Engineering Logs for Troubleshooting & Debugging
Problem During iterative development in the shared sandbox, data‑science engineers observed frequent GPU out‑of‑memory (OOM) failures when launching PyTorch training jobs. The failures coincided with a Prometheus alert that fired on the gpu_memory_usage_bytes metric exceeding a static threshold. The alert itself did not trigger any remediation, and the training jobs aborted with the following error … Read more
Problem – TGI Node Out‑of‑Memory (OOM) During Large Batch Inference with Autoscaling Disabled In a production Kubernetes deployment of Text Generation Inference (TGI), a batch job that aggregates thousands of text‑generation requests into a single inference call caused the pod to be killed with an OOM error. Autoscaling was deliberately disabled, so the node could … Read more
Problem The AI inference microservice runs on GPU‑enabled nodes in a Kubernetes cluster and consumes a high‑throughput Kafka topic. After a blue‑green deployment rollout, the new (green) pods start reporting CUDA out of memory (OOM) errors, causing request failures and a cascade of consumer lag in Kafka. Typical log excerpt from a failing pod: 2026-09-28 … Read more
Problem – Azure VM OOM error when launching AI inference after DR failover During a planned disaster‑recovery (DR) failover from the primary Azure region to a secondary region, the AI inference service fails to start. The container runtime reports a memory allocation failure and the process is terminated by the Linux OOM killer: 2024-09-15T08:12:34.567Z ERROR … Read more
Problem – Docker Inference Container OOM After Batch Size Overflow In a micro‑services architecture each AI inference service runs inside its own Docker container. Under normal load the service batches incoming requests (e.g., 16 images) and runs a single forward pass on the model. When a traffic spike or a mis‑configured batcher allows the queue … Read more
Problem Description During fine‑tuning of the OpenAI GPT‑4o model on a single GPU (e.g., A100 40 GiB, RTX 4090 24 GiB), the training loop crashes with a CUDA out‑of‑memory (OOM) error when the batch size or sequence length is increased. A typical failure looks like: RuntimeError: CUDA out of memory. Tried to allocate 12.34 GiB (GPU 0; 24.00 … Read more
Problem – GPU OOM on a Single A100 During DDP Mixed‑Precision Training When training a large transformer (e.g., 1.5 B‑parameter GPT‑like) on a 4 × NVIDIA A100 40 GB node with torch.nn.parallel.DistributedDataParallel (DDP) and mixed‑precision (AMP), one rank repeatedly crashes with an out‑of‑memory (OOM) error: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 5.23 GiB (GPU 2; 40.00 GiB total … 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
Problem – OOM and CPU Saturation Under Concurrent Vector Search & Embedding Ingestion In a production Retrieval‑Augmented Generation (RAG) service the Elasticsearch cluster receives >15 k requests / second. Each request either: Executes a k‑nn similarity search on a dense_vector field. Streams newly generated embeddings via bulk indexing. During peak traffic the following symptoms appear on the hot … Read more
Problem Description On‑premise LLM inference nodes equipped with NVIDIA GPUs (e.g., A100 40 GiB, RTX 3090 24 GiB) crash when processing prompts that exceed the pre‑allocated key‑value (KV) cache size. The failure manifests as a CUDA out‑of‑memory (OOM) exception during the attention kernel launch. Typical log excerpts: [2026-08-10 14:32:07] ERROR – torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 … Read more