For those doing AI/ML on the European Weather Cloud, we have published a number of templates to help you set up the necessary software stacks to run your favourite workloads. You may apply those onto an existing instance, and combine them as needed. This is part of the EWC Automation templates.

The stacks currently part of this collection are:

ML Basic: provides a conda environment with the basic AI/ML tools in python such as torch, tensorflow, keras, scikit-learn, and others.
AI Models: sets up a conda environment with the AI-models package, which allows you to run popular data-driven weather forecasting models such as panguweather or graphcast.
Anemoi: installs a conda environment featuring all the Anemoi components. It includes the basic packages such as datasets, training, graphs, models or inference.
AIFS Single MSE: installs the ECMWF AIFS Single MSE Data-Driven Forecasting system and supporting dependencies.
AIFS ENS CRPS: installs the ECMWF AIFS ENS CRPS Data-Driven Forecasting system and supporting dependencies.

These stacks are based on Ansible playbooks and can be found in https://github.com/ewcloud/ewc-ecmwf-ai-stacks.

AI/ML software stacks usually require a significant amount of disk space. Make sure your instances have enough free space before applying them. Although it is possible that less space is required based on the specific setup and combinations, we recommend at least 15 GB for each of those you may want to apply. You may extend your instance volume volume or add a new one where to install those if needed. If a new volume is added, make sure

How to apply an AI stack to an existing instance via Morpheus

If using ECMWF's Morpheus portal, you may find those are offered as Morpheus Workflows by default:

European Weather Cloud Knowledge Base > EWC ECMWF AI Stacks > ecmwf-ai-stacks_workflows.png

If you cannot see them, you may also add them to your tenancy by creating a new Git integration of the https://github.com/ewcloud/ewc-ecmwf-ai-stacks as described in the Morpheus documentation, and then creating the Automation Tasks and Workflows required using that git integration, which is described here.

Go to Provisioning - Instances, and select the desired instance to apply the stack. If you don't have one, create one first.
Click Actions - Run Workflow and type the name of the desired one. Some stacks may offer the possibility of customising certain parameters, such as the location of the conda installation. You may tune them if needed, for example if you are using an additional volume with more capacity for your software stack. You can also configure the ansible command options (for example, passing a -v for a more verbose output).
Click Execute
You may follow the progress of the workflow execution under History.

How to apply an AI stack to an existing instance via Ansible

You can also install those directly with Ansible, for example as part of your existing Infrastructure as Code (IaC) or CI/CD pipelines.

Advanced: How to use your custom Ansible playbook using provided Ansible roles