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Documentation
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allegroai
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README.md
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README.md
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# TRAINS Server
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## Magic Version Control & Experiment Manager for AI
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## Automagical Version Control & Experiment Manager for AI
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[](https://img.shields.io/badge/license-SSPL-green.svg)
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[](https://img.shields.io/github/release-pre/allegroai/trains-server.svg)
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## Introduction
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The **trains-server** is the infrastructure for [TRAINS](https://github.com/allegroai/trains).
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It allows multiple users to collaborate and manage their experiments.
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It allows multiple users to collaborate and manage their experiments.
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The **trains-server** contains the following components:
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* the Web-App which is a single-page UI for experiment management and browsing
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* a REST interface for:
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* a REST interface for:
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* documenting and logging experiment information, statistics and results
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* querying experiments history, logs and results
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* a locally-hosted file server for storing images and models making them easily accessible using the Web-App
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You can quickly setup your **trains-server** using a pre-built Docker image (see [Installation](#installation)).
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When new releases are available, you can upgrade your pre-built Docker image (see [Upgrade](#upgrade)).
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When new releases are available, you can upgrade your pre-built Docker image (see [Upgrade](#upgrade)).
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The **trains-server's** code is freely available [here](https://github.com/allegroai/trains-server).
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* This Docker image was tested with Linux, only. For Windows users, we recommend running the server
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on a Linux virtual machine.
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* All command-line instructions below assume you're using `bash`.
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* All command-line instructions below assume you're using `bash`.
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### Prerequisites
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You must be logged in as a user with sudo privileges.
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### Setup
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#### Step 1: Install Docker CE
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#### Step 2: Setup the Docker daemon
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To run the ElasticSearch Docker container, you must setup the Docker daemon by modifying the default
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To run the ElasticSearch Docker container, you must setup the Docker daemon by modifying the default
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values required by Elastic in your Docker configuration file (see [Notes for production use and defaults](https://www.elastic.co/guide/en/elasticsearch/reference/master/docker.html#_notes_for_production_use_and_defaults)). We provide instructions for the most common Docker configuration files.
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You must edit or create a Docker configuration file:
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* If your system contains a `/etc/sysconfig/docker` Docker configuration file, edit it.
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* If your system contains a `/etc/sysconfig/docker` Docker configuration file, edit it.
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Add the options in quotes to the available arguments in the `OPTIONS` section:
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* Otherwise, edit `/etc/docker/daemon.json` (if it exists) or create it (if it does not exist).
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Add or modify the `defaults-ulimits` section as shown below. Be sure the `defaults-ulimits` section contains the `nofile` and `memlock` sub-sections and values shown.
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Add or modify the `defaults-ulimits` section as shown below. Be sure the `defaults-ulimits` section contains the `nofile` and `memlock` sub-sections and values shown.
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**Note**: Your configuration file may contain other sections. If so, confirm that the sections are separated by commas (valid JSON format). For more information about Docker configuration files, see [Daemon configuration file](https://docs.docker.com/engine/reference/commandline/dockerd/#daemon-configuration-file) in the Docker documentation.
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#### Step 4: Set the Maximum Number of Memory Map Areas
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The maximum number of memory map areas a process can use is defined
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using the `vm.max_map_count` kernel setting.
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using the `vm.max_map_count` kernel setting.
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Elastic requires that `vm.max_map_count` is at least 262144 (see [Production mode](https://www.elastic.co/guide/en/elasticsearch/reference/master/docker.html#docker-cli-run-prod-mode)).
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Once you've installed the **trains-server**, please make sure to configure **trains** to use your locally installed server (and not the demo server).
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If you've already installed **trains**, run the `trains-init` command for an interactive setup or edit your `trains.conf` file and make sure the `api.host` value is configured as follows:
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If you've already installed **trains**, run the `trains-init` command for an interactive setup or edit your `trains.conf` file and make sure the `api.host` value is configured as follows:
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```
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api {
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}
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```
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See [Installing and Configuring TRAINS](https://github.com/allegroai/trains#installing-and-configuring-trains) for more details.
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See [Installing and Configuring TRAINS](https://github.com/allegroai/trains#installing-and-configuring-trains) for more details.
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## Upgrade
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sudo docker stop <docker-name>
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sudo docker rm -v <docker-name>
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The Docker names are (see [Launching Docker Containers](#launching-docker-containers)):
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* `trains-elastic`
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* `trains-mongo`
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* `trains-fileserver`
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2. We highly recommend backing up your data directory!. A simple way to do that is using `tar`:
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For example, if your data directory is `/opt/trains`, use the following command:
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sudo tar czvf ~/trains_backup.tgz /opt/trains/data
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This back ups all data to an archive in your home directory.
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This back ups all data to an archive in your home directory.
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To restore this example backup, use the following command:
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sudo rm -R /opt/trains/data
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sudo tar -xzf ~/trains_backup.tgz -C /opt/trains/data
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