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Kubernetes
Metarank can be deployed in a distributed fashion inside a Kubernetes cluster.
installation overview
For a distributed K8S deployment, metarank requires the following external services and tools to be already available:
- 1.Helm: used to install the Metarank chart.
- 2.Redis: as an almost-persistent data store for inference. Can be also installed either inside k8s with helm, or as a managed service like AWS ElastiCache Redis.
- 3.Distributed event bus for event ingestion: Kafka, Pulsar, Kinesis and internal RESTful API are supported.
Metarank supports multiple ways of ingesting training data into the system:
- event file can be HTTT POSTed to the
/feedbackendpoint using the REST API. Metarank does not do any in-memory buffering, so if your dataset is below 1GiB in size, this may be the simplest way to ingest. - event can be imported from a Kafka/Pulsar/Kinesis topic or read from files locally. Note that distributed import is not yet supported.
We suggest to start with a HTTP-based event import, and switch to offline local import if you have any issues with it.
With Helm installed according to its official installation guide, you need to add a Metarank Helm repo:
$> helm repo add metarank https://metarank.github.io/helm-charts
"metarank" has been added to your repositories
$> helm repo update
Hang tight while we grab the latest from your chart repositories...
...Successfully got an update from the "metarank" chart repository
Update Complete. Happy Helming!
$> helm pull metarank/metarank --untar
$> cd metarank
In the chart directory there are
metarank.conf and values.yaml files you'll need to update before the deployment:total 24
drwxr-xr-x 2 user user 4096 Oct 4 14:25 charts
-rw-r--r-- 1 user user 124 Oct 4 17:23 Chart.yaml
-rw-r--r-- 1 user user 376 Oct 4 17:23 metarank.conf
-rw-r--r-- 1 user user 989 Oct 4 17:23 README.md
drwxr-xr-x 3 user user 4096 Oct 4 17:23 templates
-rw-r--r-- 1 user user 1889 Oct 4 17:23 values.yaml
The
metarank.conf file is a regular metarank configuration file, so you can check the configuration guide to set things up manually, or use an automatic data-based config generator.The
metarank.conf file requires you to define a Redis endpoint for state store. A good-looking config file is shown below:api:
host: 0.0.0.0
state:
type: redis
host: add-redis-hostname-here
port: 6379
models:
xgboost:
type: lambdamart
backend:
type: xgboost
iterations: 50
weights:
click: 1
features:
- popularity
features:
- name: popularity
type: number
scope: item
source: metadata.popularity
The
values.yaml is a generic helm deployment configuration file. You can tune it, but default one usually requires no extra changes.The default helm chart sets no specific memory requests & limits, but it can be configured with
values.yaml.The Metarank docker container accepts a
JAVA_OPTS environment variable to control the JVM memory usage. It defaults to JAVA_OPTS="-Xmx1g -verbose:gc" which means:- Use 1Gb for JVM heap. The actual RSS memory usage should be a bit higher due to JVM extra overhead.
- Enable verbose GC logging. You may notice the following lines in the log, they are normal:
[282.621s][info][gc] GC(26) Pause Young (Allocation Failure) 55M->36M(67M) 2.718ms
The chart itself is agnostic to the Metarank version, and has separate versioning. For the latest Metarank
0.7.4 release, use the following command to install the chart:helm install metarank . --set-file config=metarank.conf --set image.tag=0.7.4
NAME: metarank
LAST DEPLOYED: Tue Oct 4 15:32:47 2022
NAMESPACE: default
STATUS: deployed
REVISION: 1
After that, a single metarank pod will be running:
$> kubectl get pods
NAME READY STATUS RESTARTS AGE
metarank-6c577f46f6-9c9mz 1/1 Running 0 136m
redis-master-0 1/1 Running 0 171m
After successful deployment you may want to do the following:
- Enable ingress in
values.yamlso Metarank can be accessible from outside.
Last modified 22h ago