Grafana allows you to query, visualize, alert on and understand your metrics no matter where they are stored. In PAI system, the Grafana provides beautiful analytics and monitoring for cluster runtime environment and job metrics.
Prometheus scrapes metrics through exporters. It stores all scraped samples locally and runs rules over this data to either aggregate and record new time series from existing data or generate alerts. Grafana or other API consumers can query the Prometheus server to visualize the collected data.
The Grafana can start up independently, but to show the monitoring data, it depends on the following service: Prometheus
If you want to build Grafana only, under the pai-management
directory, run the following command, note to replace /path/to/cluster-configuration/dir
with your own cluster configuration path.
python paictl.py image build -p /path/to/cluster-configuration/dir -n grafana
You can change the following configuration of Grafana in services-configuration.yaml file:
grafana-port: 3000 # port for grafana
The deployment of web portal goes with the bootstrapping process of the whole PAI cluster, which is described in detail in Tutorial: Booting up the cluster.
If you want to redeploy Grafana only, firstly stop it, note to replace /path/to/cluster-configuration/dir
with your own cluster configuration path:
python paictl.py service stop -p /path/to/cluster-configuration/dir -n grafana
Then redeploy Grafana only:
python paictl.py service start -p /path/to/cluster-configuration/dir -n grafana
For more details, please refer to Maintain your service.
System will automatically pull the latest Grafana image, there is no need to upgrade. If you want to use the specific version of Grafana, you can change the version configuration at the grafana.yaml.template and then redeploy it.
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The new feature is on the way.
It doesn't require much resources, a normal PC with Docker installed is ok. Usually it takes 0.04% of CPU utilization, about 28MB of memory usage. Disk consumption is tiny.
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