Fixed runtimeerror: cuda error: no kernel image is available for execution on the device

When we are using CUDA-enabled libraries like PyTorch or TensorFlow with GPU acceleration at that time we may get the error RuntimeError: CUDA error: no kernel image is available for execution on the device as our code is unable to run on the GPU due to compatibility issues.

The error tells us that the CUDA runtime was unable to find an appropriate kernel for the hardware in use. This happens due to a mismatch between the version of CUDA and the version of our GPU drivers or the architecture of the GPU itself.

Common Causes and Solutions for No Kernel Image Available

Let’s go through some of the most common reasons why this error might occur along with potential solutions to solve the issue.

Incompatible CUDA Version

We have different versions of CUDA and each of them corresponds to different versions of GPU architectures. If the version of CUDA installed on our system does not support the compute capability of your GPU, we might encounter this error.

Solution:

Check the version of CUDA installed on your system by using the following command.

nvcc --version

Next, we should check the compute capability of your GPU. We can find the compute capability of your NVIDIA GPU model on the CUDA GPUs page. For example a Tesla V100 has a compute capability of 7.0, while a GeForce GTX 1080 has a compute capability of 6.1.

If we have an incompatible version of CUDA with our GPU then we have to need to either:

  • Update CUDA to a version that supports our GPU’s architecture.
  • Downgrade CUDA to match the compute capability of our device.

To install a specific CUDA version compatible with your GPU, you can use pip:

PyTorch or TensorFlow CUDA Version Mismatch

When we have libraries like PyTorch or TensorFlow installed. There are chances the library might not be compiled with the appropriate CUDA version for our system.

Solution:

One possible solution is to check the installed versions of PyTorch or TensorFlow and their respective CUDA versions:

For PyTorch: 
import torch
print(torch.__version__)
print(torch.cuda.is_available())
print(torch.version.cuda)
For TensorFlow: 
import tensorflow as tf
print(tf.__version__)
print(tf.test.is_gpu_available())

If we have a mismatch then reinstall the library with the appropriate CUDA version. For example,

with PyTorch: 
 pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
with TensorFlow: 
pip install tensorflow-gpu==2.x
Above are just examples.

We need to make sure that the installed library is compatible with both your CUDA version and GPU architecture.

Outdated GPU Driver

If we have an outdated or incompatible GPU driver that can also cause CUDA errors. If the GPU driver does not support the installed version of CUDA, it can lead to this error.

Solution:

Check the current version of your GPU driver:

On Linux system run this command :

nvidia-smi

On Windows, we can just use the NVIDIA Control Panel or Device Manager.

We need to update our GPU driver to the latest version compatible with our CUDA installation. We can always download the latest drivers from the NVIDIA website.

Remember after downloading the driver, we need to restart the system

Incompatible GPU Architecture

If CUDA code is compiled for a specific GPU architecture might not work on older or different GPUs.This error will appear if the software is compiled for a GPU architecture that our hardware does not support.

Solution:

We have to make sure that our environment and code is correctly configured to support the compute capability of your GPU. The GPU architecture is defined by the compute capability (e.g., 6.1, 7.0) mentioned earlier in the blog.

GPU Availability

In our system, if we have multiple GPUs running with no available GPU, our code might fall back to CPU execution. Making CUDA unable to find a suitable GPU.

Solution:

We need to confirm that CUDA can detect your GPU:

nvidia-smi

The above code shows you the status of your GPU. If no GPU is detected, there might be an issue with your hardware, drivers, or how CUDA is configured.

We also have to check in Python whe check if PyTorch or TensorFlow is correctly detecting your GPU:

For PyTorch, run these commands and check it.

import torch
print(torch.cuda.is_available())

For TensorFlow, run these commands and check it.

import tensorflow as tf
print(tf.test.is_gpu_available())

If any of these functions return False means CUDA was not set up correctly. Make sure your CUDA installation is correctly set up and that your GPU is correctly configured.

Mixed CPU and GPU Code

In some cases, if you are mixing CPU and GPU operations in your code, you may run into this error if certain operations are incompatible with the GPU or if they’re not explicitly moved to the correct device.

Solution:

Ensure that tensors and operations are correctly placed on the GPU.

In PyTorch, for example: 
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tensor = tensor.to(device)

In TensorFlow,

you can use with 
tf.device('/GPU:0')
to specify that operations should be executed on the GPU.

Checklist to solve RuntimeError: CUDA error: no kernel image is available for execution on the device.

  • Check if we have an Incompatible
    CUDA Version with GPU architectures
  • Check if we have a PyTorch or TensorFlow CUDA version mismatch.
  • Outdated GPU drivers
  • Make sure GPU is available
  • Avoid mixed CPU and GPU code.

Conclusion

The “RuntimeError: CUDA error: no kernel image is available for execution on the device” is a compatibility issue between your GPU and CUDA versions. The error may also relate to the deep learning library we’re using.

By checking compatibility between our CUDA version, GPU drivers, and PyTorch/TensorFlow installation, we can resolve most issues. If the problem still exists then we have to check whether hardware is supported and that you’re using the correct environment for your GPU’s architecture.

That’s all.

You can also read about our other blogs on how to solve ValueError: can only compare identically-labeled series objects in Pandas and how to solve notimplementederror: loading a dataset cached in a localfilesystem is not supported.

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