AWS SageMaker

Skills

19 in the Cursor listing

• Dataset evaluationValidates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR).
• Dataset transformationGenerates code that transforms datasets between ML schemas for model training or evaluation.
• Directory managementManages project directory setup and artifact organization.
• finetuningGenerates code that fine-tunes a base model using SageMaker serverless training jobs.
• Finetuning techniqueSelects a fine-tuning technique (SFT, DPO, RLVR, or RLAIF) for the user's use case and validates it against the selected model's available recipes.
• Hyperpod cluster debuggerDiagnose and remediate cluster-wide HyperPod (EKS or Slurm) problems — creation / deployment failures (CloudFormation, EFA health check, lifecycle scripts, capacity), EKS access, node replacement, CloudFormation nested-stack errors, post-maintenance rollback state, dangling nodes
• Hyperpod issue reportGenerate comprehensive issue reports from HyperPod clusters (EKS and Slurm) by collecting diagnostic logs and configurations for troubleshooting and AWS Support cases.
• Hyperpod ncclDiagnose NCCL failures and adjacent training-pod failures on HyperPod GPU clusters (EKS or Slurm) — training hangs, AllReduce / collective-op timeouts, EFA or libfabric errors, rendezvous failures, EFA TCP fallback, /dev/shm or memlock issues, NCCL version mismatch across pods, c
• Hyperpod node debuggerDiagnose and remediate per-node issues on a HyperPod cluster (EKS or Slurm) — a specific node is unhealthy, unresponsive, stuck, or needs replacing.
• Hyperpod performance debuggerDiagnose performance issues on Amazon SageMaker HyperPod clusters — uneven NCCL bandwidth across nodes and poor filesystem throughput.
• Hyperpod slurm debuggerDiagnostic-only skill for Slurm scheduler and node-daemon issues on Amazon SageMaker HyperPod Slurm clusters.
• Hyperpod ssmRemote command execution and file transfer on SageMaker HyperPod cluster nodes via AWS Systems Manager (SSM).
• Hyperpod version checkerCheck and compare software component versions on SageMaker HyperPod cluster nodes - NVIDIA drivers, CUDA toolkit, cuDNN, NCCL, EFA, AWS OFI NCCL, GDRCopy, MPI, Neuron SDK (Trainium/Inferentia), Python, and PyTorch.
• Model deploymentGenerates code that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock.
• Model evaluationGenerates python code that evaluates SageMaker models.
• Model selectionSelects a base model for the user's use case by querying SageMaker Hub.
• planningDiscovers user intent and generates a structured, step-by-step plan for model customization workflows.
• Sdk getting startedValidates the user's environment for SageMaker AI operations — checks SDK version, AWS region, and execution role.
• Use case specificationCreates a reusable use case specification file that defines the business problem, stakeholders, and measurable success criteria for model customization, as recommended by the AWS Responsible AI Lens.