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README.md
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@ -26,8 +26,6 @@ You can quickly setup your **trains-server** using a pre-built Docker image (see
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).
## System diagram
@ -154,6 +152,51 @@ For example, if your data directory is `/opt/trains`, then use the following com
sudo mkdir -p /opt/trains/data/elastic && sudo chown -R 1000:1000 /opt/trains
```
### 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.
<!---
#### Fixed users mode (basic users management)
In this mode, the server authenticates users based on a pre-configured users list.
Enable this feature by placing an `apiserver.conf` file under `/opt/trains/config`, containing for example:
fixed_users {
enabled: true
users: [
{
username: "jane"
password: "123456"
name: "Jane Doe"
},
{
username: "john"
password: "abcdef"
name: "John Doe"
}
]
}
-->
#### Non-responsive experiments watchdog
This watchdog monitors experiments that were not updated for a given period of time, and marks them as `stopped`. The watchdog is always active.
To change the watchdog's timeouts, place a `services.conf` file under `/opt/trains/config`, containing for example:
tasks {
non_responsive_tasks_watchdog {
# In-progress tasks that haven't been updated for at least 'value' seconds will be stopped by the watchdog
threshold_sec: 7200
# Watchdog will sleep for this number of seconds after each cycle
watch_interval_sec: 900
}
}
### Launching Docker Containers
**Note**:
@ -179,7 +222,7 @@ sudo docker run -d --restart="always" --name="trains-fileserver" --network="host
```
```bash
sudo docker run -d --restart="always" --name="trains-apiserver" --network="host" -v /opt/trains/logs:/var/log/trains allegroai/trains:latest apiserver
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
```
```bash
@ -198,11 +241,9 @@ Once you have installed the **trains-server**, make sure to configure **trains**
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 {
host: "http://localhost:8008"
}
See [Installing and Configuring TRAINS](https://github.com/allegroai/trains#installing-and-configuring-trains) for more details.
@ -231,7 +272,15 @@ When we release a new version and include a new pre-built Docker image for it, u
* `trains-apiserver`
* `trains-webserver`
2. We highly recommend backing up your data directory!. A simple way to do that is using `tar`:
2. 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:0.10.0
3. 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:
@ -244,7 +293,7 @@ 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
3. Launch the newly released Docker image (see [Launching Docker Containers](#launching-docker-containers)).
4. Launch the newly released Docker image (see [Launching Docker Containers](#launching-docker-containers)).
## License