Powerful GPU

For AI models requiring a GPU for execution, the GPU needs to fulfil certain criteria, i.e. be powerful enough. For the use of AI models, the recommended hardware configuration is 8GB GPU. Only Nvidia GPUs and the CPU are supported. For further reference, also refer to the requirements of the Intellesis functionality, see Remarks and Additional Information.

In ZEN, you can download AI models from arivis Cloud, e.g. for instance segmentation. These models are packaged as Docker containers and therefore need the Docker Desktop software to work. Note that Docker containers from arivis Cloud are not shared between different Windows user accounts. Each user needs to re-download a model.

  1. Docker Desktop is installed and running on your computer. For more information about Docker Desktop, see Requirements for Docker Desktop.
  2. You have the model you want to download available on arivis Cloud and your PC is connected to the internet.
  3. You have created an access token and entered it in ZEN, see Creating and Entering an Access Token.
  1. Go to the Applications, open the AI Model Store tool and click Open AI Model Store.
  2. The AI Model Store dialog opens to display all available AI models.
  3. On the left side, click Download from arivis Cloud.
  4. The model list is updated and displays all models on arivis Cloud that you have available.
  5. Select the model you want to download in the list.
  6. The Properties section on the right displays more detailed information about your selected model.
  7. In the Properties section, select the model version you want to download and click Download Model.
  8. The download of the model starts and the progress is displayed in the status bar. You have the possibility to cancel the download with the button next to the progress bar.
  1. After a successful download, the model is displayed in the AI Model Store tool. It is now available in ZEN, even without an active internet connection.
  2. You can now use the model, e.g. in the Automatic Segmentation step of the image analysis.