Why torch.cuda.is_available() returns False, and how to fix it

On some platforms and package indexes, a bare pip install torch resolves to the CPU-only build rather than a CUDA-enabled one, particularly inside certain Docker base images, conda environments, or when a requirements.txt lacks an explicit CUDA index URL. The CPU wheel imports fine and runs fine on CPU, but torch.cuda.is_available() will always return False regardless of the hardware present.

Fix: Reinstall torch explicitly from the CUDA-specific PyTorch index for your driver's supported CUDA version, rather than a bare pip install torch with no index specified.

pip uninstall -y torch
pip install torch --index-url https://download.pytorch.org/whl/cu121

Even with a correctly installed CUDA-enabled torch wheel, if the underlying driver is missing or predates the CUDA version torch bundles, CUDA initialization fails silently in some contexts and torch.cuda.is_available() reports False rather than raising the driver-too-old error you would see with direct GPU calls.

Fix: Confirm nvidia-smi runs successfully outside Python first; if it fails, resolve the driver installation before revisiting torch, since torch cannot detect a GPU the driver itself cannot expose.

Containers do not have GPU access by default. Without launching with --gpus all (or the equivalent Kubernetes device plugin resource request) and having the NVIDIA Container Toolkit configured on the host, the container's PyTorch process runs in a GPU-invisible environment even though the host machine has a perfectly working GPU and driver.

Fix: Launch the container with --gpus all (Docker) or the appropriate GPU resource limit and runtimeClassName (Kubernetes), and confirm the NVIDIA Container Toolkit is installed and configured on the host.

docker run --gpus all -it your-image python -c "import torch; print(torch.cuda.is_available())"

Setting CUDA_VISIBLE_DEVICES to an empty string, a nonexistent GPU index, or leaving it inherited as empty from a parent process or job scheduler explicitly hides all GPUs from the process, which is indistinguishable from a real detection failure unless you check this environment variable directly.

Fix: Print and inspect the CUDA_VISIBLE_DEVICES environment variable in the exact process context that fails, and unset it or correct it to a valid GPU index list.

echo $CUDA_VISIBLE_DEVICES
unset CUDA_VISIBLE_DEVICES

A GPU experiencing Xid errors, a failed PCIe link retrain, or one that lost power or physical connection after a maintenance window can disappear from the PCI bus entirely, which no software fix in torch or the driver stack can resolve short of a physical reseat or hardware reset.

Fix: Check dmesg and nvidia-smi -q for Xid errors or a missing GPU from lspci, and if the hardware itself is the problem, escalate to a physical reseat, reboot, or hardware replacement.