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  • User documentation
    • Quickstart Guide
      • Run a module locally
        • Run the container
        • Access the module via API
      • Train a module on DEEP Pilot Infrastructure
      • Develop and share your own module
    • Overview
      • DEEP architecture
        • The marketplace
        • DEEP as a Service
        • The DEEPaaS API
        • The data storage resources
        • The dashboards
      • User roles and workflows
        • The basic user
        • The intermediate user
        • The advanced user
      • Data Science template
        • <your_project> repo
        • <DEEP-OC-your_project>
        • Step-by-step guide
      • DEEPaaS API
        • Integrate your model with the API
        • Running the API
      • DEEP Dashboard
        • Selecting the modules
        • Making a deployment
        • Managing the deployments
    • HowTo’s
      • Develop a model
        • 1. Prepare DEEP Data Science environment
        • 2. Improve the initial code of the model
        • 3. Connect with a remote storage
        • 4. Create a python installable package
        • 5. Create a docker container for your model
      • Train a model locally
        • 1. Choose your module
        • 2. Store your data
        • 3. Train the model
      • Train a model remotely
        • 1. Choose a model
        • 2. Upload your files to Nextcloud
        • 3. Deploy with the Training Dashboard
        • 4. Go to the API, train the model
        • 5. Testing the training
      • Perform inference locally
        • 1. Choose your module
        • 2. Launch the API and predict
      • Perform inference remotely
      • Add module to the DEEP marketplace
        • Creating the Github repositories
        • Making the Pull Request (PR)
      • Use rclone
        • Installation of rclone in Docker image
        • Nextcloud configuration for rclone
        • Creating rclone.conf for your local host
        • Example code on usage of rclone from python
      • Install and configure oidc-agent
      • Deploy with CLI via Orchent
        • Prepare your TOSCA file (optional)
        • Orchent submission script
        • Submit your deployment
      • Video demos
    • Modules
  • Technical documentation
    • Mesos
      • Introduction
      • Testbed Setup
        • Nodes characteristics
        • Tested Components Versions
      • Prepare the agent (slave) node
        • Verify the nvidia-driver installation
        • Mesos slave configuration
        • Testing GPU support in Mesos
      • Testing Chronos patch for GPU support
        • Patch compilation
        • Testing
      • Testing GPU support in Marathon
      •  Running tensorflow docker container
      • References
      • Enabling open-id connect authentication
    • Kubernetes
      • DEEP : Installing and testing GPU Node in Kubernetes - CentOS7
        • Introduction
        • Cluster Status
        • Tests
        • Access PODs from outside the cluster
        • References
      • Installing GPU node and adding it to Kubernetes cluster
        • Step-by-step guide
    • OpenStack nova-lxd
      • OpenStack nova-lxd installation via Ansible
        • Comparison between Openstack Ansible and Juju/conjure-up
        • Installing a All-in-One Openstack site with nova-lxd via Openstack Ansible
        • Notes:
        • References
      • Deploying OpenStack environment with nova-lxd via DevStack
        • Installation steps
        • Handy commands:
        • Notes:
        • References
      • Installing nova-lxd with Juju
        • Installation
        • Notes
      • OpenStack nova-lxd testing configuration
        • Testing of nova-lxd with different software configurations
        • Working configuration
    • uDocker
      • uDocker new GPU implementation
        • Test and evaluation of new implementation
        • References
    • Miscelaneous
      • GPU sharing with MPS
        • How to use MPS service
        • Testing environment
        • Test 1. Test with CUDA native sample nbody, without nvidia-cuda-mps service
        • Test 2. Test with CUDA native sample nbody, with nvidia-cuda-mps service
        • Test 3. Test with Docker using mariojmdavid/tensorflow-1.5.0-gpu image, without nvidia-cuda-mps service
        • Test 4. Test with Docker using mariojmdavid/tensorflow-1.5.0-gpu image, with nvidia-cuda-mps service
        • Test 5. Test with Docker using vykozlov/tf-benchmarks:181004-tf180-gpu image, without and with nvidia-cuda-mps service
        • Identified reasons why Tensoflow does not work correctly with MPS
        • Final remarks:
        • References
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