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Deploy DL workspace cluster on Ubuntu.

This document describes the procedure to deploy DL workspace cluster on a Ubuntu Cluster that is on a VLAN with a initial node that is used as a PXE-server for prime the cluster.

  1. [Run Once] Setup development environment.

  2. Configuration the cluster, and determine important information of the cluster (e.g., cluster name, number of Etcd servers used). Please refer to Backup/Restore on instruction to backup/restore cluster configuration.

  3. Configure and setup the databased used in the cluster.

  4. Config shared file system to be used in the cluster, following instructions in Storage and the configuration.

  5. Configure the information of the servers used in the cluster. Please write the following entries in config.yaml.

network:
  domain: <<current_domain>>
  container-network-iprange: "<<your_cluster_ip_range, in 10.109.x.x/24 format>>" 

platform-scripts : ubuntu

machines:
  <<machine1>>:
    role: infrastructure
  <<machine2>>:
    role: worker
  <<machine3>>:
    role: worker
  ....

If you are building a high availability cluster, please include multiple infrastructure nodes. The number of infrastructure nodes should be odd, e.g., 1, 3, 5. 3 infrastructure nodes tolerate 1 failure. 5 infrastructure nodes tolerate 2 failures.

  1. Build Ubuntu PXE-server via:
./deploy.py -y build 
./deploy.py build pxe-ubuntu
  1. Start Ubuntu PXE-server. You will need to point DHCP server to the Ubuntu PXE-server.
./deploy.py docker run pxe-ubuntu

Reboot each machine to be deployed. In each boot screen, select to install Ubuntu 16.04.

  1. After the machines is reimaged to Ubuntu, install sshkey. (optional: If you ignore step 2,3 and choose to use an existing ubuntu cluster, you may put root username and password to files: ./deploy/sshkey/rootuser and ./deploy/sshkey/rootpasswd. In this case, the root user should be able to run "sudo" without password.)
./deploy.py sshkey install
  1. Setup basic tools on the Ubuntu image.
./deploy.py runscriptonall ./scripts/prepare_ubuntu.sh
./deploy.py execonall sudo usermod -aG docker core

  1. Partition hard drive, if necessary. Please refer to section Partition for details.

  2. Setup kubernetes

./deploy.py -y deploy
./deploy.py -y updateworker
./deploy.py -y kubernetes labels

If you are running a small cluster, and need to run workload on the Kubernete master node (this choice may affect cluster stability), please use:

./deploy.py -y kubernetes uncordon

Works now will be scheduled on the master node. If you stop here, you will have a fully functional kubernete cluster. Thus, part of DL Workspace setup can be considered automatic procedure to setup a kubernete cluster. You don't need shared file system or database for kubernete cluster operation.

  1. [optional] Configure, setup and mount GlusterFS

  2. [Optional] Configure, setup and mount HDFS

  3. [Optional] Setup Spark

  4. Mount shared file system

./deploy.py mount
  1. Build and deploy jobmanager, restfulapi, and webportal. Mount storage.
./deploy.py webui
./deploy.py docker push restfulapi
./deploy.py docker push webui
./deploy.py kubernetes start jobmanager
./deploy.py kubernetes start restfulapi
./deploy.py kubernetes start webportal