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$ 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') |
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Using Docker
If you want to use GPUs in docker, you need to take few extra steps after creating the VM.
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 $USER
- Logout and login again
Install nvidia-container toolkit
Ubuntu:Code Block distribution=$(. /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
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