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Selects, investigates, and compares AWS object, file, and block storage services, and answers cost, performance, configuration, security, and troubleshooting questions about storage services. Applies when a user asks where to store or archive data based on their usage patterns; which storage service to choose or how two compare; how to migrate data from on-premises or between AWS services; how to protect, replicate, or recover data; how to optimize storage costs; where to deploy shared NFS, SMB, or POSIX file systems; where to store vector embeddings or tabular data; what storage backs enterprise file shares, self-managed databases on EC2, VMware, or stateful containers; or asks what an AWS storage service can do or how it works. Relevant for storage needs for workloads such as AI/ML, analytics, EDA, HPC, media, genomics, or financial trading. Not applicable for SQL query engines (Athena, Spark, Redshift, EMR), ETL (Glue), streaming (Kafka, MSK, Kinesis), or managed database services (RDS, Aurora, DynamoDB).

Use this Skill: https://skilld.dev/gh/aws/agent-toolkit-for-aws/aws-storage

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referencesfsx-lustre-knowledge.md

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Amazon FSx for Lustre

This reference captures information addressing common gotchas and frequently asked questions for Amazon FSx for Lustre to support accurate model responses. It is not a complete specification. The authoritative source for current specifications, limits, quotas, and API behavior is the Amazon FSx for Lustre User Guide, Amazon FSx API Reference, Amazon FSx for Lustre product page, and Amazon FSx for Lustre FAQs. Retrieve current specifications, limits, and quotas from those pages before citing specifics; cite figures only from those pages.

1. Overview

What it is: Amazon FSx for Lustre is a fully managed service that provides high-performance, cost-effective, and scalable storage powered by Lustre, the world's most popular high-performance file system. FSx for Lustre provides the fastest storage performance for GPU instances in the cloud, with up to terabytes per second of throughput, millions of IOPS, sub-millisecond latencies, and virtually unlimited storage capacity.

Well-suited for: compute-intensive workloads where storage must keep pace with large fleets of GPU or CPU compute, such as ML training and inference, HPC, genomics, seismic and financial modeling, media rendering, and back-end EDA, with native Amazon S3 integration that makes datasets in S3 transparently accessible as files.

See When to choose Amazon FSx and Amazon FSx for Lustre Features.

2. Service Information

You MUST retrieve information from the linked documentation in the below table before answering any user question on the related topic.

Topic Example service characteristics, features, and actions Documentation
Access and protocols Lustre parallel client on Linux; cross-AZ client access; Elastic Fabric Adapter for high-throughput, low-latency inter-node networking Accessing file systems
Deployment and availability Persistent 1 and 2; Scratch 2 Deployment and storage class options
Storage classes SSD; Intelligent-Tiering Lustre storage classes
Data protection and management S3 data repository association; auto-import and auto-export; writes from multiple locations; file release to free local space; capacity increase; backups Using data repositories with Amazon FSx for Lustre
Performance aggregate throughput; sub-millisecond latency; high IOPS; metadata IOPS; NVIDIA GPUDirect Storage (GDS); working-set sizing Amazon FSx for Lustre performance
Security KMS at rest; in-transit automatic from EC2 instances that support it (Nitro-based) and between file-system hosts; instances that do not support it and on-premises clients do not receive in-transit encryption; POSIX permissions; VPC and security groups Security in Amazon FSx for Lustre
Health and monitoring CloudWatch metrics; alarms; CloudTrail Monitoring Amazon FSx for Lustre file systems
Pricing storage capacity; throughput capacity; metadata IOPS; backup storage; cross-AZ transfer; Intelligent-Tiering (not exhaustive, review pricing page for the full list of pricing dimensions) Amazon FSx for Lustre pricing

Related services and integrations

You MUST retrieve information from the linked documentation in the below table before answering any user question on the related service.

Example service Example characteristics and common workloads Documentation
Amazon EKS (FSx for Lustre CSI driver) dynamic and static Kubernetes volume provisioning; pod access to shared Lustre Amazon FSx for Lustre CSI driver
Amazon SageMaker high-throughput training-data input; file-system mount for training jobs Setting up training jobs to access datasets

3. Troubleshooting

You MUST retrieve information from the linked documentation in the below table before providing the user with any guidance on the related area.

Example area Example errors Example fixes Documentation
Connectivity and networking mount fails; Lustre client missing install the client matching the OS and kernel Installing the Lustre client
Connectivity and networking mount hangs or times out; security group misconfigured allow the required Lustre ports between the file-system and client security groups (restrict to the specific client security group IDs); verify VPC routing File system access control with Amazon VPC
Data protection and recovery data repository misconfigured; slow first read; conflicting writes; backups only on Persistent non-S3-linked restore IAM access; pre-stage hot files; write from a single source; use Persistent (not S3-linked) for backups Data repository association lifecycle state, Linking to an S3 bucket, Protecting your data with backups
Capacity and scaling out of space with a large S3 dataset release infrequently-accessed files; size to the working set Releasing files, Increasing storage capacity

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub1mo

    This skill provides domain expertise for AWS storage services, offering architectural and operational guidance. It emphasizes security best practices, such as encryption and least-privileged access, and relies on official AWS documentation and repositories. No malicious patterns were detected.

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