If TensorFlow doesn’t detect your GPU, it will default to the CPU, which means when doing heavy training jobs, these will take a really long time to complete. This is most likely because the CUDA and CuDNN drivers are not being correctly detected in your system.

I am assuming that you have already installed Tensorflow with GPU support. If you haven’t check this article:

Installing Tensorflow 2.9 with GPU Support

To check that GPU support is enabled, run the following from a terminal:

python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

If your GPU is detected you should see something similar to this output:

[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

But if you are unlucky, then you will instead get the following output:

[]

Or you might get an obscure error like the below:

2022-05-24 20:29:24.352218: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: UNKNOWN ERROR (100)
2022-05-24 20:29:24.352261: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (c37259b3e9a1): /proc/driver/nvidia/version does not exist

In both cases, Tensorflow is not detecting your Nvidia GPU. This can be for a variety of reasons:

• Nvidia Driver not installed
• CUDA not installed, or incompatible version
• CuDNN not installed or incompatible version
• Tensorflow running on Docker but without Nvidia drivers installed in host, or Nvidia Docker not installed
• etc

Now that you are sure that Tensorflow is not detecting your GPU, it’s time to install Tensorflow correctly. Check the article below:

Installing Tensorflow 2.9 with GPU Support

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