Engineering Logs for Troubleshooting & Debugging
Problem Description The Horizontal Pod Autoscaler (HPA) attached to a Deployment that serves Hugging Face Transformers inference never creates additional replicas, even when the pods report CPU utilization above the configured target (e.g., 80%). The symptom manifests as: CPU usage in kubectl top pod consistently >90%. Inference latency spikes and occasional request time‑outs. HPA status shows … Read more
Problem – TensorRT Batch Inference Times Out Under High Concurrency In a production GPU inference service the following symptoms appear when the request rate spikes: Requests that submit a batched input to IExecutionContext::enqueueV2 return false after ~10 s. Server logs contain messages such as: [ERROR] Failed to enqueue inference: Timeout while waiting for GPU [TensorRT] Execution … Read more
Problem – Inconsistent Authentication After Secret Rotation During a blue‑green rollout of an ML pipeline on Kubernetes, the MLflow tracking server stopped logging experiments. The server reported authentication failures against the backend database despite the new deployment using the updated secret ARN. Typical log excerpt: 2024-06-28 14:32:11,842 ERROR mlflow.store.sqlalchemy_store SQLAlchemyStore: Backend store connection failed: sqlalchemy.exc.OperationalError: … Read more
Problem: Intermittent OAuth2 Flow Failures in PyTorch Microservices In a production AI platform built on PyTorch Distributed RPC and TorchServe, services communicate over Kubernetes using OAuth2‑bearer tokens. Under normal load the authentication succeeds, but during peak traffic or after certain deployments the following symptoms appear: HTTP 401 responses from inference endpoints with messages such as Token … Read more
Problem: Inconsistent RAG Answer Extraction Between Blue and Green Deployments In a production RAG pipeline accelerated with NVIDIA TensorRT, the same user query yields different answer strings when routed to the blue deployment versus the green deployment. The discrepancy appears intermittently during traffic shifts and can cause downstream ranking failures, SLA breaches, and user‑visible errors. … Read more
Problem – StatefulSet update fails with incompatible node configurations in a vLLM deployment A hybrid‑cloud environment runs a vllm StatefulSet that stores model files on Persistent Volume Claims (PVCs) and is front‑ended by an Ingress controller. After introducing a new node pool (AWS EKS) and applying a Helm upgrade that adds a nodeSelector for GPU accelerators, … Read more
Problem – Model Evaluation Fails with SSL Certificate Expiration During a distributed evaluation run, PyTorch attempts to download model checkpoints, dataset shards, or auxiliary assets from remote HTTPS endpoints (e.g., torch.hub, torch.utils.model_zoo, torchvision.datasets, or torch.distributed RPC). When the TLS certificate presented by the server has expired, the download aborts and the entire evaluation job terminates. … Read more
Problem Description During the staging deployment of a GPU‑accelerated inference service, the TensorRT engine build fails with the following error messages: [TensorRT] Error: Invalid sampling parameters (code: 3) Failed to create optimization profile: Invalid sampling parameters for input ‘input_0’ tensorrt.tensorrt.BuilderError: Invalid sampling parameters ASSERT FAILED: validateSamplingParameters() – sampling parameters out of allowed range The failure … Read more
Problem – HybridSearch Scoring Inconsistencies on GPU In a development sandbox (Ubuntu 22.04, NVIDIA A10G, ONNX Runtime 1.18, PyTorch backend) a BERT‑based reranker that uses the HybridSearch operator returns different top‑k results when the model is executed on the CPU (FP32) versus the GPU (FP16 mixed‑precision). Typical symptoms include: Top‑5 candidate order differs by up to three positions. … Read more
Problem – Inconsistent Citation Formatting After Model Update During development of a custom Retrieval‑Augmented Generation (RAG) pipeline built on PyTorch and Hugging Face Transformers, the generated answers increasingly contain malformed citations. Typical symptoms include: Missing source identifiers, e.g. [?] or plain text cite tags. Reference numbers that do not correspond to the retrieved document list, producing … Read more