Engineering Logs for Troubleshooting & Debugging
Problem Description – Empty Retrieval Results on A100/H100 GPU Cluster When executing a Retrieval‑Augmented Generation (RAG) pipeline on a multi‑node GPU cluster (A100/H100), the RAGRetriever consistently returns an empty document list despite: Valid natural‑language queries. A fully populated FAISS index built from the knowledge base. Successful generation of query embeddings on the GPU. Typical log … Read more
Problem: HAProxy Timeouts During High‑Throughput Batch Ingestion In a multi‑GPU training cluster, HAProxy fronts a set of tensorflow/torchserve workers that accept large JSON or binary payloads via HTTP POST. During nightly batch uploads (10 GB + per request) clients observe: HTTP 504 “Gateway Timeout” with HAProxy log entry SC (client timeout) Intermittent HTTP 502 “Bad Gateway” with log entry sH … Read more
Problem Description In an air‑gapped deployment of large language models (LLMs) on AMD GPUs, inference jobs abort once the prompt length exceeds a certain token count. The failure manifests as memory allocation errors that appear unrelated to the reported free VRAM. Typical log excerpts: 2026-07-12 14:03:21 [INFO] Starting inference on model LLaMA-13B 2026-07-12 14:03:22 [ERROR] … 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 – Token Refresh Failure During Disaster Recovery on AMD GPU Systems During a planned disaster‑recovery (DR) drill, AI inference workloads that rely on AMD Instinct GPUs stopped abruptly after the failover to the backup cluster. The ROCm runtime emitted the following error: TokenRefreshError: failed to refresh access token – ROCm runtime aborts with exit … Read more
Problem – Mistral AI GPU OOM during Batch Inference When running a data‑pipeline that performs text‑classification with the Mistral‑7B model on a GPU with 24 GiB VRAM (e.g., RTX 3090), the job crashes after processing a few 512‑token batches. The failure manifests as: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.12 GiB (GPU 0; 24.00 GiB … 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 – 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 — Token‑limit failures during language‑model evaluation on AWS EC2 GPU instances When running a Hugging Face transformers evaluation pipeline on an EC2 GPU instance (e.g., p3.2xlarge, g5.12xlarge), the script aborts with errors such as: ValueError: Token indices sequence length is longer than the model maximum sequence length (max_length=2048). or RuntimeError: CUDA out of memory. Tried to allocate 12.3 GiB … Read more
Problem Description In a Kubernetes‑based microservices architecture that uses AMD GPUs for Retrieval‑Augmented Generation (RAG), the generated citations are malformed. Observed symptoms include: Missing source fields in the JSON payload returned by the generation service. Inconsistent or empty metadata.title and metadata.author entries. Intermittent duplicate citation entries when pods restart under memory pressure. Log excerpts such … Read more