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Introduction

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Introduction Introduction

EUMETSAT infrastructure contains H200 NVIDIA GPU cards. To employ the GPU, one need to provision one of the following flavors:

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( * ) These plans are only available upon request for a limited amount of time for justified use case requirements.

Provision

To use the GPUs:

1. Provision a new instance using the OpenStack Horizon UI
   
2. Give the instance an appropriate name 
   
3. Select the image that supports GPU, called Ubuntu 24.04 NV_GRID_Open. The size of the root volume will be set by default to 32GB, given by the selected image. However, It is highly recommended to select a larger root volume, as 32GB is not enough for most purposes, e.g. the tensorflow package alone is larger than the remaning disk space.
   
   
4. Select one the flavors with GPUs. The options for these can be found above in the Introduction
   
5. Once VM is deployed, you can verify GPUs for example using nvidia-smi program from command line (see below for confirming library installations and drivers).
   
   

Usage

Essential commands

You can see GPU information using nvidia-smi 

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# NVIDIA tools are available in /usr/local/cuda-<cuda_version>/bin/. You can add them to PATH following:
$ export PATH=$PATH:/usr/local/cuda-<cuda_version>/bin/

Installing Libraries

You can install a variety of libraries using different methods. Below, we have a basic tutorial showing you how you can install libraries such as TensorFlow, Keras and PyTorch with conda package manger.  Tensorflow library compatibility is available at: https://www.tensorflow.org/install/source#gpu.

Using conda

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# create a conda environment called ML with a spcecific Python version, e.g. 3.12
$ conda create -n ML python=3.12

# activate the environment
$ conda activate ML

# install packages, note that installing tensorflow-gpu and keras also installs many number of extra libraries such as CUDA toolkit, cuDNN (CUDA Deep Neural Network library), Numpy, Scipy, Pillow
(ML) $ conda install tensorflow-gpu keras

Using pip

# create a python environment
$ python3 -m venv .venv

# activate this environment
$ source .venv/bin/activate

# upgrade pip
(.venv) $ python3 -m pip install --upgrade pip

# install tensorflow packages, note that the GPU version of tensorflow requires the installation of the CUDA toolkit, as well as other libraries such as cuDNN (CUDA Deep Neural Network library).
(.venv) $ python3 -m pip install 'tensorflow[and-cuda]'

# install keras
(.venv) $ python3 -m pip install keras

# install pytorch
(.venv) $ python3 -m pip install torch torchvision torchaudio

Using Docker

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

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$ 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

Testing the installation

Regardless of the method of installation, to test whether the installation worked, and the GPU works as expected, here we run a few initial commands for different libraries & drivers for confirming the library integrations with the GPU.

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