The DEEP platform is sunsetting.
The DEEP-Hybrid-DataCloud project project has ended and its plaftform and software are being decomissioned during 2023, as they have been superseeded by the AI4EOSC platform and the AI4OS software stack.
Please refer to the following links for further information:
Useful project links
A high level overview of the project.The main source of knowledge on how to use the project. Refer always to here in case of doubt.Where users will typically search for modules developed by the community, and find the relevant pointers to use them.Deploy virtual machines on specific hardware (eg. gpus) to train a module. Access is restricted to authenticated users.The authentication manager of the project, where you should register to get access to the Dashboard for example.The service that allows to store your data remotely and access them from inside your deployment.The code of all the modules and services behind the project is stored.Where the Docker images of the modules are stored.Continuous Integration and Continuous Development Jenkins instance to keep everything up-to-date with latest code changes.Check if a specific DEEP service might be down for some reason.Create new modules based on our project’s template.
New to the project? How about a quick dive?
A more in depth documentation, with detailed description on the architecture and components is provided in the following sections.
- DEEP architecture
- User roles and workflows
- DEEP Modules
- DEEP Modules Template
- DEEPaaS API
- DEEP Dashboard
Use a model (basic user)
Train a model (intermediate user)
- Train a model locally
- Train a model remotely
- 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
- Use rclone