nvidia-container-cli device node error /dev/nvidiactl no such file or directory

The container runtime could not find the NVIDIA device nodes on the host. The kernel modules are not loaded, or the node was created after the container runtime cached its view, the GPU is invisible inside every container on this host. This entry explains how to confirm the cause, apply the fix, and separate it from adjacent containers failures.

The literal signature is "nvidia-container-cli device node error /dev/nvidiactl no such file or directory". It is a environment failure associated with NVIDIA Container Toolkit, device plugins, Enroot, and Pyxis. The line identifies the failing operation or subsystem, while the surrounding evidence decides whether it is the initiating fault or a downstream symptom.

The container runtime could not find the NVIDIA device nodes on the host. The kernel modules are not loaded, or the node was created after the container runtime cached its view, the GPU is invisible inside every container on this host. The failure becomes visible at this call site because the operation first requires the missing resource, valid state, healthy peer, or correct result. Earlier log lines and a known-good control carry more causal value than the final wrapper exception.

• The workload stops or loses forward progress after emitting "nvidia-container-cli device node error /dev/nvidiactl no such file or directory".
• A retry on the same configuration reproduces the failure because the causal state has not changed.
• The outer framework exception can hide the rank, node, allocation, or dependency that failed first.
• Increasing timeouts or reducing workload size can suppress the symptom without correcting the cause.

• NVIDIA Container Toolkit, device plugins, Enroot, and Pyxis
• production-shaped multi-accelerator workloads
• containerized and bare-metal deployments of the same stack

Apply the following checklist to a small reproduction: each box below is a positive signal that you are looking at this exact failure rather than a sibling in the same taxonomy.

• ✓Find the first occurrence of "nvidia-container-cli device node error /dev/nvidiactl no such file or directory" and preserve at least 100 lines before it.
• ✓Identify which rank, node, device, or process emitted the first related warning.
• ✓nvidia-smi on the host creates the device nodes as a side effect, which is why "it works after I run nvidia-smi" is a common and misleading observation. On a fresh boot with no display manager, nothing else creates them, that is the real bug.
• ✓Repeat the same input after the targeted change and require the signature to disappear.
• ✓Resume from container start only after the control passes.

nvidia-container-cli device node error /dev/nvidiactl no such file or directory

Timestamps and exact values vary across runs, but the pattern. An info-level start, an early WARN, an ERROR carrying the symptom. Is the actual fingerprint you should alert on. The Denpex platform flags this combination automatically.

on the HOST, confirm the modules are loaded, lsmod | grep nvidia. If absent: modprobe nvidia nvidia_uvm nvidia_modeset. Then verify the nodes exist: ls -l /dev/nvidia*. This changes the condition in the causal diagnosis instead of hiding the outer exception. The repeated control proves ownership before recovery from container start.

# Preserve evidence before restarting
kubectl describe pod <pod>
kubectl get events --sort-by=.lastTimestamp
kubectl logs <pod> --all-containers

# Find the exact signature in the complete log
rg -n -F -- "nvidia-container-cli device node error /dev/nvidiactl no such file or directory" <log-file>

Adapt the snippet to your framework. The same pattern holds for PyTorch Lightning, Hugging Face Trainer, DeepSpeed, Megatron-LM, and vLLM training wrappers. Where the wrapper exposes a config flag (for examplelr_scheduler_type in Trainer), prefer the flag over the imperative API to keep the schedule declarative and reproducible.

Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.

• The container runtime could not find the NVIDIA device nodes on the host. The kernel modules are not loaded, or the node was created after the container runtime cached its view, the GPU is invisible inside every container on this host.
• The decisive evidence is the first log line that precedes "nvidia-container-cli device node error /dev/nvidiactl no such file or directory" and differs from a healthy run.
• A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.