Engineering Logs for Troubleshooting & Debugging
Problem Description Batch processing workloads that run on NVIDIA‑GPU enabled compute nodes in a cloud environment are aborting intermittently during startup. The failure manifests as an OAuth2 authentication error, causing the job to terminate with a non‑zero exit code. Typical log excerpts look like: [2023-07-15 12:34:56] ERROR AuthenticationError: token refresh failed after 3 attempts – … Read more
Problem – Stale Inference Results from NVIDIA GPU EndpointSlice after Autoscaling In a hybrid‑cloud deployment that mixes on‑premises NVIDIA GPU nodes with cloud‑based orchestration, engineers observed that after a Horizontal Pod Autoscaler (HPA) scale‑up or scale‑down event the Triton Inference Server began returning identical outputs for distinct inputs. The symptom manifested as: Log entry from … Read more
Problem – Inference Queue Backlog on NVIDIA GPU After Model Reload (Local Development) During local development of a deep‑learning service that uses NVIDIA GPUs (e.g., Triton Inference Server, PyTorch TorchScript, or TensorFlow Serving), engineers observed a sudden increase in pending inference requests after hot‑reloading a model. The symptoms include: GPU utilization drops to 0 % while … Read more
Problem: ReplicaSet Fails to Scale GPU Pods During Benchmark Load During a performance benchmark of a deep‑learning workload, a ReplicaSet that requests nvidia.com/gpu resources stalls after a subset of pods become running. The remaining pods stay in Pending with scheduler events such as: 0/5 nodes are available: 5 Insufficient nvidia.com/gpu Typical impact includes: Benchmark run … Read more
Problem Description On‑premise LLM inference nodes equipped with NVIDIA GPUs (e.g., A100 40 GiB, RTX 3090 24 GiB) crash when processing prompts that exceed the pre‑allocated key‑value (KV) cache size. The failure manifests as a CUDA out‑of‑memory (OOM) exception during the attention kernel launch. Typical log excerpts: [2026-08-10 14:32:07] ERROR – torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 … Read more
Problem Description The Horizontal Pod Autoscaler (HPA) in a Kubernetes‑based CI/CD pipeline is expected to add or remove training pods based on GPU utilization. In practice the HPA never scales out: the replica count stays at 1 even when GPU usage spikes to 95 % during model training. The symptom manifests as a growing job queue, … Read more
Problem – NVIDIA GPU Webhook Timeouts Under High API Traffic During peak loads on an API gateway that forwards real‑time inference requests to a Triton Inference Server (or similar GPU‑accelerated service), clients begin receiving HTTP 504 or custom webhook timeout errors. Typical log excerpts look like: 2024-05-28T14:12:03.421Z [ERROR] Triton Inference Server: Request timed out after … Read more
Problem – CRD Validation Failure When Deploying NVIDIA GPU Workloads After upgrading the NVIDIA GPU Operator (or during a fresh installation) AI training pods are rejected by the Kubernetes API server with errors such as: admission webhook “validation.gpu-operator.nvidia.com” denied the request: spec.gpus: Invalid value: “”: required property “count” missing error: unable to recognize “gpu-crd.yaml”: no … Read more