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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
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
VII ⁃
Simulations with AWS Batch
a. Lab Environment
b. IAM Role Setup
c. Build A Container With Docker
d. Upload to Amazon ECR
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. Job Dependencies
l. Next Steps & Clean up
m. Optimize Cost with EC2 Spot
n. Next Steps & Clean up
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
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
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h. View Metrics with CloudWatch