Page History

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• At SkyHiGh (IIK's production instance):
  • General purpose flavors, all IIK affiliates are eligible:
    
    • dx5.8c90r.v100-8g: A flavor with 90GB RAM, 8 vCPU's and a 1/4 of a Tesla v100 (8GB GPU-RAM).
    • de3.12c60r.a100-10g: A flavor with 60GB RAM, 12 vCPUs and 1/4 of a Tesla A100 (10GB GPU-RAM)
      
      
  • Flavors only available for SFI NORCICS:
    • de3.24c120r.a100-20g: A flavor with 120GB RAM, 24 vCPUs and 1/2 of a Tesla A100 (20GB GPU-RAM)
    • Only available for SFI NORCICS
    • de3.48c240r.a100-40g: A flavor with 240GB RAM, 48 vCPUS and 1/1 of a Tesla A100 (40GB GPU-RAM)
      Only
      
  • Flavors only available for SFI NORCICSNorwegian Biomtetrics Lab:
    • dx4
    dx5
    • .24c60r.p40-24g: A flavor with 60GB RAM, 24 vCPUs and 1/1 of a Tesla P40 (24GB GPU-RAM)
      
    • Only available for Norwegian Biometrics Lab
    • de2.24c240r.a100-20g: A flavor with 240GB RAM, 24 vCPU's and 1/2 of a Tesla A100 (20GB GPU-RAM)
    • Only available for Norwegian Biometrics Lab
      
    • de3.24c120r.a100d-20g: A flavor with 120GB RAM, 24 vCPU's and 1/4 of a Tesla A100 80GB (20GB GPU-RAM)
      
    • Only available for Norwegian Biometrics lab
    • 
      
• At SkyLow (IIK's development instance):
  • dx4.8c20r.m10-8G: A flavor with 20GB RAM, 8 vCPU's and one core of a Tesla M10 card (8GB GPU-RAM).
  • dx4.24c110r.p100: A flavor with 110GB RAM, 24 vCPU's and a Tesla p100 card (16GB GPU-RAM)
  • dx4.48c220r.2p100: A flavor with 220GB RAM, 48 vCPU's and two Tesla p100 cards (2*16GB GPU-RAM)
    
    
• At stackit (NTNU IT's production platform):
  • dx4.28c120r.a100-20g: A flavor with 120GB RAM, 28vCPU's and 1/2 of a Tesla a100 (20GB GPU-RAM)
  • dx5s.96c470r.a100d-80g.e3400g: A flavor with 470GB RAM, 96 vCPUs and a Tesla a100d (80 GB GPU-RAM) and 3.4TiB with compute-local flash storage.
    • Only available for an IV-EPT project.

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# Enable the repositories
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

# Install the package
sudo apt update && sudo apt -y install nvidia-docker2

# Restart the docker daemon
sudo systemctl restart docker

# Run a test to verifiy that it works
sudo docker run --rm --gpus all nvidia/cuda:12.0.1-base-ubuntu22.04 nvidia-smi

# Optionally run a test with Tensorflow that actually runs a bit of code on the GPU via docker
sudo docker run --gpus all -it --rm tensorflow/tensorflow:latest2.14.0-gpu \
   python -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

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