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I ⁃
AWS HPC Overview
II ⁃
Getting Started in the Cloud
Prerequisites
a. Sign in to the Console
b. Create a Cloud9 Environment
c. Work with the AWS CLI
d. Create an S3 Bucket
e. Launch the EC2 Dashboard
f. Create an EC2 Instance
g. Create an IAM Role
Summary
III ⁃
Create an HPC Cluster
a. Install AWS ParallelCluster
b. Configure ParallelCluster
c. Create a Cluster Config
d. Build an HPC Cluster
e. Log in to Your Cluster
f. Submit your first HPC job
h. Update your cluster
i. Terminate Your Cluster
IV ⁃
Build a High-Performance File System
a. Install AWS ParallelCluster
b. Create your HPC Cluster
c. Examine the File System
d. About Lazy File Loading
d. Install IOR Benchmark Tool
e. Test IO Performance
f. View Metrics with CloudWatch
g. Summary and Cleanup
V ⁃
Simulations with AWS Batch
a. Lab Environment
b. Workshop Initial Setup
c. Build Your AMI with Packer
d. Create a Docker Repository
e. Set up Compute Environment
f. Set up a Job Queue
g. Set up a Job Definition
h. Describe Your Environment
i. Run a Single Job
j. Run an Array Job
k. Next Steps & Clean up
VI ⁃
Remote Visualization using NICE DCV
DCV using ParallelCluster
a. Create a cluster configured with NICE DCV
b. Build an HPC Cluster with NICE DCV
c. Connect to your NICE DCV Session
e. Terminate Your Cluster
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
VII ⁃
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
VIII ⁃
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
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f. View Metrics with CloudWatch