torch.distributed.elastic.multiprocessing.errors.ChildFailedError exitcode -9
torchrun ChildFailedError, a worker process died and torchelastic is reporting the WRAPPER error. The real failure is in the per-rank traceback above/below this block (exitcode -9 = OOM kill, 1 = python exception, -11 = segfault). This entry explains how to confirm the cause, apply the fix, and separate it from adjacent pytorch-fsdp-ddp failures.
torch.distributed.elastic.multiprocessing.errors.ChildFailedError exitcode -9 means torchrun ChildFailedError, a worker process died and torchelastic is reporting the WRAPPER error. The real failure is in the per-rank traceback above/below this block (exitcode -9 = OOM kill, 1 = python exception, -11 = segfault). Preserve the first preceding error, then run the targeted control below.
What this failure is
The literal signature is "torch.distributed.elastic.multiprocessing.errors.ChildFailedError exitcode -9". It is a distributed training failure associated with PyTorch Distributed, FSDP, DDP, and TorchElastic. The line identifies the failing operation or subsystem, while the surrounding evidence decides whether it is the initiating fault or a downstream symptom.
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Why it happens (the mechanism)
torchrun ChildFailedError, a worker process died and torchelastic is reporting the WRAPPER error. The real failure is in the per-rank traceback above/below this block (exitcode -9 = OOM kill, 1 = python exception, -11 = segfault). 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.
What you'll observe
- The workload stops or loses forward progress after emitting "torch.distributed.elastic.multiprocessing.errors.ChildFailedError exitcode -9".
- 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.
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| torch.distributed.elastic.multiprocessing.errors.ChildFailedError exitcode -9 | torchrun ChildFailedError, a worker process died and torchelastic is reporting the WRAPPER error. The real failure is in the per-rank traceback above/below this block (exitcode -9 = OOM kill, 1 = python exception, -11 = segfault). |
| The same operation fails at a consistent stage of PyTorch Distributed, FSDP, DDP, and TorchElastic. | The decisive evidence is the first log line that precedes "torch.distributed.elastic.multiprocessing.errors.ChildFailedError exitcode -9" and differs from a healthy run. |
| The first related warning appears before the final exception and names the causal subsystem. | A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes. |
| A known-good control changes one variable and either reproduces or clears the failure. | torchrun ChildFailedError, a worker process died and torchelastic is reporting the WRAPPER error. The real failure is in the per-rank traceback above/below this block (exitcode -9 = OOM kill, 1 = python exception, -11 = segfault). |
Which systems are affected
- PyTorch Distributed, FSDP, DDP, and TorchElastic
- production-shaped multi-accelerator workloads
- containerized and bare-metal deployments of the same stack
How to confirm this is the problem
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 "torch.distributed.elastic.multiprocessing.errors.ChildFailedError exitcode -9" and preserve at least 100 lines before it.
- ✓Identify which rank, node, device, or process emitted the first related warning.
- ✓exitcode -9 → host OOM (dmesg oom-kill) not GPU; -11 → segfault (core dump); 1 → scroll for the python exception.
- ✓Repeat the same input after the targeted change and require the signature to disappear.
- ✓Resume from latest checkpoint only after the control passes.
Example training logs (fingerprint)
torch.distributed.elastic.multiprocessing.errors.ChildFailedError exitcode -9Timestamps 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.
The fix and the prevention pattern
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Why the recommended fix works
find the FIRST failing rank's own traceback (torchrun prints root-cause section with rank/exitcode/time). This changes the condition in the causal diagnosis instead of hiding the outer exception. The repeated control proves ownership before recovery from latest checkpoint.
Code examples
# 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 -- "torch.distributed.elastic.multiprocessing.errors.ChildFailedError exitcode -9" <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.
Best practices by model family
| Model / Stack | Recommendation | Notes |
|---|---|---|
| First response | Preserve the first failure | Keep the context before "torch.distributed.elastic.multiprocessing.errors.ChildFailedError exitcode -9" so aggregation does not erase causality. |
| Confirmation | Change one variable | Use a known-good node, rank, input, or configuration as the control. |
| Recovery | Resume from latest checkpoint | Resume only after the literal signature no longer appears in the same control. |
With the fix vs without the fix
| Dimension | With the fix | Without the fix |
|---|---|---|
| Evidence | First preceding error and one controlled comparison | Only the final aggregated exception |
| Fix | find the FIRST failing rank's own traceback (torchrun prints root-cause section with rank/exitcode/time). | Retrying the unchanged workload |
| Exit criterion | "torch.distributed.elastic.multiprocessing.errors.ChildFailedError exitcode -9" is absent in the repeated control | The job happened to run once |
Real engineering notes
“Treat "torch.distributed.elastic.multiprocessing.errors.ChildFailedError exitcode -9" as a search key and an investigation checkpoint, not as proof of every cause associated with the phrase. The high-value evidence is what changed immediately before it and whether the failure follows the workload, node, or configuration.”
Visual fingerprint
literal error captured
|
v
find first preceding failure
|
v
run one known-good control
|
+-- follows workload --> inspect input or configuration
+-- follows node ------> inspect hardware or platform
+-- disappears --------> validate the targeted fixDiagnose this failure in VS Code
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.
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Root cause
- torchrun ChildFailedError, a worker process died and torchelastic is reporting the WRAPPER error. The real failure is in the per-rank traceback above/below this block (exitcode -9 = OOM kill, 1 = python exception, -11 = segfault).
- The decisive evidence is the first log line that precedes "torch.distributed.elastic.multiprocessing.errors.ChildFailedError exitcode -9" and differs from a healthy run.
- A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.
The fix and how to prevent it
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Frequently asked questions
Twelve targeted questions that engineers and on-call staff most commonly ask about this failure.
What does "torch.distributed.elastic.multiprocessing.errors.ChildFailedError exitcode -9" mean?
Is this line always the root cause?
What should I collect before restarting?
What is the fastest confirmation?
How do I prevent it from recurring?
Don't just read the fix, diagnose your run
The encyclopedia tells you what went wrong. Denpex tells you what went wrong in YOUR training run. With your logs, your config, and your stack.