Python Out of Memory
Python OOM kills the training process when Python's heap memory is exhausted, even if GPU memory is fine.
Python OOM kills the training process when Python's heap memory is exhausted, even if GPU memory is fine.
What this failure is
Python Out of Memory is a Memory failure seen during ML training runs. Python OOM kills the training process when Python's heap memory is exhausted, even if GPU memory is fine. Common tags: Python, Oom, Memory, Heap.
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Why it happens (the mechanism)
Accumulating large Python objects (lists, dicts) in memory. Large dataset cached in Python heap. Memory leak in custom code. Too many parallel Python processes. 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 MemoryError
- Python process is killed by OOM killer
- Swap usage reaches maximum
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| MemoryError: out of memory | Accumulating large Python objects (lists, dicts) in memory |
| OSError: [Errno 12] Cannot allocate memory | Large dataset cached in Python heap |
| Process killed by OOM killer in dmesg | Memory leak in custom code |
Which systems are affected
- Large dataset preprocessing
- Accumulating metrics in Python lists
- Loading entire dataset into memory
- Custom training loops with many Python objects
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: MemoryError: out of memory
- ✓Verified signal present: OSError: [Errno 12] Cannot allocate memory
- ✓Verified signal present: Process killed by OOM killer in dmesg
- ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.
The fix and the prevention pattern
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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.
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Root cause
- Accumulating large Python objects (lists, dicts) in memory
- Large dataset cached in Python heap
- Memory leak in custom code
- Too many parallel Python processes
The fix and how to prevent it
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