AI modules metadata

All modules have comprehensive metadata to make them FAIR friendly. The metadata follows a JSON Schema defined by the AI4EOSC project and can be downloaded in several formats in the Dashboard module detail page.

Editing a module’s metadata

The module’s metadata is located in the ai4-metadata.yml file (example). This is the information that will be displayed in the Marketplace. The fields you need to edit to comply with our schemata are:

  • title (mandatory): short title,

  • summary (mandatory): one liner summary of your module,

  • description (optional): extended description of your module, like a README,

  • links (mostly optional): links to related info (training dataset, module citation. etc),

  • tags (mandatory): relevant user-defined keywords (can be empty),

  • categories, tasks, libraries, data-type (mandatory): one or several keywords, to be chosen from a closed list (can be empty).

    ㅤ 📋 Supported values

    Libraries

    Tasks

    Categories

    Data Type

    TensorFlow

    Computer Vision

    AI4 pre trained

    Image

    PyTorch

    Natural Language Processing

    AI4 trainable

    Text

    Keras

    Time Series

    AI4 inference

    Time Series

    Scikit-learn

    Recommender Systems

    AI4 tools

    Tabular

    XGBoost

    Anomaly Detection

    Graph

    LightGBM

    Regression

    Audio

    CatBoost

    Classification

    Video

    Other

    Clustering

    Other

    Dimensionality Reduction

    Generative Models

    Graph Neural Networks

    Optimization

    Reinforcement Learning

    Transfer Learning

    Uncertainty Estimation

    Other

  • inference (optional): this is is the minimum resources your module needs to run an inference correctly (eg. CPU cores, RAM, GPUs, etc). If not specified, the Dashboard will prefill with some defaults, that can later be adapted by the user during the configuration step.

  • provenance (optional): this will allow your model to have a more rich provenance information, as your model provenance graph will show the resources and the hyper-parameters you used to train. The are two subfields you can specify:

    • nomad_job: the Dashboard deployment UUID you used to train the final model,

    • mlflow_run: the MLflow run UUID you used to train the final model,

Some fields are pre-filled via the AI Modules Template and usually do not need to be modified. Check you didn’t mess up the YAML definition by running our metadata validator:

pip install ai4-metadata
ai4-metadata validate ai4-metadata.yml