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How to Fix the Tensorflow ImportError: libcublas.so.8.0 Error

In the realm of data science or software engineering utilizing Tensorflow, encountering the ImportError: libcublas.so.8.0 Error message during code execution is not uncommon. This error arises when Tensorflow is unable to locate the libcublas.so.8.0 shared library file. This post aims to elucidate the origins of this error and furnish comprehensive, step-by-step instructions for resolving it.
By Saturn Cloud | | Miscellaneous

As a data scientist or software engineer working with Tensorflow, you may encounter the following error message when trying to run your code:

ImportError: libcublas.so.8.0: cannot open shared object file: No such file or directory

This error occurs when Tensorflow cannot find the libcublas.so.8.0 shared library file, which is required for Tensorflow to run on a GPU. In this post, we will explain what causes this error and provide step-by-step instructions on how to fix it.

Table of Contents

  1. What Causes the ImportError: libcublas.so.8.0 Error?
  2. How to Fix the ImportError: libcublas.so.8.0 Error
  3. Conclusion

What Causes the ImportError: libcublas.so.8.0 Error?

The libcublas.so.8.0 shared library file is part of the NVIDIA CUDA toolkit, which provides an API for accelerating scientific and engineering applications on NVIDIA GPUs. Tensorflow uses CUDA to accelerate computations on a GPU, and therefore requires the CUDA toolkit to be installed on your system.

The ImportError: libcublas.so.8.0 error occurs when Tensorflow cannot find the libcublas.so.8.0 shared library file, which is part of the CUDA toolkit. This can happen if the CUDA toolkit is not installed on your system, or if Tensorflow is not configured to use the correct version of the CUDA toolkit.

How to Fix the ImportError: libcublas.so.8.0 Error

To fix the ImportError: libcublas.so.8.0 error, you need to install the CUDA toolkit and configure Tensorflow to use the correct version of the CUDA toolkit. Follow the steps below to do this.

Step 1: Check Your GPU Compatibility

Before installing the CUDA toolkit, you need to make sure that your GPU is compatible with the version of the CUDA toolkit you plan to install. You can check the compatibility of your GPU with the CUDA toolkit on the NVIDIA website.

Step 2: Install the CUDA Toolkit

To install the CUDA toolkit, follow these steps:

  1. Go to the NVIDIA CUDA toolkit downloads page and download the version of the CUDA toolkit that is compatible with your GPU and operating system.
  2. Follow the installation instructions provided by NVIDIA to install the CUDA toolkit on your system.

Recommended CUDA Toolkit Versions:

  • TensorFlow 2.0: CUDA Toolkit 10.0
  • TensorFlow 2.1: CUDA Toolkit 10.1
  • TensorFlow 2.2: CUDA Toolkit 10.1
  • TensorFlow 2.3: CUDA Toolkit 10.1
  • TensorFlow 2.4: CUDA Toolkit 11.0

Step 3: Verify cuBLAS Installation:

Confirm that the cuBLAS library is installed and aligned with TensorFlow requirements. To install cuBLAS:

sudo apt-get install libcublas-dev

Step 4: Set Environment Variables

After installing the CUDA toolkit, you need to set the LD_LIBRARY_PATH environment variable to the directory where the CUDA toolkit is installed. This tells Tensorflow where to find the libcublas.so.8.0 shared library file.

To set the LD_LIBRARY_PATH environment variable, follow these steps:

  1. Open your terminal and type the following command:
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:$LD_LIBRARY_PATH

Note that the path /usr/local/cuda-8.0/lib64 may be different depending on the version of the CUDA toolkit you installed.

  1. Run your Tensorflow code again. The ImportError: libcublas.so.8.0 error should now be resolved.

Step 5: Verify Your Tensorflow Installation

To verify that your Tensorflow installation is now working correctly, you can run the following code:

import tensorflow as tf
sess = tf.Session()

If Tensorflow is installed correctly, this code should create a new Tensorflow session without any errors.

If those steps don’t solve the issue, consider rebuild TensorFlow:

Commands:

git clone https://github.com/tensorflow/tensorflow.git
cd tensorflow
./configure
bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
pip install /path/to/tensorflow_pkg.whl

Make sure Bazel has been installed. More information can be found here.

Conclusion

If you encounter the ImportError: libcublas.so.8.0 error when working with Tensorflow, it means that Tensorflow cannot find the libcublas.so.8.0 shared library file, which is part of the NVIDIA CUDA toolkit. To fix this error, you need to install the CUDA toolkit and configure Tensorflow to use the correct version of the CUDA toolkit. Follow the steps outlined in this post to fix the ImportError: libcublas.so.8.0 error and get your Tensorflow code running smoothly on your GPU.

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