ai4life

The BioImage Model Zoo is a community-driven platform that provides standardized deep learning models for bioimage analysis.

This module integrates models from the BioImage.IO package into the AI4EOSC Marketplace dashboard, specifically those using PyTorch weights and following the v0.5 format. The module allows users to seamlessly explore, deploy, and utilize these models within the AI4EOSC ecosystem, providing a user-friendly interface for advanced bioimage analysis.

Key Features

• Model Discovery: Automatically fetch and list models available in BioImage.IO that meet the criteria (PyTorch weights, v0.5 format).

• Metadata Visualization: Display essential information about each model, such as input/output specifications, authors, license details, and documentation.

• Seamless Deployment: Enable one-click deployment of models to AI4EOSC compute resources.

• Model Preview: Provide an interactive preview to test models on sample data directly in the dashboard.

Supported Models

• PyTorch Models: Only models with PyTorch weights are supported to ensure compatibility with our deployment backend.
• BioImage.IO v0.5 Specification: Models must adhere to the v0.5 specification, ensuring a standardized format for inputs, outputs, and metadata.

To launch it, first install the package then run deepaas:

Warning: If you are using a virtual environment, make sure you are working with the last version of pip before installing the package. Use pip install --upgrade pip to upgrade pip.

git clone https://github.com/ai4os/ai4os-ai4life-loader
cd ai4life
pip install -e .
deepaas-run --listen-ip 0.0.0.0

The associated Docker image(s) for this module can be found in: https://hub.docker.com/r/ai4oshub/ai4os-ai4life-loader/tags

AI4OS-AI4Life-Loader Deployment Guide

Deployment Steps

• Navigate to the AI4EOSC dashboard marketplace
• Locate and select the ai4os-ai4life-loader tool
• Click on the "Deploy" button to start the deployment proces

Configuration Form

• Select your desired model from the dropdown menu
• Complete all required fields in the deployment form
• Click on the "Deploy" button to start the deployment process

Post-Deployment

• The system will initialize your selected model
• Wait for the deployment process to complete and the status change to running
• Access your deployed model through the provided endpoint

Project structure

├── Jenkinsfile             <- Describes basic Jenkins CI/CD pipeline
├── Dockerfile              <- Steps to build a DEEPaaS API Docker image
├── LICENSE                 <- License file
├── README.md               <- The top-level README for developers using this project.
├── VERSION                 <- Version file indicating the version of the model
│
├── ai4life
│   ├── README.md           <- Instructions on how to integrate your model with DEEPaaS.
│   ├── __init__.py         <- Makes <your-model-source> a Python module
│   ├── ...                 <- Other source code files
│   └── config.py           <- Module to define CONSTANTS used across the AI-model python package
│
├── api                     <- API subpackage for the integration with DEEP API
│   ├── __init__.py         <- Makes api a Python module, includes API interface methods
│   ├── config.py           <- API module for loading configuration from environment
│   ├── responses.py        <- API module with parsers for method responses
│   ├── schemas.py          <- API module with definition of method arguments
│   └── utils.py            <- API module with utility functions
│
├── data                    <- Data subpackage for the integration with DEEP API
│
├── docs                    <- A default Sphinx project; see sphinx-doc.org for details
│
├── models                  <- Folder to store your models
│
├── notebooks               <- Jupyter notebooks. Naming convention is a number (for ordering),
│                              the creator's initials (if many user development),
│                              and a short `_` delimited description, e.g.
│                              `1.0-jqp-initial_data_exploration.ipynb`.
│
├── references              <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports                 <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures             <- Generated graphics and figures to be used in reporting
│
├── requirements-dev.txt    <- Requirements file to install development tools
├── requirements-test.txt   <- Requirements file to install testing tools
├── requirements.txt        <- Requirements file to run the API and models
│
├── pyproject.toml          <- Makes project pip installable (pip install -e .)
│
├── tests                   <- Scripts to perform code testing
│   ├── configurations      <- Folder to store the configuration files for DEEPaaS server
│   ├── conftest.py         <- Pytest configuration file (Not to be modified in principle)
│   ├── data                <- Folder to store the data for testing
│   ├── models              <- Folder to store the models for testing
│   ├── test_deepaas.py     <- Test file for DEEPaaS API server requirements (Start, etc.)
│   ├── test_metadata       <- Tests folder for model metadata requirements
│   ├── test_predictions    <- Tests folder for model predictions requirements
│   └── test_training       <- Tests folder for model training requirements
│
└── tox.ini                 <- tox file with settings for running tox; see tox.testrun.org

References:

• BioImage.Io github repository: https://github.com/bioimage-io/core-bioimage-io-python
• Documentation: https://bioimage-io.github.io/core-bioimage-io-python/bioimageio/core