Engineering Logs for Troubleshooting & Debugging
Problem – ONNX Runtime Fails to Load Model After Azure AD OAuth2 Config Change In a staging environment the ONNX Runtime instance is configured to download models from an Azure Machine Learning Model Registry that is protected by Azure AD OAuth2 client‑credentials flow. After a recent change to the Azure AD application (secret rotation, redirect‑URI … Read more
Problem Description During integration tests in the staging environment, the inference service crashes when loading a Hugging Face dataset with a custom filter. The failure manifests as a datasets.exceptions.FilterParseError raised by the datasets.Dataset.filter utility. Typical log excerpt: Traceback (most recent call last): File “/app/load_data.py”, line 42, in <module> ds = load_dataset(“my_dataset”, split=”train”).filter(filter_expr) File “…/site-packages/datasets/dataset_dict.py”, line 1234, … Read more
Problem Description On‑premises inference jobs that run large transformer models (e.g., BERT‑large, GPT‑2‑XL) with ONNX Runtime repeatedly fail with a timeout error such as: ORT_RUN_TIMEOUT: Inference session timed out after 300000 ms Error: Execution failed due to timeout – see OrtRunOptionsSetRunLogVerbosityLevel for details Typical symptoms observed across multiple deployments include: Job termination after exactly 5 minutes … Read more
Problem Description A blue‑green rollout of a Hugging Face transformers service on Kubernetes fails during container start‑up. The new pods report: MountVolume.SetUp failed for volume “model-pvc”: permission denied, are you root? … OSError: [Errno 13] Permission denied: ‘/model/pytorch_model.bin’ Traceback (most recent call last): File “/app/serve.py”, line 42, in <module> model = AutoModel.from_pretrained(“/model”) File “…/transformers/modeling_utils.py”, line … Read more
Problem Description A canary rollout of a Hugging Face transformers model on a Kubernetes cluster results in the newly created pods entering CrashLoopBackOff. The failure manifests during the model initialization phase, before the inference server becomes ready. Typical log excerpts include: 2024-08-30T12:15:42.123Z ERROR [model_loader] RuntimeError: CUDA out of memory. Tried to allocate 12.34 GiB. This … Read more
Problem Description A production inference server that runs a Retrieval‑Augmented Generation (RAG) pipeline with TensorRT started throwing runtime errors after the passage encoder checkpoint was upgraded. The server processes thousands of queries per second in batches of up to 128 on a single GPU. Within seconds of deployment the following errors appeared in the logs: … Read more
Problem Description In a staging environment a tgi:latest container is deployed in a Kubernetes pod with 16 GiB of RAM. During startup the container crashes with an out‑of‑memory (OOM) termination: kubectl describe pod tgi-staging-abc123 … State: Waiting Reason: OOMKilled … Events: Type Reason Age From Message —- —— —- —- ——- Warning OOMKill 2m kubelet, node-01 … Read more
TGI checkpoint storage class not found after A/B deployment Problem During an A/B testing rollout of Text Generation Inference (TGI) instances, the newly created replica fails to start and logs an error similar to: Failed to load checkpoint: storage class “fast-ssd” not found Other observed symptoms include: Pod tgi-a-12345 stays in Pending state. kubectl describe … Read more
Problem Description Symptoms and Impact After upgrading the Docker image to CUDA 12.4, vLLM v0.6.3 started emitting malformed JSON for function‑calling prompts. The downstream FastAPI gateway receives a truncated or syntactically invalid JSON payload, which triggers json.JSONDecodeError exceptions and causes request drops. Log snippet from a worker thread: INFO vllm.engine.output_parser – Received partial JSON: {“name”: “search”, “parameters”: … Read more
Problem: RAG pipeline context injection failure after MLflow model update During a blue‑green deployment, traffic was shifted from the green version of the rag-service endpoint to the newly promoted blue version. After the switch, the Retrieval‑Augmented Generation (RAG) pipeline began sending prompts to the LLM that contained an empty or malformed context variable. Typical symptoms … Read more