Updates to AWS Deep Learning Containers for TensorFlow 1.15.2 with Python-3.7
The AWS Deep Learning Containers are available today with the latest framework versions of 1.15.2 with python 3.7 support . The release includes updates to the Amazon SageMaker Experiments package. Amazon SageMaker Experiments is a feature in Amazon SageMaker that lets you organize, track, compare, and evaluate machine learning (ML) experiments and model versions. The TensorFlow 1.15.2 python3.7 training containers now also include SageMaker Debugger, which allow data scientists to save and inspect the model tensors during training jobs.
The AWS Deep Learning Containers are available today with the latest framework versions of 1.15.2 with python 3.7 support . The release includes updates to the Amazon SageMaker Experiments package. Amazon SageMaker Experiments is a feature in Amazon SageMaker that lets you organize, track, compare, and evaluate machine learning (ML) experiments and model versions. The TensorFlow 1.15.2 python3.7 training containers now also include SageMaker Debugger, which allow data scientists to save and inspect the model tensors during training jobs.
You can launch the new versions of the Deep Learning Containers on Amazon SageMaker, Amazon Elastic Kubernetes Service (Amazon EKS), self-managed Kubernetes on Amazon EC2, and Amazon Elastic Container Service (Amazon ECS). For a complete list of frameworks, end-of-life announcements, and versions supported by the AWS Deep Learning Containers, see release notes for TensorFlow 1.15.2.
More details can be found in the AWS marketplace, and a list of available containers can be found in our documentation. Get started quickly with the AWS Deep Learning Containers 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.