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Improved docker-compose installation process

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233
README.md
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@ -31,141 +31,83 @@ When new releases are available, you can upgrade your pre-built Docker image (se
![Alt Text](https://github.com/allegroai/trains/blob/master/docs/system_diagram.png?raw=true)
## Install / Upgrade - AWS <a name="aws"></a>
## Install / Upgrade - AWS
Use one of our pre-installed Amazon Machine Images for easy deployment in AWS.
Use our pre-installed Amazon Machine Image for easy deployment in AWS.
For details and instructions, see [TRAINS-server: AWS pre-installed images](docs/install_aws.md).
Details and instructions can be found [here](docs/install_aws.md).
## Install - Linux, Mac OS X <a name="installation"></a>
## Installation - Docker
Use our pre-built Docker image for easy deployment in Linux and Mac OS X.
For Windows, we recommend installing our pre-built Docker image on a Linux virtual machine.
This section contains the instructions to setup and launch a pre-built Docker image for the **trains-server**.
This is the quickest way to get started with your own server.
Alternatively, you can build the entire trains-server architecture using the code available in our repositories.
**Please Note**:
* This Docker image was tested with Linux, only. For Windows users, we recommend running the server
on a Linux virtual machine.
* All command-line instructions below assume you're using `bash`.
### Prerequisites
Make sure you are logged in as a user with sudo privileges.
### Setup
#### Step 1: Install Docker CE
In order to run the pre-packaged **trains-server**, install Docker.
* See [Supported platforms](https://docs.docker.com/install//#support) in the Docker documentation for instructions
* For example, to install in [Ubuntu](https://docs.docker.com/install/linux/docker-ce/ubuntu/) / Mint (x86_64/amd64):
1. Setup Docker (Full details [Setup Docker Service](docs/docker_setup.md))
Make sure port 8080/8081/8008 are available for the `trains-server` services
Increase vm.max_map_count for `ElasticSearch` docker
```bash
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: Setup the Docker daemon
To run the ElasticSearch Docker container, 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.
Edit or create the Docker configuration file:
* 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:
```bash
OPTIONS="--default-ulimit nofile=1024:65536 --default-ulimit memlock=-1:-1"
```
* 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.
**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.
The **trains-server** required defaults values are:
```json
{
"default-ulimits": {
"nofile": {
"name": "nofile",
"hard": 65536,
"soft": 1024
},
"memlock":
{
"name": "memlock",
"soft": -1,
"hard": -1
}
}
}
```
#### Step 3: Restart the Docker daemon
After modifying the configuration file, restart the Docker daemon:
```bash
sudo service docker stop
sudo service docker start
```
#### Step 4: Set the Maximum Number of Memory Map Areas
The maximum number of memory map areas a process can use is defined
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)).
* For CentOS 7, Ubuntu 16.04, Mint 18.3, Ubuntu 18.04 and Mint 19 users, we tested the following commands to set
`vm.max_map_count`:
```bash
sudo echo "vm.max_map_count=262144" > /tmp/99-trains.conf
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
sudo sudo service docker restart
```
1. Create local directories for the databases and storage.
```bash
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/logs
sudo mkdir -p /opt/trains/data/fileserver
```
Linux
```bash
sudo chown -R 1000:1000 /opt/trains
```
Mac OS X
```bash
sudo chown -R $(whoami):staff /opt/trains
```
1. Clone the [trains-server](https://github.com/allegroai/trains-server) repository and change directories to the new **trains-server** directory.
* For information about setting this parameter on other systems, see the [elastic](https://www.elastic.co/guide/en/elasticsearch/reference/current/docker.html#docker-cli-run-prod-mode) documentation.
$ git clone https://github.com/allegroai/trains-server.git
$ cd trains-server
1. Launch the Docker containers <a name="launch-docker"></a>
#### Step 5: Choose a Data Directory
* Automatically with docker-compose (details: [Linux/Ubuntu](docs/faq.md##ubuntu), [OS X](docs/faq.md#mac-osx))
```bash
$ docker-compose up
```
* Manually
See [TRAINS-server: Launching Docker Containers Manually](docs/manual_docker.md)) for instructions.
1. Your server is now running on [http://localhost:8080](http://localhost:8080) and the following ports are available:
* Web server on port `8080`
* API server on port `8008`
* File server on port `8081`
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 will include the database, uploaded files and logs.
For example, if your data directory is `/opt/trains`, then use the following command:
```bash
sudo mkdir -p /opt/trains/data/elastic && sudo chown -R 1000:1000 /opt/trains
```
### Optional Configuration
## Optional: Configuration
The **trains-server** default configuration can be easily overridden using external configuration files. By default, the server will look for these files in `/opt/trains/config`.
If the configuration is changed while the server is running, the server should be restarted for changes to take effect.
If the configuration is changed while the server is running, to apply the changes you must restart the server (see [Restarting trains-server](#restart-server)).
### Configuring TRAINS to Authenticate Web Login Credentials
#### Fixed users mode (basic users management)
In this mode, the server authenticates users based only on a pre-configured users list.
By default anyone can login to the **trains-server** Web-App.
You can configure the **trains-server** to allow access only to specific users (with pre-configured user/pass).
Enable this feature by placing `apiserver.conf` file under `/opt/trains/config`.
**Notice**: In order for the changes to take effect, the *trains-apiserver* (docker) will need to be restarted.
Sample fixed user configuration file `/opt/trains/config/apiserver.conf`:
@ -189,10 +131,12 @@ Sample fixed user configuration file `/opt/trains/config/apiserver.conf`:
}
}
To apply the `apiserver.conf` changes, you must restart the *trains-apiserver* (docker) (see [Restarting trains-server](#restart-server)).
#### Non-responsive experiments watchdog
### Configuring the Non-Responsive Experiments Watchdog Thresholds
This watchdog monitors experiments that were not updated for a given period of time, and marks them as `stopped`. The watchdog is always active.
The non-responsive experiment watchdog monitors experiments that were not updated for a given period of time,
and marks them as `aborted`. The watchdog is always active with a default of 7200 seconds (2 hours).
To change the watchdog's timeouts, place a `services.conf` file under `/opt/trains/config`, containing for example:
@ -206,54 +150,31 @@ To change the watchdog's timeouts, place a `services.conf` file under `/opt/trai
}
}
### Launching Docker Containers
To apply the `services.conf` changes, you must restart the *trains-apiserver* (docker) (see [Restarting trains-server](#restart-server)).
To launch the Docker containers, first confirm the required ports are available and then launch the Docker containers.
### Restarting trains-server <a name="restart-server"></a>
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.
To restart the **trains-server**, you must first stop and remove the containers, and then restart.
1. Make sure ports `8008`, `8080` and `8081` are not in use. If these ports are already taken, the containers will fail to initialize.
1. Restarting docker-compose containers.
Run the following commands:
sudo netstat -tplna | egrep "8008|8080|8081"
$ docker-compose down
$ docker-compose up
If the commands show no output, the ports are available.
1. Manually restarting dockers [instructions](docs/manual_docker.md).
1. Launch the **trains-elastic** Docker container.
## Configuring **TRAINS** client
sudo docker run -d --restart="always" --name="trains-elastic" -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
Once you have installed the **trains-server**, make sure to configure **TRAINS** [client](https://github.com/allegroai/trains)
to use your locally installed server (and not the demo server).
1. Launch the **trains-mongo** Docker container.
- Run the `trains-init` command for an interactive setup
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
- Or manually edit `~/trains.conf` file, making sure the `api_server` value is configured correctly, for example:
1. 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
1. 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
1. Launch the **trains-webserver** Docker container.
sudo docker run -d --restart="always" --name="trains-webserver" -p 8080:80 allegroai/trains:latest webserver
After the **trains-server** Dockers are up, the following are available:
* API server on port `8008`
* Web server on port `8080`
* File server on port `8081`
### Configuring **TRAINS** client
Once you have installed the **trains-server**, make sure to configure **TRAINS** [client](https://github.com/allegroai/trains) to use your locally installed server (and not the demo server).
If you have 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 {
host: "http://localhost:8008"
api_server: "http://localhost:8008"
}
See [Installing and Configuring TRAINS](https://github.com/allegroai/trains#installing-and-configuring-trains) for more details.
@ -264,7 +185,7 @@ Now that the **trains-server** is installed, and TRAINS is configured to use it,
you can [use](https://github.com/allegroai/trains#using-trains) TRAINS in your experiments and view them in the web server,
for example http://localhost:8080
## Upgrade
## Upgrading <a name="upgrade"></a>
We are constantly updating, improving and adding to the **trains-server**.
New releases will include new pre-built Docker images.
@ -275,7 +196,7 @@ When we release a new version and include a new pre-built Docker image for it, u
sudo docker stop <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](#launch-docker)):
* `trains-elastic`
* `trains-mongo`
@ -289,7 +210,7 @@ When we release a new version and include a new pre-built Docker image for it, u
If you wish to pull a different version, replace `latest` with the required version number, for example:
sudo docker pull allegroai/trains:0.10.0
sudo docker pull allegroai/trains:0.10.1
3. We highly recommend backing up your data directory!. A simple way to do that is using `tar`:
@ -304,7 +225,7 @@ When we release a new version and include a new pre-built Docker image for it, u
sudo rm -R /opt/trains/data
sudo tar -xzf ~/trains_backup.tgz -C /opt/trains/data
4. Launch the newly released Docker image (see [Launching Docker Containers](#launching-docker-containers)).
4. Launch the newly released Docker image (see [Launching Docker Containers](#launch-docker)).
## Community & Support

34
docker-compose-unified.yml
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@ -11,9 +11,6 @@ services:
- 8008:8008
- 8080:80
- 8081:8081
environment:
ELASTIC_SERVICE_SERVICE_HOST: ${HOSTIP}
MONGODB_SERVICE_SERVICE_HOST: ${HOSTIP}
restart: always
volumes:
- type: bind
@ -22,8 +19,18 @@ services:
- type: bind
source: /opt/trains/data/fileserver
target: /mnt/fileserver
links:
- mongo:mongo
- elasticsearch:elasticsearch
- fileserver:fileserver
environment:
ELASTIC_SERVICE_SERVICE_HOST: elasticsearch
MONGODB_SERVICE_SERVICE_HOST: mongo
networks:
- backend
elasticsearch:
networks:
- backend
container_name: trains-elastic
environment:
ES_JAVA_OPTS: -Xms2g -Xmx2g
@ -42,20 +49,23 @@ services:
thread_pool.search.queue_size: "10000"
xpack.monitoring.enabled: "false"
xpack.security.enabled: "false"
transport.host: localhost
ulimits:
memlock:
soft: -1
hard: -1
image: docker.elastic.co/elasticsearch/elasticsearch:5.6.16
ports:
- 9200:9200
restart: always
volumes:
- type: bind
source: /opt/trains/data/elastic
target: /usr/share/elasticsearch/data
ports:
- "9200:9200"
mongo:
networks:
- backend
container_name: trains-mongo
image: mongo:3.6.5
ports:
- 27017:27017
restart: always
volumes:
- type: bind
@ -64,3 +74,9 @@ services:
- type: bind
source: /opt/trains/data/mongo/configdb
target: /data/configdb
ports:
- "27017:27017"
networks:
backend:
driver: bridge

42
docker-compose.yml
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@ -5,13 +5,25 @@ services:
- apiserver
container_name: trains-apiserver
image: allegroai/trains:latest
network_mode: host
restart: always
volumes:
- type: bind
source: /opt/trains/logs
target: /var/log/trains
links:
- mongo:mongo
- elasticsearch:elasticsearch
- fileserver:fileserver
environment:
ELASTIC_SERVICE_SERVICE_HOST: elasticsearch
MONGODB_SERVICE_SERVICE_HOST: mongo
ports:
- "8008:8008"
networks:
- backend
elasticsearch:
networks:
- backend
container_name: trains-elastic
environment:
ES_JAVA_OPTS: -Xms2g -Xmx2g
@ -30,19 +42,25 @@ services:
thread_pool.search.queue_size: "10000"
xpack.monitoring.enabled: "false"
xpack.security.enabled: "false"
ulimits:
memlock:
soft: -1
hard: -1
image: docker.elastic.co/elasticsearch/elasticsearch:5.6.16
network_mode: host
restart: always
volumes:
- type: bind
source: /opt/trains/data/elastic
target: /usr/share/elasticsearch/data
ports:
- "9200:9200"
fileserver:
networks:
- backend
command:
- fileserver
container_name: trains-fileserver
image: allegroai/trains:latest
network_mode: host
restart: always
volumes:
- type: bind
@ -51,10 +69,13 @@ services:
- type: bind
source: /opt/trains/data/fileserver
target: /mnt/fileserver
ports:
- "8081:8081"
mongo:
networks:
- backend
container_name: trains-mongo
image: mongo:3.6.5
network_mode: host
restart: always
volumes:
- type: bind
@ -63,14 +84,25 @@ services:
- type: bind
source: /opt/trains/data/mongo/configdb
target: /data/configdb
ports:
- "27017:27017"
webserver:
networks:
- backend
command:
- webserver
container_name: trains-webserver
image: allegroai/trains:latest
network_mode: host
restart: always
volumes:
- type: bind
source: /opt/trains/logs
target: /var/log/trains
links:
- apiserver
ports:
- "8080:80"
networks:
backend:
driver: bridge

143
docs/docker_setup.md Normal file
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@ -0,0 +1,143 @@
# 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](https://github.com/allegroai/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](https://docs.docker.com/install//#support) in the Docker documentation.
For example, to [install in Ubuntu](https://docs.docker.com/install/linux/docker-ce/ubuntu/) / Mint (x86_64/amd64):
```bash
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: Setup the Docker daemon
To setup the Docker daemon to run the ElasticSearch Docker container,
modify 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)) in the Elasticsearch documentation.
The following are the instructions to modify those Elastic default values for the most common Docker configuration files.
* 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:
```bash
OPTIONS="--default-ulimit nofile=1024:65536 --default-ulimit memlock=-1:-1"
```
* 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.
**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.
The **trains-server** required defaults values are:
```json
{
"default-ulimits": {
"nofile": {
"name": "nofile",
"hard": 65536,
"soft": 1024
},
"memlock":
{
"name": "memlock",
"soft": -1,
"hard": -1
}
}
}
```
### Step 3: 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`:
```bash
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
sudo service docker restart
```
For information about setting this parameter on other systems, see the [elastic](https://www.elastic.co/guide/en/elasticsearch/reference/current/docker.html#docker-cli-run-prod-mode) documentation.
### Step 4: Restart the Docker daemon
Restart the Docker daemon.
```bash
sudo service docker restart
```
### Step 5: 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:
```bash
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/logs
sudo mkdir -p /opt/trains/data/fileserver
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 "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
1. 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
1. 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
1. 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
1. Launch the **trains-webserver** Docker container.
sudo docker run -d --restart="always" --name="trains-webserver" -p 8080:80 allegroai/trains:latest webserver
1. Your server is now running on [http://localhost:8080](http://localhost:8080) and the following ports are available:
* API server on port `8008`
* Web server on port `8080`
* File server on port `8081`

93
docs/faq.md
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@ -6,12 +6,12 @@
* [Running trains-server on Mac OS X](#mac-osx)
* [Installing trains-server on stand alone Linux Ubuntu systems ](#ubuntu)
* [Resolving port conflicts preventing fixed users mode authentication and login](#port-conflict)
* [Configuring trains-server for sub-domains and load balancers](#sub-domains)
* [Installing trains-server on stand alone Linux Ubuntu systems ](#ubuntu)
### Deploying trains-server on Kubernetes clusters <a name="kubernetes"></a>
**trains-server** supports Kubernetes. See [trains-server-k8s](https://github.com/allegroai/trains-server-k8s)
@ -53,15 +53,49 @@ After installing, each time your computer reboots, you must run the `docker-comp
1. Run `docker-compose` with a unified docker.
Notice: *HOSTIP* holds the host network IP address.
If you have multiple network cards, you might want to specify a different network interface or set the IP manually.
**You must run this command each time your computer reboots, because the dockers will not automatically reload.**
$ HOSTIP=$(ipconfig getifaddr en0) docker-compose -f docker-compose-unified.yml up
$ docker-compose -f docker-compose-unified.yml up
Your server is now running on [http://localhost:8080](http://localhost:8080)
### Installing trains-server on stand alone Linux Ubuntu systems <a name="ubuntu"></a>
To install **trains-server** on a stand alone Linux Ubuntu, follow the steps belows.
After installing, each time your computer reboots, you must run the `docker-compose` command (see Step 6), because the dockers will not automatically reload.
1. Install [docker for Linux Ubuntu](https://docs.docker.com/install/linux/docker-ce/ubuntu/).
1. Install `docker-compose` using the following commands (for more detailed information, see the [Install Docker Compose](https://docs.docker.com/compose/install/) in the Docker documentation):
sudo curl -L "https://github.com/docker/compose/releases/download/1.24.1/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose
1. Remove the previous installation of **trains-server**.
**WARNING**: This clears all existing **TRAINS** databases.
$ sudo rm -R /opt/trains/
1. Create local directories for the databases and storage.
$ 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/logs
$ sudo mkdir -p /opt/trains/data/fileserver
$ sudo chown -R 1000:1000 /opt/trains
1. Clone the [trains-server](https://github.com/allegroai/trains-server) repository and change directories to the new **trains-server** directory.
$ git clone https://github.com/allegroai/trains-server.git
$ cd trains-server
1. Run `docker-compose` with a unified docker.
$ /usr/local/bin/docker-compose -f docker-compose-unified.yml up
Your server is now running on [http://localhost:8080](http://localhost:8080)
### Resolving port conflicts preventing fixed users mode authentication and login <a name="port-conflict"></a>
A port conflict may occur between the **trains-server** MongoDB and Elastic instances and other
@ -147,46 +181,3 @@ For example, if your domain is `trains.mydomain.com` and your sub-domains are `a
1. Run the Docker containers with our updated `docker run` commands (see [Launching Docker Containers](#https://github.com/allegroai/trains-server#launching-docker-containers)).
### Installing trains-server on stand alone Linux Ubuntu systems <a name="ubuntu"></a>
To install **trains-server** on a stand alone Linux Ubuntu, follow the steps belows.
After installing, each time your computer reboots, you must run the `docker-compose` command (see Step 6), because the dockers will not automatically reload.
1. Install [docker for Linux Ubuntu](https://docs.docker.com/install/linux/docker-ce/ubuntu/).
1. Install `docker-compose` using the following commands (for more detailed information, see the [Install Docker Compose](https://docs.docker.com/compose/install/) in the Docker documentation):
sudo curl -L "https://github.com/docker/compose/releases/download/1.24.1/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose
1. Remove the previous installation of **trains-server**.
**WARNING**: This clears all existing **TRAINS** databases.
$ sudo rm -R /opt/trains/
1. Create local directories for the databases and storage.
$ 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/logs
$ sudo mkdir -p /opt/trains/data/fileserver
$ sudo chown -R $(whoami):$(whoami) /opt/trains
1. Clone the [trains-server](https://github.com/allegroai/trains-server) repository and change directories to the new **trains-server** directory.
$ git clone https://github.com/allegroai/trains-server.git
$ cd trains-server
1. Run `docker-compose` with a unified docker.
Notice: *HOSTIP* holds the host network IP address.
If you have multiple network cards, you might want to specify a different network interface or set the IP manually.
**You must run this command each time your computer reboots, because the dockers will not automatically reload.**
$ HOSTIP=$(ip route get 8.8.8.8 | head -1 | cut -d' ' -f8) /usr/local/bin/docker-compose -f docker-compose-unified.yml up
Your server is now running on [http://localhost:8080](http://localhost:8080)