Introduction
EUMETSAT infrastructure contains H200 NVIDIA GPU cards. To employ the GPU, one need to provision one of the following flavors:
| Flavor name | GPU Partition Type | vCPU | RAM | vGPU Type | vGPU RAM |
|---|
| 6cpu-32gbmem-h200.1g.18gb | MIG | 6 | 32 GB | H200 | 18 GB |
| 11cpu-64gbmem-h200.2g.35gb | MIG | 11 | 64GB | H200 | 35 GB |
| 17cpu-128gbmem-h200.3g.71gb | MIG | 17 | 128 GB | H200 | 71 GB |
| 40cpu-256gbmem-h200.7g.141gb ( * ) | MIG | 40 | 256 GB | H200 | 141 GB |
| 40cpu-256gbmem-h200.pt1x ( * ) | non-MIG | 40 | 256 GB | H200 | 141 GB |
( * ) These plans are only available upon request for a limited amount of time for justified use case requirements.
Provision
To use the GPUs:
- Provision a new Ubuntu GPU instance.
Image Removedinstance using the OpenStack Horizon UI
Image Added - Give the instance an appropriate name
Image Added - Select an image for the VM that supports GPUs TODO: These are not the real images, replace when ready
Image Added - Select one the flavors with GPUs. The options for these can be found above in the Introduction
Image Added
- Once VM is deployed, you can verify GPUs for example
- Select layout ending with
eumetsat-gpu and one of the plans listed above. Beside that, configure your instance as preferred and continue deployment process.
Image Removed - 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
| Code Block |
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| language | bash |
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| title | Checking the GPU drivers |
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|
# Login to your instance and run below command
$ nvidia-smi
# Check if the input you received shows the NVIDIA-SMI, Driver and CUDA versions. You can also see the GPU hardware (e.g., RTXA6000H200X-1-6C18C) and the GPU memory
MonTue AprMay 1319 1112:4157:4945 2026
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 570580.124126.0609 Driver Version: 570580.124126.0609 CUDA Version: 1213.80 |
|+-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA RTXA6000H200X-1-48Q18C On | 00000000:00:05.0 Off | On 0 |
| N/A N/A P8P0 N/A / N/A | 1MiB / 49152MiB18432MiB | N/A 0% Default |
| | | N/A Enabled |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| MIG devices: |
+------------------+----------------------------------+-----------+-----------------------+
| GPU GI CI MIG | Shared Memory-Usage | Vol| Shared |
| ID ID Dev | Shared BAR1-Usage | SM Unc| CE ENC DEC OFA JPG |
| | | ECC| |
|==================+==================================+===========+=======================|
| 0 0 0 0 | 1MiB / 15928MiB | 16 0 | 1 0 1 0 1 |
| | 0MiB / 8192MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+ |
| Info |
|---|
As of the 13th 19th of April May 2026, GPU instances come with CUDA 1213.8 0 and NVIDIA driver 570580.124126.0609 . The instructions here have been tested with these versions, but there is no guarantee that they will all work with future versions.
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| language | bash |
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| title | Adding NVIDIA tools to path |
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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.
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| language | bash |
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| title | Library installations with conda |
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# create a conda environment called ML with a spcecific Python version, e.g. 3.812
$ conda create -n ML python=3.812
# 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
# (OPTIONAL) cudatoolkit is installed automatically while installing keras and tensorflow-gpu, but if you need a specific (or latest) version run below command.
(ML) $ conda install -c anaconda cudatoolkit |
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| language | bash |
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| title | Run tensorflow JupyterNotebooks |
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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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