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# TRAINS Server # TRAINS Server
## Magic Version Control & Experiment Manager for AI ## Automagical Version Control & Experiment Manager for AI
[![GitHub license](https://img.shields.io/badge/license-SSPL-green.svg)](https://img.shields.io/badge/license-SSPL-green.svg) [![GitHub license](https://img.shields.io/badge/license-SSPL-green.svg)](https://img.shields.io/badge/license-SSPL-green.svg)
[![GitHub version](https://img.shields.io/github/release-pre/allegroai/trains-server.svg)](https://img.shields.io/github/release-pre/allegroai/trains-server.svg) [![GitHub version](https://img.shields.io/github/release-pre/allegroai/trains-server.svg)](https://img.shields.io/github/release-pre/allegroai/trains-server.svg)
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## Introduction ## Introduction
The **trains-server** is the infrastructure for [TRAINS](https://github.com/allegroai/trains). The **trains-server** is the infrastructure for [TRAINS](https://github.com/allegroai/trains).
It allows multiple users to collaborate and manage their experiments. It allows multiple users to collaborate and manage their experiments.
The **trains-server** contains the following components: The **trains-server** contains the following components:
* the Web-App which is a single-page UI for experiment management and browsing * the Web-App which is a single-page UI for experiment management and browsing
* a REST interface for: * a REST interface for:
* documenting and logging experiment information, statistics and results * documenting and logging experiment information, statistics and results
* querying experiments history, logs and results * querying experiments history, logs and results
* a locally-hosted file server for storing images and models making them easily accessible using the Web-App * a locally-hosted file server for storing images and models making them easily accessible using the Web-App
You can quickly setup your **trains-server** using a pre-built Docker image (see [Installation](#installation)). You can quickly setup your **trains-server** using a pre-built Docker image (see [Installation](#installation)).
When new releases are available, you can upgrade your pre-built Docker image (see [Upgrade](#upgrade)). When new releases are available, you can upgrade your pre-built Docker image (see [Upgrade](#upgrade)).
The **trains-server's** code is freely available [here](https://github.com/allegroai/trains-server). The **trains-server's** code is freely available [here](https://github.com/allegroai/trains-server).
@ -72,12 +72,12 @@ This section contains the instructions to setup and launch a pre-built Docker im
* This Docker image was tested with Linux, only. For Windows users, we recommend running the server * This Docker image was tested with Linux, only. For Windows users, we recommend running the server
on a Linux virtual machine. on a Linux virtual machine.
* All command-line instructions below assume you're using `bash`. * All command-line instructions below assume you're using `bash`.
### Prerequisites ### Prerequisites
You must be logged in as a user with sudo privileges. You must be logged in as a user with sudo privileges.
### Setup ### Setup
#### Step 1: Install Docker CE #### Step 1: Install Docker CE
@ -99,12 +99,12 @@ You must install Docker to run the pre-packaged **trains-server**.
#### Step 2: Setup the Docker daemon #### Step 2: Setup the Docker daemon
To run the ElasticSearch Docker container, you must setup the Docker daemon by modifying the default To run the ElasticSearch Docker container, you must setup the Docker daemon by modifying the default
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. 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.
You must edit or create a Docker configuration file: You must edit or create a Docker configuration file:
* If your system contains a `/etc/sysconfig/docker` Docker configuration file, edit it. * If your system contains a `/etc/sysconfig/docker` Docker configuration file, edit it.
Add the options in quotes to the available arguments in the `OPTIONS` section: 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). * Otherwise, edit `/etc/docker/daemon.json` (if it exists) or create it (if it does not exist).
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. 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.
**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. **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 #### Step 4: Set the Maximum Number of Memory Map Areas
The maximum number of memory map areas a process can use is defined The maximum number of memory map areas a process can use is defined
using the `vm.max_map_count` kernel setting. using the `vm.max_map_count` kernel setting.
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)). 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). Once you've installed the **trains-server**, please make sure to configure **trains** to use your locally installed server (and not the demo server).
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: 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:
``` ```
api { api {
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} }
``` ```
See [Installing and Configuring TRAINS](https://github.com/allegroai/trains#installing-and-configuring-trains) for more details. See [Installing and Configuring TRAINS](https://github.com/allegroai/trains#installing-and-configuring-trains) for more details.
## Upgrade ## Upgrade
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sudo docker stop <docker-name> sudo docker stop <docker-name>
sudo docker rm -v <docker-name> sudo docker rm -v <docker-name>
The Docker names are (see [Launching Docker Containers](#launching-docker-containers)): The Docker names are (see [Launching Docker Containers](#launching-docker-containers)):
* `trains-elastic` * `trains-elastic`
* `trains-mongo` * `trains-mongo`
* `trains-fileserver` * `trains-fileserver`
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2. We highly recommend backing up your data directory!. A simple way to do that is using `tar`: 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: For example, if your data directory is `/opt/trains`, use the following command:
sudo tar czvf ~/trains_backup.tgz /opt/trains/data sudo tar czvf ~/trains_backup.tgz /opt/trains/data
This back ups all data to an archive in your home directory. This back ups all data to an archive in your home directory.
To restore this example backup, use the following command: To restore this example backup, use the following command:
sudo rm -R /opt/trains/data sudo rm -R /opt/trains/data
sudo tar -xzf ~/trains_backup.tgz -C /opt/trains/data sudo tar -xzf ~/trains_backup.tgz -C /opt/trains/data