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  • User documentation
    • Quickstart Guide
      • Download module from the marketplace
      • Run a module locally
        • Docker Hub way (easy)
        • Github way (pro)
      • Run a module on DEEP Pilot Infrastructure
      • Integrate your model with the API
    • Overview
      • Architecture overview
        • The marketplace
        • The API
        • The storage resources
      • Our different user roles
        • The basic user
        • The intermediate user
        • The advanced user
      • DEEP Data Science template
        • your_project repo
        • DEEP-OC-your_project
      • DEEPaaS API
        • Integrate your model with the API
        • Methods
    • HowTo’s
      • Develop a model
        • 1. Prepare DEEP DS environment
        • 2. Improve the initial code of the model
      • Train a model locally
        • 1. Get Docker
        • 1. Search for a model in the marketplace
        • 3. Get the model
        • 4. Upload your data to storage resources
        • 5. Train the model
        • 6. Testing the training
      • Train a model remotely
        • 1. Choose a model
        • 2. Prerequisites
        • 3. Upload your files to Nextcloud
        • 4. Orchent submission script
        • 5. The rclone configuration file
        • 6. Prepare your TOSCA file
        • 7. Create the orchent deployment
        • 8. Go to the API, train the model
        • 9. Testing the training
      • Test a service locally
        • 1. Get Docker
        • 2. Search for a model in the marketplace
        • 3. Get the model
        • 4. Run the model
        • 5. Go to the API, get the results
      • Use rclone
        • Installation of rclone in Docker image (pro)
        • Nextcloud configuration for rclone
        • Creating rclone.conf
        • Example code on usage of rclone from python
      • Install and configure oidc-agent
        • 1. Installing oidc-agent
        • 2. Configuring oidc-agent with DEEP-IAM
      • Video demos
    • Modules
      • Toy example: dog’s breed detection
        • Description
        • Local Workflow
        • DEEP Pilot infrastructure submission
        • Examples
      • DEEP Open Catalogue: Image classification on TensorFlow
        • Workflow
        • Launching the full DEEPaas API
      • DEEP Open Catalogue: Massive Online Data Streams
        • Description
        • Workflow
        • Launching the full DEEPaas API
  • 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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