The service discovery file is generated on the head node. The following information guides you on the setup. This function allows you to scrape all metrics in the cluster without knowing their IPs. Ray auto-generates a Prometheus service discovery file on the head node to facilitate metrics agents’ service discovery. Use auto-discovery to export Prometheus metrics when using the Ray cluster launcher, as node IP addresses can often change as the cluster scales up and down. You can allow Prometheus to dynamically find the endpoints to scrape by using Prometheus’ file based service discovery. To scrape the endpoints, we need to ensure service discovery, which allows Prometheus to find the metrics agents’ endpoints on each node. You can then scrape each endpoint to access the metrics. Node and exposes them in a Prometheus format. Each metrics agent collects metrics from the local Ray runs a metrics agent per node to export system and application metrics. To ensure a proper setup, mount the shared volume on the respective path for the container, which contains the recommended configurations to initiate the Prometheus servers. To fix this issue, employ an automated shell script for seamlessly transferring the Prometheus configurations from the Ray container to a shared volume. However, Docker does not support the mounting of symbolic links on shared volumes and you may fail to load the Prometheus configuration files. In the Ray container, the symbolic link “/tmp/ray/session_latest/metrics” points to the latest active Ray session. Loading Ray Prometheus configurations with Docker Compose # See these instructions for how to override the restriction and install or run the application. Users can manually override this requirement. Many developers are not on macOS’s trusted list. When downloading binaries from the internet, macOS requires that the binary be signed by a trusted developer ID. macOS does not trust the developer to install Prometheus # You can then start or restart the services with brew services start prometheus. Instead, change the –config-file line in /usr/local/etc/prometheus.args to read -config.file /tmp/ray/session_latest/metrics/prometheus/prometheus.yml. To configure these services, you cannot simply pass in the config files as command line arguments. Homebrew installs Prometheus as a service that is automatically launched for you. Now, you can access Ray metrics from the default Prometheus URL, Troubleshooting # Using Ray configurations in Prometheus with Homebrew on macOS X # See the “Troubleshooting” guide below to fix the issue. If you are using macOS, you may receive an error at this point about trying to launch an application where the developer has not been verified. Users need to decide where to host and configure it to scrape the metrics from Clusters. Ray doesn’t start Prometheus servers for users. Use Prometheus to scrape metrics from Ray Clusters. View adding application metrics for how to record metrics. View system metrics for more details about the emitted metrics.Īpplication metrics: Application-specific metrics are useful for monitoring your application states. System metrics: Ray exports a number of system metrics. Dashboard agent process is responsible for aggregating and reporting metrics to the endpoints for Prometheus to scrape. Ray exports metrics if you use ray or other installation commands that include Dashboard component. This page provides instructions on how to collect and monitor metrics from Ray Clusters. Users need to manage the lifecycle of the metrics by themselves. Ray does not provide a native storage solution for metrics. Ray records and emits time-series metrics using the Prometheus format. For example, you may want to access a node’s metrics if it terminates unexpectedly. Metrics are useful for monitoring and troubleshooting Ray applications and Clusters.
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