coreweave-pack

Integrate CoreWeave deployments into CI/CD pipelines with GitHub Actions. Use when automating container builds, deploying inference services from CI, or validating GPU manifests in pull requests. Trigger with phrases like "coreweave CI", "coreweave github actions", "coreweave pipeline", "automate coreweave deploy".

Diagnose and fix CoreWeave GPU scheduling, pod, and networking errors. Use when pods are stuck Pending, GPUs are not allocated, or experiencing CUDA and NCCL errors. Trigger with phrases like "coreweave error", "coreweave pod pending", "coreweave gpu not found", "coreweave debug", "fix coreweave".

Deploy KServe InferenceService on CoreWeave with autoscaling and GPU scheduling. Use when serving ML models with KServe, configuring scale-to-zero, or deploying production inference endpoints on CoreWeave. Trigger with phrases like "coreweave inference service", "coreweave kserve", "coreweave model serving", "deploy model on coreweave".

Run distributed GPU training jobs on CoreWeave with multi-node PyTorch. Use when training models across multiple GPUs, setting up distributed training, or running fine-tuning jobs on CoreWeave H100 clusters. Trigger with phrases like "coreweave training", "coreweave multi-gpu", "distributed training coreweave", "fine-tune on coreweave".

Optimize CoreWeave GPU cloud costs with right-sizing and scheduling. Use when reducing GPU spend, selecting cost-effective instances, or implementing scale-to-zero for dev workloads. Trigger with phrases like "coreweave cost", "coreweave pricing", "reduce coreweave spend", "coreweave budget".

Handle training data and model artifacts on CoreWeave persistent storage. Use when managing large datasets, configuring storage classes, or implementing data pipelines for GPU workloads. Trigger with phrases like "coreweave data", "coreweave storage", "coreweave pvc", "coreweave dataset management".

Collect CoreWeave cluster diagnostics for support tickets. Use when preparing a support case, collecting GPU node status, or documenting pod failures. Trigger with phrases like "coreweave debug", "coreweave support", "coreweave diagnostics", "collect coreweave logs".

Deploy inference services on CoreWeave with Helm charts and Kustomize. Use when deploying multi-model inference, managing GPU deployments at scale, or templating CoreWeave manifests. Trigger with phrases like "deploy coreweave", "coreweave helm", "coreweave kustomize", "coreweave deployment patterns".

Configure RBAC and namespace isolation for CoreWeave multi-team GPU access. Use when managing team permissions, isolating GPU quotas, or implementing namespace-level access control. Trigger with phrases like "coreweave rbac", "coreweave permissions", "coreweave namespace isolation", "coreweave team access".

Diagnose the most expensive silent failure on a CoreWeave multi-node GPU job: GPUDirect RDMA falling back from InfiniBand to TCP. When NCCL drops from NET/IB to NET/Socket, collectives keep running with NO error but throughput collapses (commonly 5-20x slower) while every GPU still bills at full rate — 5x the GPU bill for the same work, invisibly. Paste an NCCL_DEBUG=INFO log (and/or a pod-spec, ibstat, or all_reduce_perf output) and the bundled deterministic script verdicts whether RDMA is actually engaged, which of the three required conditions is missing, and the fix. Use when multi-node training is slow, when checking whether RDMA/InfiniBand is engaged, or when all-reduce bandwidth looks low. Trigger with "coreweave slow training", "is RDMA working", "NCCL fell back to TCP", "NET/Socket", "GPUDirect RDMA", "infiniband not used", "multi-node training slow".

Hunt down CoreWeave GPU cost leaks — idle reserved capacity, wrong-GPU-type right-sizing waste, allocated-but-idle instances, and on-demand spend that should be committed — then produce a CFO-grokkable, dollar-ranked FinOps report. CoreWeave ships no cost dashboard and no billing API, so the spend view is built from PromQL against its managed Grafana. Use when a user asks why their CoreWeave GPU bill is high, wants to find wasted GPU spend or idle reservations, or needs a GPU FinOps cost report. Trigger with "coreweave cost", "why is my coreweave bill", "wasted GPU spend", "idle reserved capacity", "GPU cost leak".

Triage a dead or degraded GPU on a CoreWeave node fast — decide reschedule vs GPU-reset vs node-reboot vs RMA from an Xid code or a pasted dmesg / nvidia-smi blob, so a bad card does not silently kill a multi-day training run. Use when a GPU throws an Xid error, a node "fell off the bus", a training run stalls or NCCL hangs on one rank, or you need to know whether to replace, reset, or just reschedule. Trigger with "xid error", "gpu fell off the bus", "coreweave gpu dead", "should I RMA this GPU", "gpu node triage".

Deploy a GPU workload on CoreWeave with kubectl. Use when running your first GPU job, testing inference, or verifying CoreWeave cluster access. Trigger with phrases like "coreweave hello world", "coreweave first deploy", "coreweave gpu test", "run on coreweave".

Incident response runbook for CoreWeave GPU workload failures. Use when inference services are down, GPUs are unavailable, or responding to production incidents on CoreWeave. Trigger with phrases like "coreweave incident", "coreweave outage", "coreweave runbook", "coreweave service down".

Configure CoreWeave Kubernetes Service (CKS) access with kubeconfig and API tokens. Use when setting up kubectl access to CoreWeave, configuring CKS clusters, or authenticating with CoreWeave cloud services. Trigger with phrases like "install coreweave", "setup coreweave", "coreweave kubeconfig", "coreweave auth", "connect to coreweave".

Set up local development workflow for CoreWeave GPU deployments. Use when building containers locally, testing YAML manifests, or iterating on model serving configurations before deploying. Trigger with phrases like "coreweave dev setup", "coreweave local testing", "develop for coreweave", "coreweave container build".

Configure CoreWeave across development, staging, and production environments. Use when setting up multi-environment GPU infrastructure, separating namespaces, or managing per-environment GPU quotas. Trigger with phrases like "coreweave environments", "coreweave staging", "coreweave multi-env", "coreweave namespace setup".

Set up GPU monitoring and observability for CoreWeave workloads. Use when implementing GPU metrics dashboards, configuring alerts, or tracking inference latency and throughput. Trigger with phrases like "coreweave monitoring", "coreweave observability", "coreweave gpu metrics", "coreweave grafana".

Optimize CoreWeave GPU inference latency and throughput. Use when reducing inference latency, maximizing GPU utilization, or tuning batch sizes and concurrency. Trigger with phrases like "coreweave performance", "coreweave latency", "coreweave throughput", "optimize coreweave inference".

Production readiness checklist for CoreWeave GPU workloads. Use when launching inference services, preparing GPU training for production, or validating deployment configurations. Trigger with phrases like "coreweave production", "coreweave go-live", "coreweave checklist", "coreweave launch".

Production-ready patterns for CoreWeave GPU workload management with kubectl and Python. Use when building inference clients, managing GPU deployments programmatically, or creating reusable CoreWeave deployment templates. Trigger with phrases like "coreweave patterns", "coreweave client", "coreweave Python", "coreweave deployment template".

Secure CoreWeave deployments with RBAC, network policies, and secrets management. Use when hardening GPU workloads, managing model access, or configuring namespace isolation. Trigger with phrases like "coreweave security", "coreweave rbac", "secure coreweave", "coreweave secrets".

Upgrade CoreWeave deployments and migrate between GPU types. Use when migrating from A100 to H100, upgrading CUDA versions, or updating inference server versions. Trigger with phrases like "upgrade coreweave", "coreweave gpu migration", "coreweave cuda upgrade", "migrate coreweave".