CoreWeave Production Checklist
SkillDev tools'Production readiness checklist for CoreWeave GPU workloads.
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Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
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What this skill tells your AI
The instructions your AI receives, as published by jeremylongshore/tons-of-skills-marketplace in skills/.curated/coreweave-prod-checklist/SKILL.md and read by Ahel’s review.
Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.
Overview
Use this gate before exposing a CoreWeave inference or training workload to production. It proves that capacity, data protection, observability, and recovery are owned and tested; it is not a substitute for a service-specific risk review.
Prerequisites
- A signed deployment change, named service owner, and approved production namespace.
- Passing staging evaluation, capacity forecast, and rollback manifest.
- On-call routing, secret-manager references, and data classification for the workload.
Instructions
- Complete each checklist item with a link to its evidence, not a verbal assertion.
- Confirm a staged rollout, health check, and rollback command during the change window.
- Stop the release if any security, data, capacity, or observability gate lacks an owner.
- Record the final go/no-go decision, time, owner, and redacted verification receipt.
Inference Services
- GPU type and count validated for model size
- Autoscaling configured (KServe or HPA)
- Health and readiness probes set
- Resource requests AND limits specified
- Node affinity targeting correct GPU class
-
minReplicas >= 1for production (no cold starts)
Storage
- Model weights in PVC (not downloaded at startup)
- Checkpoints saved to persistent storage
- Storage class appropriate (SSD for inference, HDD for archival)
Security
- Secrets for model tokens and registry access
- Network policies applied
- Container images from trusted registries
Monitoring
- GPU utilization metrics collected
- Inference latency and throughput tracked
- Alert on pod restarts and OOM events
- Log aggregation configured
Rollback
kubectl rollout undo deployment/my-inference
kubectl rollout status deployment/my-inference
Output
- A production-readiness record linking capacity, storage, security, monitoring, and rollback evidence to the deployment.
- A clear go/no-go decision with a responsible owner and tested recovery command.
Error Handling
| Failed gate | Required action |
|---|---|
| Readiness or staging test fails | Hold the release and restore the prior known-good revision. |
| GPU capacity is unconfirmed | Do not launch; resolve quota or capacity with the platform owner. |
| Secret or network policy is missing | Block deployment until the namespace hardening baseline is present. |
| Monitoring route is absent | Block production exposure until alerts reach the on-call owner. |
Examples
Run the last production checks during the approved change window:
kubectl -n inference-prod apply -f release.yaml
kubectl -n inference-prod rollout status deployment/my-inference --timeout=15m
kubectl -n inference-prod get pods -l app=my-inference
If rollout or the smoke test fails, execute the documented rollback immediately, capture redacted events, and mark the change no-go rather than retrying with weaker security or capacity controls.
Resources
Next Steps
For upgrades, see coreweave-upgrade-migration.
Signals
- GitHub stars
- 3k
- Forks
- 415
- Last commit
- Oct 2026
Advanced
- Item type
- skill
- Key
coreweave-prod-checklist- Source
- github.com/jeremylongshore/tons-of-skills-marketplace
github.com/jeremylongshore/tons-of-skills-marketplace
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