Engineering Logs for Troubleshooting & Debugging
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
Problem – Stale Endpoint Slice Data in Anthropic Claude Multi‑GPU Training When training Claude models across multiple GPU nodes, operators have observed inconsistent gradients, divergent loss curves, and occasional training crashes. Typical symptoms include error messages such as: StaleSliceError: endpoint slice data is out of sync (expected version 4, got version 3) DataSliceOutOfSync – Slice … Read more
Kubernetes Scheduler Failing to Place AI Pods on AMD GPU Edge Nodes Problem Description In a remote edge site the AI inference workload never leaves the Pending state. Typical scheduler messages include: 0/5 nodes are available: 5 node(s) didn’t match node selector. Insufficient amd.com/gpu: 0/5 nodes are available: 5 node(s) have insufficient resources. scheduler: no … Read more
AMD GPU CLI Argument Parsing Failure During Disaster Recovery Problem Description (Symptoms and Impact) During automated disaster‑recovery runs on AMD GPU clusters, the recovery scripts invoke ROCm utilities such as rocm-smi, rocminfo, and rocm-bandwidth-test. The scripts abort with parsing errors, causing the scheduler (e.g., Slurm) to mark all GPUs as unavailable. Typical log excerpts are: … Read more
Problem: Kubernetes Secret Rotation Failure for Multi‑GPU Training Pods In a production AI training platform, each training job runs in a pod that requests multiple GPUs. Access to the GPU resource provider (e.g., NVIDIA GPU Cloud, a proprietary licensing server, or a cloud‑native GPU quota service) is gated by short‑lived credentials stored in a Secret. … Read more
Problem – Collection Creation Failure During Canary Deployment on AMD MI210 Nodes During a rolling canary deployment of a new AI model on a Kubernetes cluster that contains AMD Instinct MI210 accelerators, pods repeatedly terminate with the following error: [2026-06-27 10:12:34] ERROR: Failed to create collection: ROCm runtime version 5.5 does not match driver version 5.6 … Read more