Agent skill · software engineering · jeremylongshore
coreweave-core-workflow-b
Run distributed GPU training jobs on CoreWeave with multi-node PyTorch.
Why this skill is useful
Adds executable YAML configurations and scripts for distributed GPU training on CoreWeave that are not available in public documentation.
What it needs
Requires kubectl installed locally. Requires coreweave account access. About 2k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
CoreWeave Core Workflow: GPU Training Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc. Overview Run distributed GPU training on CoreWeave: single-node multi-GPU and multi-node training with PyTorch DDP, Slurm-on-Kubernetes, and shared storage. Prerequisites CKS cluster with multi-GPU node pools (8xA100 or 8xH100) Shared storage (CoreWeave PVC or NFS) Training container with PyTorch and NCCL Instructions Step 1: Single-Node Multi-GPU Training Step 2: Persistent Storage for Training Data Step 3: Monitor Training Progress Error Handling Error Cause Solution ------- ------- ---------- NCCL timeout Network issue between GPUs Use NVLink nodes (SXM4/SXM5) OOMKilled Batch size too large Reduce batch size or use gradient accumulation Checkpoint save failed PVC full Increase storage or prune old checkpoints Job evicted Preemption Use on-demand nodes for training Output A GPU training Job bound to explicitly selected node, storage, and checkpoint resources. A repeatable monitoring trail: pod state, GPU utilization, and training metrics are available to the authorized operator without exposing model inputs or credentials. Durable checkpoints on the approved PVC so a failed or preempted job can resume from a known state rather than silently restarting training. Examples Before scheduling a costly multi-GPU run, submit a small trusted smoke job to the same namespace and inspect its scheduling event and GPU allocation: If the job cannot schedule, stop before increasing quota or changing node selectors. Confirm the namespace quota, approved GPU class, and PVC binding with the platform owner; preserve the failed event output with secrets and customer data redacted. Resources CoreWeave CKS PyTorch Distributed Training Next Steps For troubleshooting, see coreweave-common-errors.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/coreweave-core-workflow-b