Docker GPU Passthrough Error
Docker containers fail to access GPUs when nvidia-docker or GPU passthrough is not properly configured.
Docker containers fail to access GPUs when nvidia-docker or GPU passthrough is not properly configured.
What this failure is
Docker GPU Passthrough Error is a Environment failure seen during ML training runs. Docker containers fail to access GPUs when nvidia-docker or GPU passthrough is not properly configured. Common tags: Docker, Container, Gpu, Passthrough.
Is this what broke your run? Paste your log.
You're reading about Docker GPU Passthrough Error. Paste your own crash log or traceback below and get the real root cause for YOUR run, not this generic entry. No account, no card. Logs are masked at ingress and never saved to account history.
Want 14 days on the Scale plan?
Request an evaluation code. A verified workplace organization activates up to 50 diagnoses a day, alerts, history, and follow-up questions. No credit card or automatic subscription.
Why it happens (the mechanism)
NVIDIA Container Toolkit not installed. --gpus flag not passed to docker run. Nvidia-docker2 not installed. Container runtime not set to nvidia. 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
- Docker container doesn't see GPUs
- nvidia-smi inside container fails
- PyTorch reports CUDA not available inside container
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| docker: Error response from daemon: could not select device driver with capabilities: gpu | NVIDIA Container Toolkit not installed |
| nvidia-smi: command not found inside container | --gpus flag not passed to docker run |
| torch.cuda.is_available() returns False in container but True on host | nvidia-docker2 not installed |
Which systems are affected
- Docker containers for training
- Kubernetes with GPU nodes
- Dev containers on laptops with NVIDIA GPUs
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: docker: Error response from daemon: could not select device driver with capabilities: gpu
- ✓Verified signal present: nvidia-smi: command not found inside container
- ✓Verified signal present: torch.cuda.is_available() returns False in container but True on host
- ✓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
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.
Sign up free. Unlock the full analysisNo 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 extensionRelated failures to investigate next
Root cause
- NVIDIA Container Toolkit not installed
- --gpus flag not passed to docker run
- nvidia-docker2 not installed
- Container runtime not set to nvidia
The fix and how to prevent it
Evaluate Denpex on your own logs
Request a Scale evaluation code. A verified workplace organization activates 14 days with up to 50 diagnoses a day. Every account keeps its current diagnosis allowance and gets a verification path. No card or automatic subscription.
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.