Artificial Intelligence
Author: David Arpin
Amazon SageMaker automatic model tuning produces better models, faster
Amazon SageMaker recently released a feature that allows you to automatically tune the hyperparameter values of your machine learning model to produce more accurate predictions. Hyperparameters are user-defined settings that dictate how an algorithm should behave during training. Examples include how large a decision tree should be grown, the number of clusters desired from a […]
Announcing the Amazon SageMaker MXNet 1.2 container
The Amazon SageMaker pre-built MXNet container now uses the latest release of Apache MXNet 1.2. Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. And the pre-built MXNet container makes it easy to write your deep learning scripts naturally […]
Amazon SageMaker now supports PyTorch and TensorFlow 1.8
Starting today, you can easily train and deploy your PyTorch deep learning models in Amazon SageMaker. This is the fourth deep learning framework that Amazon SageMaker has added support for, in addition to TensorFlow, Apache MXNet, and Chainer. Just like with those frameworks, now you can write your PyTorch script like you normally would and […]
Use the Amazon SageMaker local mode to train on your notebook instance
This blog post shows you how to use the Amazon SageMaker Python SDK local mode on a recently launched multi-GPU notebook instance type to quickly test a large scale image classification model.
Mount an EFS file system to an Amazon SageMaker notebook (with lifecycle configurations)
In this blog post, we’ll show you how you can mount an Amazon Elastic File System (EFS) to your Amazon SageMaker notebook instance. This is an easy way to store and access large datasets, and to share machine learning scripts from your SageMaker notebook instance. Amazon SageMaker notebooks provide fast access to your own instance running […]
Amazon SageMaker support for TensorFlow 1.5, MXNet 1.0, and CUDA 9
Amazon SageMaker pre-built deep learning framework containers now support TensorFlow 1.5 and Apache MXNet 1.0, both of which take advantage of CUDA 9 optimizations for faster performance on SageMaker ml.p3 instances. In addition to performance benefits, this provides access to updated features such as Eager execution in TensorFlow and advanced indexing for NDArrays in MXNet. More […]