CUDA Context Leak
CUDA context leaks occur when CUDA contexts are not properly destroyed, accumulating GPU memory across processes.
CUDA context leaks occur when CUDA contexts are not properly destroyed, accumulating GPU memory across processes.
What this failure is
CUDA Context Leak is a Memory failure seen during ML training runs. CUDA context leaks occur when CUDA contexts are not properly destroyed, accumulating GPU memory across processes. Common tags: Cuda Context, Memory Leak, Multiprocessing, Memory.
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Why it happens (the mechanism)
CUDA context not destroyed on process exit. Torch.cuda.empty_cache() not called. CUDA context per process accumulates. Notebook kernel restart not done. 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
- GPU memory grows across processes
- CUDA context count keeps increasing
- GPU 0 shows memory in use without processes
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| nvidia-smi shows growing memory with no process | CUDA context not destroyed on process exit |
| GPU memory not released after process exit | torch.cuda.empty_cache() not called |
| Multiprocessing CUDA contexts accumulate | CUDA context per process accumulates |
Which systems are affected
- Multi-process training
- Multiprocessing with CUDA
- Notebooks with many kernel runs
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: nvidia-smi shows growing memory with no process
- ✓Verified signal present: GPU memory not released after process exit
- ✓Verified signal present: Multiprocessing CUDA contexts accumulate
- ✓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.
Install the free VS Code extensionCUDA errors in context
CUDA reports errors asynchronously, so the traceback usually points at whatever line synchronised next rather than the one at fault. The hub covers every common CUDA error and how to make it report honestly.
Compare every cuda error side by sideRelated failures to investigate next
Root cause
- CUDA context not destroyed on process exit
- torch.cuda.empty_cache() not called
- CUDA context per process accumulates
- Notebook kernel restart not done
The fix and how to prevent it
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