byaws-samples· 161 skills
Use whenever someone runs a GPU / ML / GenAI / LLM workload on Amazon ECS: GPU on ECS, ECS GPU-optimized AMI, g4dn/g5/g6/p4/p5 on ECS, which ECS launch type for GPU, Inferentia/Trainium/Neuron on ECS, distributed training, model inference or vLLM/Triton/TGI/Ray on ECS, Capacity Blocks, GPU sharing, ASG per GPU type. Covers GPU on ECS-on-EC2 and ECS Managed Instances (GPU AMIs, NVIDIA runtime, instance families); mixed-instance ASGs supported but constrained (no weighting; managed scaling protects on the smallest type); Capacity Blocks; inference/serving; Neuron; distributed ML; GPU observability; a GPU/ML security slice (PCI/HIPAA). AWS Fargate has NO GPU: use ECS-on-EC2, Managed Instances, ECS Anywhere, or keep Fargate and call Bedrock. Trigger even if GenAI is unsaid. Use eks-genai for Kubernetes/EKS; SageMaker for fully-managed ML; Bedrock for managed foundation models; ecs-architect for non-accelerator ECS design; ecs-build to render the settled design as Terraform; ecs-security for deep compliance.
$npx -y skills add aws-samples/sample-apex-skills --skill ecs-genaiInstalls into the current project.
Run `npx skills use "https://github.com/aws-samples/sample-apex-skills" --skill "aws-samples/sample-apex-skills/ecs-genai"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/aws-samples/sample-apex-skills" that are relevant to the current task. Run `npx skills add "https://github.com/aws-samples/sample-apex-skills"` and select the relevant skills, then follow their instructions.