Page History

...

$ python
import tensorflow as tf

tf.test.is_built_with_cuda()
True

tf.config.list_physical_devices('GPU')
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

print(tf.__version__)
2.13.1

# (OPTIONAL) Check pytorch
import torch

print(torch.__version__)  # Print PyTorch version
2.2.0

print(torch.cuda.is_available())  # Check if CUDA is available
True

print(torch.version.cuda)  # Print the CUDA version PyTorch is using
11.8

if torch.cuda.is_available():
    # Create a tensor and move it to GPU
    x = torch.tensor([1.0, 2.0]).cuda()
    print(x)  # Print the tensor to verify it's on the GPU
else:
    print("CUDA is not available. Check your PyTorch installation.")

tensor([1., 2.], device='cuda:0')

...

Using Docker

If you want to use GPUs in docker, you need to take few extra steps after creating the VM.

1. Install Docker 
   In ubuntuUbuntu:

   Code Block
   sudo apt install -y docker.io sudo usermod -aG docker $USER

   In Centos:

   Code Block
   sudo yum-config-manager \ --add-repo \ https://download.docker.com/linux/centos/docker-ce.repo sudo yum install docker-ce docker-ce-cli containerd.io sudo systemctl --now enable docker sudo usermod -aG docker $USER

   
   

2. Logout and login again

3. Install nvidia-container toolkit
   Ubuntu:

   Code Block
   distribution=$(. /etc/os-release;echo $ID$VERSION_ID) curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit sudo systemctl restart docker

   Centos:

   Code Block
   distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.repo | sudo tee /etc/yum.repos.d/nvidia-container-toolkit.repo sudo yum clean expire-cache && sudo yum install -y nvidia-docker2 sudo systemctl restart docker

   
   

4. Run GPU-compatible notebook. For example:

   Code Block
   sudo docker run --gpus all --env NVIDIA_DISABLE_REQUIRE=1 -it --rm -v $(realpath ~/notebooks):/tf/notebooks -p 8888:8888 tensorflow/tensorflow:latest-gpu-jupyter