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Easily Understand and Fix the cudaErrorNoKernelImageForDevice Error ​

If you encounter a long and confusing error message like this while running the pyVideoTrans program:

Traceback (most recent call last):
File "videotrans\task\_only_one.py", line 43, in run
File "videotrans\task\trans_create.py", line 371, in recogn
File "videotrans\recognition\__init__.py", line 236, in run
File "videotrans\recognition\_base.py", line 75, in run
File "videotrans\recognition\_overall.py", line 155, in _exec
RuntimeError: err[msg]=_process:parallel_for failed: cudaErrorNoKernelImageForDevice: no kernel image is available for execution on the device

Don't worry—this usually isn't a serious issue. The key error message is: cudaErrorNoKernelImageForDevice: no kernel image is available for execution on the device

Root Cause: The PyTorch library embedded in your current software is incompatible with your computer's graphics card environment (NVIDIA driver or CUDA version). It's like trying to use an old key with a new lock—they just don't work together.

Solution: Address the Specific Issue ​

Choose one of the following solutions based on your situation.

Case 1: If You Upgraded the Software via a "Small Patch" ​

Full pyVideoTrans Download Link: https://pyvideotrans.com/downpackage

If you upgraded from an older version below v3.76 by downloading and overwriting with a small patch (e.g., around 360 MB in size), you might be facing this issue.

  • Reason: To keep the upgrade file small, the patch may not include the latest AI computation core libraries fully compatible with newer drivers.

  • Solution: Visit the official website or download page and download the latest full installation package (approx. 2.5 GB), then perform a clean overwrite installation. The full package includes all necessary components to ensure the software works perfectly with your graphics card environment.

Case 2: If You're Using the Full Package or Encounter the Error on First Installation ​

If you're sure you're already using the latest full package but the problem persists, it's likely that your computer's graphics card environment needs an update.

Step 1: Check Your Graphics Card Environment ​

  1. Press Win + R on your keyboard to open the Run dialog.
  2. Type cmd and press Enter to open the Command Prompt.
  3. In the window, enter the following command and press Enter:
    bash
    nvidia-smi
    This command shows the highest CUDA version supported by your NVIDIA graphics driver. Look for CUDA Version in the top-right corner of the output.

  1. If you have CUDA Toolkit installed, you can also check its version with nvcc --version. However, for this issue, the information from nvidia-smi is more critical.

(Example output of nvcc --version)

Step 2: Upgrade Your Graphics Card Environment ​

If the nvidia-smi command shows your CUDA Version is below 12.0, or if nvcc --version shows a CUDA version below 12.0, you need to upgrade.

  • Solution:
    1. Update NVIDIA Graphics Driver: Go to the NVIDIA website, download and install the latest driver for your graphics card model. This is the most important step.
    2. Install Matching CUDA and cuDNN: The software relies on the correct CUDA Toolkit and cuDNN libraries to function properly.

To ensure a smooth installation, follow the step-by-step tutorial with images at the link below:

Detailed GPU Environment Installation Tutorial ​

The cudaErrorNoKernelImageForDevice error is a signal of "version mismatch."

  • First, check the software: Did you use a small patch for upgrade? If yes, switch to the full package.
  • Then, check your computer: Is the software fine? Then upgrade your graphics driver and related components.