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AWS-Logo_White-Color I ⁃ AWS HPC Overview II ⁃ ParallelCluster UI a. Deploy ParallelCluster UI b. Connect to ParallelCluster UI c. Summary III ⁃ ParallelCluster using CLI (optional) Prerequisites a. Sign in to the Console b. Create a Cloud9 Environment c. Work with the AWS CLI d. Create a Key Pair e. Install AWS ParallelCluster f. (Optional) Create config with ’pcluster configure’ g. Create a Cluster Config h. Build an HPC Cluster i. Log in to Your Cluster j. Submit your first HPC job Summary IV ⁃ Create an HPC Cluster a. Create a Cluster b. Connect to the Cluster c. Get to know your Cluster f. Submit your first HPC job g. Update your cluster h. Terminate Your Cluster V ⁃ Build a High-Performance File System a. Create HPC Cluster b. Create FSx Lustre c. Create S3 Bucket d. Link S3 to FSx Lustre e. Examine the File System f. About Lazy File Loading g. Install IOR Benchmark Tool h. View Metrics with CloudWatch i. Test IO Performance j. Summary and Cleanup VIII ⁃ Elastic Fabric Adapter (EFA) a. EFA Basics b. Create an HPC Cluster with EFA c. Examine an EFA enabled instance d. Work With Intel MPI e. Download, compile and run the OSU Benchmark f. Delete Your EFA Cluster VI ⁃ Remote Visualization using NICE DCV DCV Connect in ParallelCluster a. Create a cluster configured with NICE DCV a. Connect to your NICE DCV Session DCV Queue in ParallelCluster a. Create Security Group b. Modify Cluster Configuration c. Create DCV Session d. No-Ingress DCV Session DCV using web browser/native client a. Deploy EC2 instance with NICE DCV b. Connect to NICE DCV EC2 Instance c. Connect to Remote Desktop Session d. Terminate Your Instance IX ⁃ Distributed Machine Learning a. Upload training data to S3 b. Create a distributed ML cluster c. Run single node data preprocessing with Slurm d. Run PyTorch Data Parallel training on ParallelCluster e. Delete Distributed ML Cluster More Github AWS - HPC External Worshops Conferences Worshops Authors Feedback / Questions? Tags Clear History Star Fork Privacy | Site Terms | © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved. navigation Welcome to the Amazon Web Services HPC Workshops Amazon Web Services provides the most elastic and scalable cloud infrastructure to run your HPC applications . With virtually unlimited capacity, engineers, researchers, and HPC system owners can innovate beyond the limitations of on-premises HPC infrastructure. AWS delivers an integrated suite of services that provides everything you need to quickly and easily build and manage HPC clusters in the cloud to run the most compute intensive workloads across various industry verticals. These workloads span the traditional HPC applications, like genomics, computational chemistry, financial risk modeling, computer aided engineering, weather prediction, and seismic imaging, as well as emerging applications, like machine learning, deep learning, and autonomous driving. HPC on AWS removes the long wait times and lost productivity often associated with on-premises HPC clusters. Flexible configuration and virtually unlimited scalability allow you to grow and shrink your infrastructure as your workloads dictate, not the other way around. Additionally, with access to a broad portfolio of cloud-based services like Data Analytics, Artificial Intelligence (AI), and Machine Learning (ML), you can redefine traditional HPC workflows to innovate faster. Today, more cloud-based HPC applications run on AWS than on any other cloud. Select a workshop from the left panel or continue to AWS HPC Overview...