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The Could not load dynamic library ‘cudart64_101.dll’ on tensorflow CPU-only installation error occurs when TensorFlow cannot load the CUDA runtime libraries required for GPU acceleration. These libraries are typically installed with NVIDIA’s CUDA toolkit.
This error usually occurs when TensorFlow is installed without the appropriate CUDA and cuDNN libraries or when these libraries are not correctly configured.
However, we tried to install a TensorFlow CPU-only package, and, strangely, TensorFlow is trying to use CUDA. It’s possible that the GPU version of TensorFlow was installed by mistake.
Solution 1: CUDA_VISIBLE_DEVICES = -1
To fix the “Could not load dynamic library ‘cudart64_101.dll’ on tensorflow CPU-only installation” error, “tell TensorFlow not to look for the CUDA libraries.”
By setting the CUDA_VISIBLE_DEVICES environment variable to -1, you instruct TensorFlow to avoid using a GPU, even if one is available. This effectively forces TensorFlow to run on a CPU only.
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
import tensorflow as tf
When imported, TensorFlow checks the CUDA_VISIBLE_DEVICES environment variable to determine which GPUs to use. If this variable is set to -1, TensorFlow will not use GPUs.
This is a good workaround if you want to use a TensorFlow installation that supports GPUs but wants to run a particular script on the CPU for some reason.
Remember to set this environment variable at the start of your script before you import TensorFlow. If you set it after importing TensorFlow, it will not have any effect.
Solution 2: Uninstall the GPU version of TensorFlow and install the CPU version
If you don’t have a compatible NVIDIA GPU or simply don’t want to use GPU acceleration, you should install the CPU version of TensorFlow. You can do this by uninstalling the current version of TensorFlow and then reinstalling it with pip, specifying the CPU version.
Here is how you can install the CPU version of TensorFlow:
pip uninstall tensorflow
pip install tensorflow-cpu
Solution 3: Using the tf.config module
Another solution is to avoid using a GPU with TensorFlow if it’s already installed. This involves using the “tf.config” module to set up the CPU as the only physical device for TensorFlow.
import tensorflow as tf
# Set up a list of CPUs for TensorFlow to use
cpus = tf.config.list_physical_devices('CPU')
# Set the visible devices to be the CPUs
tf.config.set_visible_devices(cpus, 'CPU')
In this script, tf.config.list_physical_devices(‘CPU’) gets all the CPUs that TensorFlow can use and tf.config.set_visible_devices(cpus, ‘CPU’) sets the visible devices to be these CPUs.
I hope these solutions will help you fix the error!
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Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.