Engineering Logs for Troubleshooting & Debugging
Problem – Webhook POST Times Out After AMD GPU Inference in Docker In a CI/CD pipeline a Docker container runs an AI model on an AMD Instinct GPU using ROCm. The inference step finishes successfully, but the subsequent HTTP POST to an external webhook hangs and eventually fails with a timeout, causing the job to … Read more
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 Description The GPT‑4o inference service runs in a Kubernetes pod that launches a Docker container with GPU acceleration (A100/H100). After upgrading the NVIDIA driver from 525.xx to 550.xx, the pod entered a crash loop: Container exit code 1 with log line: Failed to initialize NVML: Driver/library version mismatch Subsequent restarts showed Segmentation fault (core … 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
AMD GPU Nodes TLS Handshake Failure to Artifact Storage After Driver Update Problem Description After upgrading the ROCm driver suite to 6.0.3 on a mixed‑GPU Kubernetes cluster (NVIDIA A100/H100 and AMD Instinct MI250), all pods scheduled on AMD GPU nodes began failing to download model artifacts from the central artifact-storage service (Azure Blob, S3, or … 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 During long‑running AI training jobs inside a Docker container, the AMD GPU controller manager (amdgpu) crashes repeatedly. The symptom manifests as the container exiting with code 137 (SIGKILL) and loss of GPU access. Typical log excerpts are: dmesg: amdgpu: controller manager crashed, resetting GPU Docker log: error while creating device /dev/kfd: No such … Read more
Problem – CRD Validation Rejects AMD GPU Specification in Deployment Manifest When deploying an AI inference service that requires an AMD GPU, the kubectl apply -f deployment.yaml command fails with a validation error from the AMD GPU admission webhook. Typical error output looks like: error: admission webhook “validate.amd.com” denied the request: spec.template.spec.containers[0].resources.limits.amd.com/gpu: Invalid value: “1” … Read more
Google Gemini Token Limit Exceeded During Multi‑GPU Distributed Training Problem Description When launching a Distributed Data Parallel (DDP) or torch.distributed job that trains a Gemini model across multiple GPUs, the training loop aborts with errors similar to the following: Error 400: Token limit exceeded – request exceeds maximum allowed tokens of 8192 gemini_token_limit_exceeded: batch_id=42, tokens=10240, … Read more