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clearml-server/docs/docker_setup.md
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TRAINS-server: Using Docker Pre-Built Images

The pre-built Docker image for the trains-server is the quickest way to get started with your own TRAINS server.

You can also build the entire trains-server architecture using the code available in the trains-server repository.

Note: We tested this pre-built Docker image with Linux, only. For Windows users, we recommend installing the pre-built image on a Linux virtual machine.

Prerequisites

  • You must be logged in as a user with sudo privileges
  • Use bash for all command-line instructions in this installation

Setup Docker

Step 1: Install Docker CE

You must first install Docker. For instructions about installing Docker, see Supported platforms in the Docker documentation.

For example, to install in Ubuntu / Mint (x86_64/amd64):

sudo apt-get install -y apt-transport-https ca-certificates curl software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
. /etc/os-release
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $UBUNTU_CODENAME stable"
sudo apt-get update
sudo apt-get install -y docker-ce

Step 2: Set the Maximum Number of Memory Map Areas

Elastic requires that the vm.max_map_count kernel setting, which is the maximum number of memory map areas a process can use, is set to at least 262144.

For CentOS 7, Ubuntu 16.04, Mint 18.3, Ubuntu 18.04 and Mint 19.x, we tested the following commands to set vm.max_map_count:

echo "vm.max_map_count=262144" > /tmp/99-trains.conf
sudo mv /tmp/99-trains.conf /etc/sysctl.d/99-trains.conf
sudo sysctl -w vm.max_map_count=262144

For information about setting this parameter on other systems, see the elastic documentation.

Step 3: Restart the Docker daemon

Restart the Docker daemon.

sudo service docker restart

Step 4: Choose a Data Directory

Choose a directory on your system in which all data maintained by the trains-server is stored. Create this directory, and set its owner and group to uid 1000. The data stored in this directory includes the database, uploaded files and logs.

For example, if your data directory is /opt/trains, then use the following command:

    sudo mkdir -p /opt/trains/data/elastic
    sudo mkdir -p /opt/trains/data/mongo/db
    sudo mkdir -p /opt/trains/data/mongo/configdb
    sudo mkdir -p /opt/trains/data/redis
    sudo mkdir -p /opt/trains/logs
    sudo mkdir -p /opt/trains/data/fileserver
    sudo mkdir -p /opt/trains/config

    sudo chown -R 1000:1000 /opt/trains

TRAINS-server: Manually Launching Docker Containers

You can manually launch the Docker containers using the following commands.

If your data directory is not /opt/trains, then in the five docker run commands below, you must replace all occurrences of /opt/trains with your data directory path.

  1. Launch the trains-elastic Docker container.

     sudo docker run -d --restart="always" --name="trains-elastic" -e "bootstrap.memory_lock=true" --ulimit memlock=-1:-1 -e "ES_JAVA_OPTS=-Xms2g -Xmx2g" -e "bootstrap.memory_lock=true" -e "cluster.name=trains" -e "discovery.zen.minimum_master_nodes=1" -e "node.name=trains" -e "script.inline=true" -e "script.update=true" -e "thread_pool.bulk.queue_size=2000" -e "thread_pool.search.queue_size=10000" -e "xpack.security.enabled=false" -e "xpack.monitoring.enabled=false" -e "cluster.routing.allocation.node_initial_primaries_recoveries=500" -e "node.ingest=true" -e "http.compression_level=7" -e "reindex.remote.whitelist=*.*" -e "script.painless.regex.enabled=true" --network="host" -v /opt/trains/data/elastic:/usr/share/elasticsearch/data docker.elastic.co/elasticsearch/elasticsearch:5.6.16

  2. Launch the trains-mongo Docker container.

     sudo docker run -d --restart="always" --name="trains-mongo" -v /opt/trains/data/mongo/db:/data/db -v /opt/trains/data/mongo/configdb:/data/configdb --network="host" mongo:3.6.5

  3. Launch the trains-redis Docker container.

     sudo docker run -d --restart="always" --name="trains-redis" -v /opt/trains/data/redis:/data --network="host" redis:5.0

  4. Launch the trains-fileserver Docker container.

     sudo docker run -d --restart="always" --name="trains-fileserver" --network="host" -v /opt/trains/logs:/var/log/trains -v /opt/trains/data/fileserver:/mnt/fileserver allegroai/trains:latest fileserver

  5. Launch the trains-apiserver Docker container.

     sudo docker run -d --restart="always" --name="trains-apiserver" --network="host" -v /opt/trains/logs:/var/log/trains -v /opt/trains/config:/opt/trains/config allegroai/trains:latest apiserver

  6. Launch the trains-webserver Docker container.

     sudo docker run -d --restart="always" --name="trains-webserver" -p 8080:80 allegroai/trains:latest webserver

  7. Your server is now running on http://localhost:8080 and the following ports are available:

    • API server on port 8008
    • Web server on port 8080
    • File server on port 8081

Manually Upgrading TRAINS-server Containers

We are constantly updating, improving and adding to the trains-server. New releases will include new pre-built Docker images. When we release a new version and include a new pre-built Docker image for it, upgrade as follows:

  1. Shut down and remove each of your Docker instances using the following commands:

    $ sudo docker stop <docker-name>
$ sudo docker rm -v <docker-name>

    The Docker names are (see Launching Docker Containers):

    • trains-elastic
    • trains-mongo
    • trains-redis
    • trains-fileserver
    • trains-apiserver
    • trains-webserver

  2. We highly recommend backing up your data directory!. A simple way to do that is using tar:

    For example, if your data directory is /opt/trains, use the following command:

    $ sudo tar czvf ~/trains_backup.tgz /opt/trains/data

    This backups all data to an archive in your home directory.

    To restore this example backup, use the following command:

    $ sudo rm -R /opt/trains/data
$ sudo tar -xzf ~/trains_backup.tgz -C /opt/trains/data

  3. Pull the new trains-server docker image using the following command:

    $ sudo docker pull allegroai/trains:latest

    If you wish to pull a different version, replace latest with the required version number, for example:

    $ sudo docker pull allegroai/trains:0.11.0

  4. Launch the newly released Docker image (see Launching Docker Containers).

Common Docker Upgrade Errors

  • In case of a docker error: "... The container name "/trains-???" is already in use by ..."
    Try removing deprecated images with: 
    $ docker rm -f $(docker ps -a -q)