Ray Monitoring

To help monitoring Ray applications, Ray

  • Collects Ray’s pre-selected system level metrics.

  • Exposes metrics in a Prometheus format. We’ll call the endpoint to access these metrics a Prometheus endpoint.

  • Support custom metrics APIs that resemble Prometheus metric types.

This page describes how to acces these metrics using Prometheus.

Note

It is currently an experimental feature and under active development. APIs are subject to change.

Getting Started (Single Node)

Ray exposes its metrics in Prometheus format. This allows us to easily scrape them using Prometheus.

Let’s expose metrics through ray start.

ray start --head --metrics-export-port=8080 # Assign metrics export port on a head node.

Now, you can scrape Ray’s metrics using Prometheus.

First, download Prometheus. Download Link

tar xvfz prometheus-*.tar.gz
cd prometheus-*

Let’s modify Prometheus’s config file to scrape metrics from Prometheus endpoints.

# prometheus.yml
global:
scrape_interval:     5s
evaluation_interval: 5s

scrape_configs:
- job_name: prometheus
    static_configs:
    - targets: ['localhost:8080'] # This must be same as metrics_export_port

Next, let’s start Prometheus.

./prometheus --config.file=./prometheus.yml

Now, you can access Ray metrics from the default Prometheus url, http://localhost:9090.

Getting Started (Multi-nodes)

Let’s now walk through how to import metrics from a Ray cluster.

Ray runs a metrics agent per node. Each metrics agent collects metrics from a local node and exposes in a Prometheus format. You can then scrape each endpoint to access Ray’s metrics.

At a head node,

ray start --head --metrics-export-port=8080 # Assign metrics export port on a head node.

At a worker node,

ray start --address=[head_node_address] --metrics-export-port=8080

You can now get the url of metrics agents using ray.nodes()

# In a head node,
import ray
ray.init(address='auto')
from pprint import pprint
pprint(ray.nodes())

"""
[{'Alive': True,
  'MetricsExportPort': 8080,
  'NodeID': '2f480984702a22556b90566bdac818a4a771e69a',
  'NodeManagerAddress': '192.168.1.82',
  'NodeManagerHostname': 'host2.attlocal.net',
  'NodeManagerPort': 61760,
  'ObjectManagerPort': 61454,
  'ObjectStoreSocketName': '/tmp/ray/session_2020-08-04_18-18-16_481195_34255/sockets/plasma_store',
  'RayletSocketName': '/tmp/ray/session_2020-08-04_18-18-16_481195_34255/sockets/raylet',
  'Resources': {'CPU': 1.0,
                'memory': 123.0,
                'node:192.168.1.82': 1.0,
                'object_store_memory': 2.0},
  'alive': True},
{'Alive': True,
 'MetricsExportPort': 8080,
 'NodeID': 'ce6f30a7e2ef58c8a6893b3df171bcd464b33c77',
 'NodeManagerAddress': '192.168.1.82',
 'NodeManagerHostname': 'host1.attlocal.net',
 'NodeManagerPort': 62052,
 'ObjectManagerPort': 61468,
 'ObjectStoreSocketName': '/tmp/ray/session_2020-08-04_18-18-16_481195_34255/sockets/plasma_store.1',
 'RayletSocketName': '/tmp/ray/session_2020-08-04_18-18-16_481195_34255/sockets/raylet.1',
 'Resources': {'CPU': 1.0,
                'memory': 134.0,
                'node:192.168.1.82': 1.0,
                'object_store_memory': 2.0},
 'alive': True}]
"""

Now, setup your prometheus to read metrics from [NodeManagerAddress]:[MetricsExportPort] from all nodes in the cluster. If you’d like to make this process automated, you can also use file based service discovery. This will allow Prometheus to dynamically find endpoints it should scrape (service discovery). You can easily get all endpoints using ray.nodes()

Getting Started (Cluster Launcher)

When you use a Ray cluster launcher, it is common node IP addresses are changing because cluster is scaling up and down. In this case, you can use Prometheus’ file based service discovery.

Prometheus Service Discovery Support

Ray auto-generates a Prometheus service discovery file in a head node to help metrics agents’ service discovery. This allows you to easily scrape all metrics at each node in autoscaling clusters. Let’s walkthrough how to acheive this.

The service discovery file is generated in a head node. Note that head node is a node where you started by ray start –head or ran ray.init().

Inside a head node, check out a temp_dir of Ray. By default, it is /tmp/ray (in both Linux and MacOS). You should be able to find a file prom_metrics_service_discovery.json. Ray periodically updates the addresses of all metrics agents in a cluster to this file.

Now, modify a Prometheus config to scrape the file for service discovery.

# Prometheus config file

# my global config
global:
  scrape_interval:     2s
  evaluation_interval: 2s

# A scrape configuration containing exactly one endpoint to scrape:
# Here it's Prometheus itself.
scrape_configs:
- job_name: 'ray'
  file_sd_configs:
  - files:
    - '/tmp/ray/prom_metrics_service_discovery.json'

Prometheus will automatically detect that the file contents are changing and update addresses it scrapes to based on the service discovery file generated by Ray.

Custom Metrics

Ray supports custom metrics APIs to enable developers to have visibility to their applications.

It current supports 3 metric types. All metric types have the same definition as Prometheus metric types.

Custom Metrics APIs Package Reference