How to Fix PyTorch, torchvision, and torchaudio CUDA Version Mismatch Breaking Transformers Imports

Fix Transformers import errors from PyTorch, torchvision, and torchaudio CUDA version mismatch. Install matching CUDA wheels from correct index, check versions.

When a working Transformers setup suddenly starts failing after a CUDA-enabled PyTorch install, the culprit is often not in your model code at all. It’s about binary compatibility between GPU wheels. Here’s a concise walkthrough of the failure mode, why it happens, and how to get back to a clean state fast.

Reproducing the breakage

The environment was updated to a CUDA 11.8 build of PyTorch using the official wheel index. The install command looked like this:

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

After that, even a basic import in Transformers stopped working:

from transformers import pipeline

The import bubbled into a runtime error. The key line tells the whole story:

RuntimeError: Detected that PyTorch and torchvision were compiled with different CUDA major versions. PyTorch has CUDA Version=11.8 and torchvision has CUDA Version=12.1. Please reinstall the torchvision that matches your PyTorch install.

What’s actually failing and why

The failure is triggered during the import chain that starts with the Transformers pipeline. Under the hood, the import path reaches image utilities that rely on torchvision. At that point, torchvision verifies CUDA compatibility and refuses to load when its CUDA major version differs from PyTorch’s. In this case, PyTorch was built for CUDA 11.8, while torchvision was built for CUDA 12.1, which is a hard incompatibility and causes the import to fail before any model code runs.

Fix: align torchvision and torchaudio with your PyTorch CUDA build

The resolution is to install torchvision and torchaudio that match your installed PyTorch CUDA version. First remove the incompatible wheels:

pip uninstall -y torchvision torchaudio

Then install the matching CUDA 11.8 builds:

pip install torchvision==0.15.2+cu118 torchaudio==2.0.2+cu118 --index-url https://download.pytorch.org/whl/cu118

If you’re unsure which PyTorch build you have, inspect it directly:

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

And verify the trio is aligned after reinstalling:

python -c "import torch, torchvision, torchaudio; print(torch.__version__, torchvision.__version__, torchaudio.__version__)"

If you prefer a quick Python check with named references, the following does the same thing without changing behavior:

import torch as tc
import torchvision as tv
import torchaudio as ta
v_tc = tc.__version__
v_tv = tv.__version__
v_ta = ta.__version__
print(v_tc, v_tv, v_ta)

Once versions match, the import works again. For example:

from transformers import pipeline as hf_pipe

Why this matters

GPU-accelerated Python stacks mix high-level Python packages with compiled CUDA extensions. A mismatch at the CUDA major version boundary between PyTorch and torchvision is enough to break imports far from your application code. Paying attention to which wheel index you install from and ensuring all related packages are built against the same CUDA version helps you avoid hours of confusion and failed imports.

Wrap-up

If a simple Transformers import fails right after a CUDA-enabled PyTorch upgrade, read the error closely and align the ecosystem. Reinstall torchvision and torchaudio that match your PyTorch CUDA build, confirm the versions in a single check, and retry the import. Keeping torch, torchvision, and torchaudio in lockstep with the same CUDA target from the same wheel source is the quickest path back to a stable, GPU-ready environment.