How to run Tensorflow on CPU

If you don’t have a GPU or want to run Tensorflow on your CPU, you can still do so. While running Tensorflow on a GPU provides faster processing speed, running it on a CPU is still possible and can be a good option for users who do not have access to a GPU.

Steps to run Tensorflow on CPU:

1. Install Tensorflow: If you haven’t already, you can install Tensorflow on your machine using pip. You can do this by running the following command in your terminal:

pip install tensorflow

2. Ensure your Tensorflow version supports CPU: Make sure that the version of Tensorflow you have installed supports running on CPU. Most versions of Tensorflow are compatible with running on both CPU and GPU, but it’s always good to double-check.
3. Import Tensorflow and configure your code: Once you have Tensorflow installed, you can import it into your code and configure it to run on CPU. You can do this by adding the following code snippet at the beginning of your Python script:

import tensorflow as tf
tf.config.set_visible_devices(tf.config.list_physical_devices('CPU'))

4. Run your Tensorflow code: Now that you have Tensorflow set up to run on CPU, you can run your Tensorflow code as you normally would. Tensorflow will automatically use your CPU for processing.

By following these steps, you can successfully run Tensorflow on your CPU. While running on a GPU may provide faster processing speed, running on a CPU is still a viable option for users who do not have access to a GPU or want to conserve GPU resources for other tasks.