Standalone

As a Java application, Metarank can be run locally either as a JAR-file or Docker container, there is no need for Kubernetes and AWS to start playing with it. Check out the installation guide for detailed setup instructions.
Running modes
Metarank has multiple running modes:
import - import historical clickthroughs to the store
train - run traing the machine learning model using the imported data
serve - start the ranking inference API
standalone - which is a shortcut for import, train and serve jobs run together.
validate - a set of sanity checks on your configuration file and event dataset.
Metarank's standalone mode is made to simplify the initial onboarding on the system:
it's a shortcut to run import, train and serve tasks all at once
with memory persistence it can process large clickthrough histories almost instantly.
Why standalone?
Standalone mode is useful for these cases:
testing Metarank without deployment. With in-memory persistence it has zero service dependencies and is the easiest way to try it out.
simple staging deployments on VM/on-prem hardware. With redis persistence it can handle typical cases with small/medium load.
Standalone mode has the following limitations:
feedback ingestion and inference throughput are limited by a single node. Please use the Kubernetes deployment for a better experience.
model training happens within the inference process, and is a memory hungry process, which may cause latency spikes and OOMs. To overcome this limitation, you can train the machine learning model externally and upload it to the same Redis instance.
Running Metarank in standalone mode
To run the JAR file, make sure to follow the installation manual for your OS and run it:
$ java -jar metarank.jar standalone --data /path/to/events.json --config /path/to/config.yml
Another option is to run Metarank standalone mode from a docker container:
$ docker run -v /data/:<path to data dir> metarank/metarank:latest standalone --data /data/events.json --config /data/config.yml
The follwing options are used for the docker container:
-v /data:<path to data dir> to map a directory with input files and configuration into the container
--data /data/events.json to pass the name of input events file, from the mapped volume
--config /data/config.yml to pass the configuration file​
During the startup process Metarank will:
import your dataset and compute all historical event statistics useful for machine learning model training
train the machine learning model you defined in the configuration file
start the inference API for real-time personaization.
import and training process
For a more detailed walkthrough of running Metarank in playground, check out the quickstart guide.
Last modified 2mo ago