Introduction

When torch.cuda.is_available() returns False inside Docker, the right place to start is the runtime stack, not your training code. A working GPU container depends on four layers lining up: the host driver, the NVIDIA container runtime, the container image, and the PyTorch build installed inside that image.

Verify the Host First

Containers do not bring their own NVIDIA driver. They rely on the host machine exposing the GPU correctly. So the first check is on the host:

nvidia-smi

If this fails outside Docker, nothing inside Docker will fix it. Install or repair the host driver first.

A useful mental order is:

1. host sees the GPU
2. Docker can request GPUs
3. a minimal CUDA container sees the GPU
4. PyTorch inside your app image sees CUDA

Skipping ahead usually wastes time.

Confirm Docker GPU Passthrough

Once the host works, test Docker directly with an NVIDIA CUDA image:

docker run --rm --gpus all \
  nvidia/cuda:12.3.2-runtime-ubuntu22.04 \
  nvidia-smi

If this fails, your problem is not PyTorch. It usually means one of these:

• the NVIDIA container toolkit is missing
• Docker is too old or not configured for --gpus
• the daemon runtime integration is broken
• the host driver is incompatible with the requested container stack

Do not move on until this command succeeds.

Check the PyTorch Build in the Container

A container can see the GPU and still report CUDA unavailable if the installed PyTorch package is CPU-only. Inspect the build directly:

python -c "import torch; print(torch.__version__); print(torch.version.cuda); print(torch.cuda.is_available())"

Read the output carefully:

• if torch.version.cuda is None, you likely installed a CPU-only wheel
• if it shows a CUDA version but availability is still false, the runtime exposure is broken
• if both look correct, the next step is to try allocating a tensor on the GPU

Minimal runtime test:

python -c "import torch; x = torch.tensor([1.0], device='cuda'); print(x)"

That proves more than a boolean check.

Use a CUDA-Capable Image and Wheel

A common safe pattern is to start from an NVIDIA CUDA runtime image and then install a matching CUDA-enabled PyTorch wheel:

1FROM nvidia/cuda:12.1.1-cudnn8-runtime-ubuntu22.04
2
3RUN apt-get update && apt-get install -y python3 python3-pip
4RUN pip3 install --no-cache-dir torch torchvision torchaudio \
5    --index-url https://download.pytorch.org/whl/cu121

Then run it with GPU access:

docker build -t torch-gpu-test .
docker run --rm --gpus all torch-gpu-test \
  python3 -c "import torch; print(torch.cuda.is_available())"

The exact CUDA version in the wheel should be compatible with the host driver. It does not need to match every host package exactly, but the driver must be new enough to support the runtime you chose.

What CUDA Version: N/A Usually Means

CUDA Version: N/A is a symptom, not a root cause. It often appears when the container cannot see the GPU libraries correctly or when the reporting tool cannot determine a usable runtime. Treat it as a sign to verify each layer, not as a diagnosis by itself.

The practical checks that matter most are:

• host nvidia-smi
• Docker --gpus all with a minimal CUDA image
• PyTorch wheel includes CUDA support
• a real CUDA tensor allocation succeeds

That sequence isolates the broken layer quickly.

Common Pitfalls

• Debugging application code before confirming nvidia-smi works on the host.
• Forgetting to start the container with --gpus all.
• Installing a CPU-only PyTorch wheel in an otherwise valid GPU container.
• Assuming the container contains the NVIDIA driver instead of relying on the host driver.
• Mixing an unsupported CUDA runtime with an older host driver.

Summary

• GPU support in Docker depends on the host, runtime, image, and framework build all matching up.
• Start with nvidia-smi on the host before touching the container.
• Validate Docker GPU passthrough with a minimal NVIDIA CUDA image.
• Check whether the PyTorch wheel inside the container actually includes CUDA.
• Treat CUDA Version: N/A as a signal to debug the stack layer by layer.