Docker Image Mismatch
Docker image mismatches between dev and production cause code to behave differently due to library versions, CUDA, or system dependencies.
Docker image mismatches between dev and production cause code to behave differently due to library versions, CUDA, or system dependencies.
What this failure is
Docker Image Mismatch is a Environment failure seen during ML training runs. Docker image mismatches between dev and production cause code to behave differently due to library versions, CUDA, or system dependencies. Common tags: Docker, Container, Image, Environment.
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Why it happens (the mechanism)
Dev uses Python 3.11, container has 3.9. Container CUDA version different from dev. System libraries missing in slim images. Working directory differs between environments. File paths differ in containers vs host. 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
- Code works locally but fails in container
- Container has different library versions
- Production deployment fails despite passing tests
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Import works locally but not in container | Dev uses Python 3.11, container has 3.9 |
| CUDA version differs between images | Container CUDA version different from dev |
| Different Python versions in images | System libraries missing in slim images |
Which systems are affected
- MLOps deployment pipelines
- Multi-environment ML workflows
- Container-based training
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: Import works locally but not in container
- ✓Verified signal present: CUDA version differs between images
- ✓Verified signal present: Different Python versions in images
- ✓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
- Dev uses Python 3.11, container has 3.9
- Container CUDA version different from dev
- System libraries missing in slim images
- Working directory differs between environments
- File paths differ in containers vs host
The fix and how to prevent it
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