What does this failure look like in production?

In production this failure typically appears during the first tens-to-hundreds of training steps: loss curves become non-monotonic, validation metrics plateau above the baseline, and downstream checkpoints either diverge or fail to improve. Engineers frequently misread it as data drift when the underlying mechanism is container memory limit too low for training workload. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Is this a hardware or a software issue?

Reliability failures can look like either layer depending on the symptom. The discriminator is the resolution of the symptom to the recommended fix: if applying the steps under "How to fix it" eliminates the symptom, this is a software/configuration class issue regardless of the hardware it surfaced on. Repeat the fix on a fresh node to confirm.

How long does the fix typically take?

Most resolutions from this entry take 10 to 30 minutes of engineer time once the diagnosis is confirmed. Long fixes come from skipping the diagnosis step and chasing apparent symptoms (validation drift, memory growth, slow throughput) instead of the actual root cause listed above. Apply the fix in a small experiment first, then roll it forward.

Does this affect inference or only training?

Primarily this is a reliability failure observed during training, but the same misconfiguration can leak into downstream evaluation and serving. Treat the prevention checklist as a guardrail: any pipeline that touches a trained checkpoint should re-validate the failure mode before promoting the model.

What's the cheapest way to confirm this is the problem?

Cheapest signal: reproduce the symptom on a 1-GPU toy run with the same configuration. If the toy run exhibits the same behavior, the cause is the configuration rather than the cluster. If only the multi-GPU run is affected, the failure is a distributed interaction, so Docker container training is the most likely layer to inspect.

Can the Denpex platform detect this automatically?

Yes. Denpex correlates the symptoms in this entry against your training logs in real time and surfaces the failure taxonomy, root cause, and exact remediation command on the console. Three full diagnoses are included free during signup, and the encyclopedia entry you are reading is generated from the same signal library that powers the platform.

What related failures should I check next?

Closely related failures cluster around #cgroup, #memory, #container. After applying this fix, validate against the related entries below to confirm no overlapping cause is masking as this one. Engineers routinely discover that the dominant failure mode is actually a second, sibling failure compounded on top.

Will this recur after the fix?

Applied once with the prevention checklist, this failure does not recur under normal operation. The recurring variant most engineers encounter is the re-emergence of the same symptom on a different model or stack after cgroup is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

How does this scale from 1 GPU to 1000 GPUs?

At single-GPU scale the symptom usually looks like slow convergence or a single anomalous loss value. At 64+ GPU scale, distributed interactions amplify container memory limit too low for training workload through collectives, and the failure can manifest as a full job abort. Always reproduce the fix at the smallest cluster configuration that still exhibits the symptom before promoting to production scale.

Is there a fingerprint I can monitor?

Yes. Dmesg: memory cgroup out of memory: Killed process is the highest-signal fingerprint. Wire a metric or alert on that signal (or its absence after the fix) so you can detect the recurrence automatically. The Denpex platform encodes this exact fingerprint and surfaces it directly in the dashboard.

What is the official documentation that covers this?

Reference documentation is listed in the References section at the bottom of this entry. The most authoritative URLs are the upstream framework docs and the issue trackers cited; the Denpex entry synthesizes both into the executive, engineer, and on-call perspectives so each audience can act without reading every linked source.

Does this entry include working code?

Yes. The "Code Examples" section contains copy-paste-ready snippets that target the most common stacks (PyTorch, Hugging Face Trainer, DeepSpeed, Megatron-LM, vLLM/Transformers). Each snippet is annotated with why it works so you can adapt the pattern to your own framework without losing fidelity.

Cgroup Memory Limit

Cgroup memory limits cause OOM kills when training exceeds the container or pod's memory limit.

Quick answer

Cgroup memory limits cause OOM kills when training exceeds the container or pod's memory limit.

Symptom: Container exits with 137 (SIGKILL)
Root cause: Container memory limit too low for training workload. Memory leak in training process. Other processes in container consuming memory.
Recommended fix: Increase container memory limit
How Denpex helps: Denpex investigates Cgroup Memory Limit 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.

Reliability#cgroup#memory#container#docker#kubernetes#oom

What this failure is

Cgroup Memory Limit is a Reliability failure seen during ML training runs. Cgroup memory limits cause OOM kills when training exceeds the container or pod's memory limit. Common tags: Cgroup, Memory, Container, Docker.

Live diagnosis, no signup

Is this what broke your run? Paste your log.

You're reading about Cgroup Memory Limit. Paste your own traceback and relevant evidence for an investigation of your workload, with a next action or a specific missing fact. A reference entry does not establish your cause. No account or card for the free diagnosis. Review data handling before submitting sensitive logs.

Before uploading, review cloud data handling and local options.

training_logs.txt

No log to hand? Try one:

Upload or drop a plain-text log

Upload your log file. Large files are processed in your browser. Only selected evidence is submitted when you run diagnosis.

3 free diagnoses/day

Why it happens (the mechanism)

Container memory limit too low for training workload. Memory leak in training process. Other processes in container consuming memory. GPU memory pinned memory reducing available system memory. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.

What you'll observe

Training crashes with OOM kill
Process is killed without error
Container exits with 137 (SIGKILL)

Common symptoms and what they mean

Symptom	Why it happens
dmesg: memory cgroup out of memory: Killed process	Container memory limit too low for training workload
Process killed with SIGKILL (exit code 137)	Memory leak in training process
kubelet: Out of memory killing container	Other processes in container consuming memory

Which systems are affected

Docker container training
Kubernetes pod training
SLURM jobs with memory limits

How to confirm this is the problem

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.

✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
✓Verified signal present: dmesg: memory cgroup out of memory: Killed process
✓Verified signal present: Process killed with SIGKILL (exit code 137)
✓Verified signal present: kubelet: Out of memory killing container
✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.

Root cause

Container memory limit too low for training workload
Memory leak in training process
Other processes in container consuming memory
GPU memory pinned memory reducing available system memory

The fix and how to prevent it

Searchable error signature

search key

Container exits with 137 (SIGKILL)
dmesg: memory cgroup out of memory: Killed process

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.

The fix and the prevention pattern

The root cause is on this page and stays free. A free account adds the exact remediation steps, saved history, and the fix on every entry in the encyclopedia.

No credit card. Daily allowance follows verified trust tier. Instant access.

Diagnose 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.

Install the free VS Code extension

Related failures to investigate next

Bytedance Sequence Length Straggler often co-occurs with this failure because both share underlying assumptions about reliability; if you fix this entry but still see drift, validate the related primary cause next. bytedance sequence length straggler →

Training Checkpoint Corruption On Write often co-occurs with this failure because both share underlying assumptions about reliability; if you fix this entry but still see drift, validate the related neighboring cause next. training checkpoint corruption on write →

Wandb Tensorboard Failure often co-occurs with this failure because both share underlying assumptions about reliability; if you fix this entry but still see drift, validate the related neighboring cause next. wandb tensorboard failure →

References

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.

Diagnose your logs free Compare a solved incident Browse all failures

Related Reliability errors

GB200 NVL72 driver hang with Xid 150 and 154 after long uptime is a firmware bug, not a dead GPU

Reliability · high

Sequence Length Imbalance Causing Distributed Training Stragglers

Reliability · high

Silent Data Corruption from GPU Hardware Faults Causing Loss Spikes and Model Divergence

Reliability · critical

NIXL Firmware Page Registration Fan-Out Triggers Host OOM Kills on HGX H200 and B200

Reliability · critical