User documentation

Useful links

Homepage

A high level overview of the project.

Documentation

The main source of knowledge on how to use the project. Refer always to here in case of doubt.

Authentication

The authentication management for accessing the AI4OS stack.

Dashboard

Where users will typically search for modules developed by the community, and find the relevant pointers to use them. It allows authenticated users to deploy virtual machines on specific hardware (eg. gpus) to train a module.

NextCloud

The service that allows to store your data remotely and access them from inside your deployment. (old instance)

Github (software)

The code of the software powering the platform.

Github (modules)

The code of all the modules available in the platform.

DockerHub

Where the Docker images of the modules are stored.

Harbor

Custom Docker image registry we deployed to overcome DockerHub limitations.

CI/CD pipeline

Continuous Integration and Continuous Development Jenkins instance to keep everything up-to-date with latest code changes.

Status of services

Check if a specific AI4OS service might be down for some reason.

Module template

Create new modules based on our project’s template.

MLflow server

Log your trainings parameters and models with our MLflow server.

Inference platform (OSCAR)

Scalable serverless inference of AI models.

Workflows platform (Flowfuse)

Compose custom AI workflows.

New to the project? How about a quick dive?

• 📝 Quickstart
• ⭐ New features
  • 🚀 Upcoming features

Overview

A more in depth documentation, with detailed description on the architecture and components is provided in the following sections.

• AI4OS architecture
  • The Dashboard
  • The AI modules
  • The DEEPaaS API
  • The data storage resources
  • The Inference Platform (OSCAR)
• User roles and workflows
  • The basic user
  • The intermediate user
  • The advanced user
• Authentication
  • How to create an account
• AI4OS Modules
  • CI /CD pipeline
• AI4OS Modules Template
  • Create your project based on the template
  • Project structure
• DEEPaaS API
  • Integrate your model with the API
  • Running the API
• AI4OS Dashboard
  • Selecting the modules
  • Making a deployment
  • Managing the deployments
• AI4OS Storage

How-to’s

Use a model (basic user)

• Perform inference locally
  • 1. Choose your module
  • 2. Launch the API and predict
• Perform inference with the Dashboard
  • 1. Choose your module and deploy
  • 2. Accessing the module and make predictions
• Perform inference with OSCAR
  • 1. Configuring an OSCAR service
  • 2. Invoking an OSCAR service
  • 3. More info and examples
• Create AI inference workflows with FlowFuse
  • 1. How to sign up in FlowFuse for AI4EOSC
  • 2. Joining the Development Team
  • 3. Creating our first application
  • 4. Application examples
  • 5. Importing to our instance
• Create AI inference workflows with Elyra
  • 1. Setting up the environment
  • 2. Prerequisites for all the examples
  • 3. Deploying a pipeline

Train a model (intermediate user)

• Train a model locally
  • 1. Choose a module from the Marketplace
  • 2. Prepare your dataset
  • 3. Run your module
  • 4. Open the DEEPaaS API and train the model
  • 5. Test and export the newly trained model
  • 6. Next steps
• Train a model with the Dashboard
  • 1. Choose a module from the Marketplace
  • 2. Upload your files to Nextcloud
  • 3. Deploy with the Training Dashboard
  • 4. Go to JupyterLab and mount your dataset
  • 5. Open the DEEPaaS API and train the model
  • 6. Test and export the newly trained model
  • 7. Create a Docker repo for your new module
  • 8. Share your new module in the Marketplace
  • 9. [optional] Add your new module to the original Continuous Integration pipeline
  • 10. Next steps
• Use rclone to sync your dataset
  • 1. Installing rclone
  • 2. Configuring rclone
  • 3. Using rclone
• Use MLFlow for tracking your trainings
  • 1. Register for an account in MLflow
  • 2. Login the MLflow UI
  • 3. Log your Experiments
  • 4. MLflow AutoLogging and CustomLogging

Develop a model (advanced user)

• Develop a model
  • 1. Setting the framework
  • 2. Editing <project-name> code
  • 3. Editing DEEP-OC-<project-name> code
  • 4. Integrating the module in the Marketplace

Use a tool (intermediate user)

We have specific guides for each of the tools.

• Federated server
  • Deploying a Federated server
  • Federated learning training in AI4EOSC

Others

• Frequently Asked Questions (FAQ)
  • 🔥 Service X is not working
  • 🔥 The Dashboard says I only have 500 MB of disk in my deployment
  • 🔥 I ran out of disk in my deployment
  • 🔥 I cannot access /storage
  • 🔥 rclone fails to connect
  • 🔥 My deployment does not correctly list my resources
  • 🔥 My GPU just disappeared from my deployment
  • 🔥 I delete my deployment but it keeps reappearing
  • 🚀 I would like to suggest a new feature
• Useful Machine Learning resources
  • AI4EOSC webinars
  • Tutorials
  • Datasets
  • Models
• Video demos