training. This release also upgrades the NVIDIA driver to 418.40.04, Horovod to 0.16.1, and adds support for CUDA 10 in Apache MXNet environments.
The AWS Deep Learning AMIs for Ubuntu, Amazon Linux, and Amazon Linux 2 now come with newer versions of the following deep learning frameworks: PyTorch 1.1 and Chainer 5.4. PyTorch 1.1 brings native TensorBoard support for model visualization and debugging, improvements to just-in-time (JIT) compiler, and better support for model parallelism in distributed training. This release also upgrades the NVIDIA driver to 418.40.04, Horovod to 0.16.1, and adds support for CUDA 10 in Apache MXNet environments.
AWS Deep Learning AMIs also support other popular frameworks and interfaces including TensorFlow, Keras, Chainer, Gluon, and Caffe — pre-installed and fully-configured for you to start developing your deep learning models in minutes while taking full advantage of the computational power of Amazon EC2 instances. When you activate a Conda environment, the Deep Learning AMIs automatically deploy higher-performance builds of frameworks, optimized for the EC2 instance of your choice. For a complete list of frameworks and versions supported by the AWS Deep Learning AMI, see the release notes.
Get started quickly with the AWS Deep Learning AMIs using the getting-started guides and beginner to advanced level tutorials in our developer guide. You can also subscribe to our discussion forum to get launch announcements and post your questions.