tf.test.is_gpu_available() returns False on GCP

Question

I am training a CNN on GCP's notebook using a Tesla V100. I've trained a simple yolo on my own custom data and it was pretty fast but not very accurate. So, I decided to write my own code from scratch to solve the specific aspects of the problem that I want to tackle.

I have tried to run my code on Google Colab prior to GCP, and it went well. Tensorflow detects the GPU and is able to use it whether it was a Tesla K80 or T4.

from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
tf.test.is_gpu_available()  #>>> True

My problem is that, this same function returns a False on GCP notebook, as if Tensorflow is unable to use the GPU it detected on GCP VM. I don't know of any command that forces Tensorflow to use the GPU over CPU, since it does that automatically. I have already tried to install or uninstall and then install some versions of tensorflow, tensorflow-gpu and tf-nightly-gpu (1.13 and 2.0dev for instance) but it yielded nothing.

output of nvidia-smi

Answer 1

Have you tried using GCP's AI Platform Notebooks instead? They offer VMs that are pre-configured with Tensorflow and have all required GPU drivers installed.