Train ML models quickly and cost-effectively with Amazon SageMaker (Level 200)
Training machine learning models at scale often requires significant investments. In this session, we show how Amazon SageMaker enables you to reduce time and costs to train and tune machine learning (ML) models without the need to manage infrastructure. Learn how to use models using built-in tools to manage and track training experiments, automatically choose optimal hyperparameters, debug training jobs, and monitor the utilization of system resources such as GPUs, CPUs, and network bandwidth. We show how SageMaker Training tools enables faster distributed training, including libraries for data parallelism and model parallelism, and the Amazon SageMaker distributed training libraries automatically split models and training datasets across GPU instances to help you complete distributed training faster.
When you deploy images with WDS you need to go through windows installaton by choosing which image you are going to use, you need to accept license etc. which is very time consuming if you need to deploy same image to many machines. In this post we will go one step further and perform a clean installation without any user interaction. To make this work we need to create a special file with the answers to those question (like accepting AULA, choosing partitions, region and language etc) which you can save in the bootable media and the setup can read automatically to perform an unattended installation of the OS.
Windows 10 Oktober 2018 Update Is Distributed Automatically
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