Engineering Logs for Troubleshooting & Debugging
Problem – TorchServe Pods Remain Pending When Using a Custom Scheduler In a Kubernetes cluster that hosts GPU‑enabled node pools, a real‑time inference service is deployed with TorchServe. The deployment succeeds, but the resulting pods stay in Pending state indefinitely. The custom scheduler (configured via a scheduler profile or extender) reports that it cannot find … Read more
Problem – Intermittent Streaming Response Interruption In a Kubernetes‑deployed microservice that serves a PyTorch model via a streaming HTTP endpoint, clients occasionally receive truncated inference results. The HTTP connection is closed before the model finishes generating the full token sequence, leading to partial outputs and downstream processing failures. The issue appears only under high concurrency … Read more
Problem When attempting to launch a PyTorch training job on a local kind cluster, the pod creation fails with an admission controller error. The user sees the following output from kubectl apply and kubectl describe pod: $ kubectl apply -f pytorch-job.yaml pod/pytorch-trainer created error: error when creating “pytorch-job.yaml”: admission webhook “podsecuritypolicy.k8s.io” denied the request: spec.securityContext.runAsUser: … Read more
Problem Description During a Kubernetes rolling update of a TorchServe‑hosted Retrieval‑Augmented Generation (RAG) service, the RAGRetriever began returning an empty list of documents for every query. The symptom manifested as log entries such as: INFO 2026-09-21 12:34:56,789 RAGRetriever: No documents retrieved for query ERROR 2026-09-21 12:34:57,102 faiss index not found at path /model/index.faiss WARNING 2026-09-21 … Read more
Problem – Webhook Timeout After PyTorch Distributed Job Completes on A100/H100 Nodes Observed behavior Training jobs launched with torchrun (TorchElastic) finish successfully on a multi‑node A100 cluster. Immediately after the last epoch, the user‑defined on_finish callback attempts to POST a JSON payload to https://monitor.example.com/notify. The job process exits with a non‑zero status and logs contain: … Read more
PyTorch Embedding Dimension Mismatch After Restoring Checkpoints on a New Cluster Problem Description During a disaster‑recovery startup, a pretrained model fails to load its torch.nn.Embedding layer: RuntimeError: size mismatch for embedding.weight: copying a param with shape torch.Size([50000, 768]) from checkpoint of shape torch.Size([47500, 768]) The error appears as soon as model.load_state_dict(checkpoint[“model_state”]) is called. The rest … Read more
Problem – Batch Size Overflow During Live Video Streaming Inference In a production pipeline that consumes a continuous stream of video frames, the inference service throws runtime errors such as: RuntimeError: Expected tensor size [16, 3, 720, 1280] but got [32, 3, 720, 1280] AssertionError: batch size exceeds max_batch_size (configured in TorchServe) These errors appear … Read more
Problem – Tensor Shape Mismatch in RAG Context Injection When using a Retrieval‑Augmented Generation (RAG) pipeline inside a Docker‑based Jupyter sandbox, the model often drops or truncates tokens from the retrieved documents. The symptom manifests as missing context during generation and degraded answer quality. Typical log excerpt: context_input_ids.shape: torch.Size([3, 210]) context_attention_mask.shape: torch.Size([3, 128]) RuntimeError: Expected … Read more
Problem Statement When benchmarking high‑throughput models on NVIDIA A100 GPUs with torch.cuda.amp.autocast enabled, the log_prob() method of torch.distributions frequently returns -inf or NaN for otherwise valid probability tensors. The symptom appears only when the batch size crosses a hardware‑dependent threshold (e.g., 8 k, 16 k, or higher) and manifests as: loss = -inf (nan) Subsequent backward passes … Read more
Problem – Intermittent CUDA OOM and Tensor Shape Mismatches During Batched Audio Inference In a Kubernetes cluster that runs containerized PyTorch inference services, pods processing variable‑length audio streams occasionally crash with: CUDA out of memory. Tried to allocate 2.34 GiB (GPU 0; 8.00 GiB total capacity; 6.12 GiB already allocated; 1.23 GiB free; 6.12 GiB … Read more