NeuronCompilerError graph compilation timed out HloModule optimization Trainium

The Neuron compiler exceeded its time budget compiling the graph. Trainium compiles the whole model graph ahead of execution, and compile time grows sharply with graph size and with the number of distinct shapes. This entry explains how to confirm the cause, apply the fix, and separate it from adjacent custom-silicon failures.

Symptom

NeuronCompilerError graph compilation timed out HloModule optimization Trainium

Root cause

The Neuron compiler exceeded its time budget compiling the graph. Trainium compiles the whole model graph ahead of execution, and compile time grows sharply with graph size and with the number of distinct shapes. The decisive evidence is the first log line that precedes "NeuronCompilerError graph compilation timed out HloModule optimization Trainium" and differs from a healthy run.

Recommended fix

raise the compiler timeout, and warm the persistent cache (NEURON_COMPILE_CACHE_URL) so the cost is paid once rather than on every launch.

How Denpex helps

Denpex investigates NeuronCompilerError graph compilation timed out HloModule optimization Trainium using the evidence you provide or your connected workload collects. Earlier rank, host or application evidence is needed to distinguish an initiating failure from a downstream report.

The literal signature is "NeuronCompilerError graph compilation timed out HloModule optimization Trainium". It is a hardware failure associated with Google TPU, AWS Trainium, and accelerator interconnects. The line identifies the failing operation or subsystem, while the surrounding evidence decides whether it is the initiating fault or a downstream symptom.

The Neuron compiler exceeded its time budget compiling the graph. Trainium compiles the whole model graph ahead of execution, and compile time grows sharply with graph size and with the number of distinct shapes. 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 "NeuronCompilerError graph compilation timed out HloModule optimization Trainium".
• 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.

• Google TPU, AWS Trainium, and accelerator interconnects
• production-shaped multi-accelerator workloads
• containerized and bare-metal deployments of the same stack

Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.

• ✓Find the first occurrence of "NeuronCompilerError graph compilation timed out HloModule optimization Trainium" and preserve at least 100 lines before it.
• ✓Identify which rank, node, device, or process emitted the first related warning.
• ✓the dominant cost is usually shape variety, not model size, every distinct input shape compiles separately. Pad inputs to fixed buckets and the compile count collapses.
• ✓Repeat the same input after the targeted change and require the signature to disappear.
• ✓Resume from job start, from a warmed compile cache only after the control passes.

• The Neuron compiler exceeded its time budget compiling the graph. Trainium compiles the whole model graph ahead of execution, and compile time grows sharply with graph size and with the number of distinct shapes.
• The decisive evidence is the first log line that precedes "NeuronCompilerError graph compilation timed out HloModule optimization Trainium" and differs from a healthy run.
• A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.

NeuronCompilerError graph compilation timed out HloModule optimization Trainium

Use this text as a lookup key in logs and upstream issue trackers. It is not presented as a captured customer log. Confirm the cause from your own preceding events, versions, configuration and the cited references.

raise the compiler timeout, and warm the persistent cache (NEURON_COMPILE_CACHE_URL) so the cost is paid once rather than on every launch. This changes the condition in the causal diagnosis instead of hiding the outer exception. The repeated control proves ownership before recovery from job start, from a warmed compile cache.

# Preserve evidence before restarting
rg -n -i 'error|exception|timeout|failed' <log-file>
nvidia-smi
python -m torch.utils.collect_env

# Find the exact signature in the complete log
rg -n -F -- "NeuronCompilerError graph compilation timed out HloModule optimization Trainium" <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.