Is this a hardware or a software issue?

Storage 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 storage 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 Kubernetes Kubelet 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 #Node Eviction. 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 Node Eviction is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. Pod is suddenly evicted with status 'Evicted' during startup or model initialization. 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.

Ephemeral Storage Exhaustion by Model Weights

Q: 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 machine learning workloads often download massive pre-trained model weights (e.g., from hugging face) into the default cache directory (like ~/.cache/huggingface) located on the container's root filesystem. this consumes the node's underlying root partition (ephemeral storage). without explicit limits, a single pod can fill the disk, triggering a kubelet eviction.. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Q: 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 machine learning workloads often download massive pre-trained model weights (e.g., from hugging face) into the default cache directory (like ~/.cache/huggingface) located on the container's root filesystem. this consumes the node's underlying root partition (ephemeral storage). without explicit limits, a single pod can fill the disk, triggering a kubelet eviction. 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.

Machine learning workloads often download massive pre-trained model weights (e.g., from Hugging Face) into the default cache directory (like ~/.cache/huggingface) located on the container's root filesystem. This consumes the node's underlying root partition (ephemeral storage). Without explicit limits, a single pod can fill the disk, triggering a kubelet eviction.

Quick answer

Machine learning workloads often download massive pre-trained model weights (e.

Root cause: Machine learning workloads often download massive pre-trained model weights (e.g., from Hugging Face) into the default cache directory (like ~/.
Recommended fix: HF_HOME=/model-cache
How Denpex helps: Denpex matches Ephemeral Storage Exhaustion by Model Weights across every rank in a distributed run and reports which rank failed first, so you act on the initiating node instead of the loudest one.

Storage#Node Eviction

What this failure is

Ephemeral Storage Exhaustion by Model Weights is a Storage failure seen during ML training runs. Machine learning workloads often download massive pre-trained model weights (e.g., from Hugging Face) into the default cache directory (like ~/.cache/huggingface) located on the container's root filesystem. This consumes the node's underlying root partition (ephemeral storage). Without explicit limits, a single pod can fill the disk, triggering a kubelet eviction. Common tags: Node Eviction.

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Why it happens (the mechanism)

Engineers often monitor GPU VRAM and system RAM, ignoring disk space. The failure happens dynamically during runtime (downloading weights) rather than at scheduling time, making it look like a crash loop rather than a resource capacity issue.

What you'll observe

The node was low on resource: ephemeral-storage.
Container <name> was using <size>, which exceeds its request of 0.

Common symptoms and what they mean

Symptom	Why it happens
Pod is suddenly evicted with status 'Evicted' during startup or model initialization.	Machine learning workloads often download massive pre-trained model weights (e.g., from Hugging Face) into the default cache directory (like ~/.cache/huggingface) located on the container's root filesystem. This consumes the node's underlying root partition (ephemeral storage). Without explicit limits, a single pod can fill the disk, triggering a kubelet eviction.
The host node transitions into a 'DiskPressure' state.	Machine learning workloads often download massive pre-trained model weights (e.g., from Hugging Face) into the default cache directory (like ~/.cache/huggingface) located on the container's root filesystem. This consumes the node's underlying root partition (ephemeral storage). Without explicit limits, a single pod can fill the disk, triggering a kubelet eviction.
Other random pods on the same node might also be evicted.	Machine learning workloads often download massive pre-trained model weights (e.g., from Hugging Face) into the default cache directory (like ~/.cache/huggingface) located on the container's root filesystem. This consumes the node's underlying root partition (ephemeral storage). Without explicit limits, a single pod can fill the disk, triggering a kubelet eviction.

Which systems are affected

Kubernetes Kubelet
Container Runtime
HuggingFace Transformers

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.

✓kubectl get events --sort-by='.metadata.creationTimestamp' | grep Evicted
✓kubectl describe node <node-name> | grep DiskPressure
✓Check pod storage usage using 'kubectl top pod --containers'

The fix and the prevention pattern

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Diagnose this failure in VS Code

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Root cause

Machine learning workloads often download massive pre-trained model weights (e.g., from Hugging Face) into the default cache directory (like ~/.cache/huggingface) located on the container's root filesystem. This consumes the node's underlying root partition (ephemeral storage). Without explicit limits, a single pod can fill the disk, triggering a kubelet eviction.

The fix and how to prevent it

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References

↗Handling Ephemeral Storage Evictions

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