Monitoring Ray Serve

This section helps you debug and monitor your Serve applications by:

• viewing the Ray dashboard

• using Ray logging and Loki

• inspecting built-in Ray Serve metrics

• exporting metrics into Arize platform

Ray dashboard

You can use the Ray dashboard to get a high-level overview of your Ray cluster and Ray Serve application’s states. This includes details such as:

• the number of deployment replicas currently running

• logs for your Serve controller, deployment replicas, and HTTP proxies

• the Ray nodes (i.e. machines) running in your Ray cluster.

You can access the Ray dashboard at port 8265 at your cluster’s URI. For example, if you’re running Ray Serve locally, you can access the dashboard by going to http://localhost:8265 in your browser.

View important information about your application by accessing the Serve page.

This example has a single-node cluster running a deployment named Translator. This deployment has 2 replicas.

View details of these replicas by browsing the Serve page. On the details page of each replica. From there, you can view metadata about the replica and the logs of the replicas, including the logging and print statements generated by the replica process.

Another useful view is the Actors view. This example Serve application uses four Ray actors:

• 1 Serve controller

• 1 HTTP proxy

• 2 Translator deployment replicas

You can see the details of these entities throughout the Serve page and in the actor’s page. This page includes additional useful information like each actor’s process ID (PID) and a link to each actor’s logs. You can also see whether any particular actor is alive or dead to help you debug potential cluster failures.

Tip

To learn more about the Serve controller actor, the HTTP proxy actor(s), the deployment replicas, and how they all work together, check out the Serve Architecture documentation.

For a detailed overview of the Ray dashboard, see the dashboard documentation.

Ray logging

To understand system-level behavior and to surface application-level details during runtime, you can leverage Ray logging.

Ray Serve uses Python’s standard logging module with a logger named "ray.serve". By default, logs are emitted from actors both to stderr and on disk on each node at /tmp/ray/session_latest/logs/serve/. This includes both system-level logs from the Serve controller and HTTP proxy as well as access logs and custom user logs produced from within deployment replicas.

In development, logs are streamed to the driver Ray program (the Python script that calls serve.run() or the serve run CLI command), so it’s convenient to keep the driver running while debugging.

For example, let’s run a basic Serve application and view the logs that it emits.

First, let’s create a simple deployment that logs a custom log message when it’s queried:

# File name: monitoring.py

from ray import serve
import logging
from starlette.requests import Request

logger = logging.getLogger("ray.serve")


@serve.deployment
class SayHello:
    async def __call__(self, request: Request) -> str:
        logger.info("Hello world!")
        return "hi"


say_hello = SayHello.bind()

Run this deployment using the serve run CLI command:

$ serve run monitoring:say_hello

2023-04-10 15:57:32,100	INFO scripts.py:380 -- Deploying from import path: "monitoring:say_hello".
[2023-04-10 15:57:33]  INFO ray._private.worker::Started a local Ray instance. View the dashboard at http://127.0.0.1:8265 
(ServeController pid=63503) INFO 2023-04-10 15:57:35,822 controller 63503 deployment_state.py:1168 - Deploying new version of deployment SayHello.
(HTTPProxyActor pid=63513) INFO:     Started server process [63513]
(ServeController pid=63503) INFO 2023-04-10 15:57:35,882 controller 63503 deployment_state.py:1386 - Adding 1 replica to deployment SayHello.
2023-04-10 15:57:36,840	SUCC scripts.py:398 -- Deployed Serve app successfully.

serve run prints a few log messages immediately. Note that a few of these messages start with identifiers such as

(ServeController pid=63881)

These messages are logs from Ray Serve actors. They describe which actor (Serve controller, HTTP proxy, or deployment replica) created the log and what its process ID is (which is useful when distinguishing between different deployment replicas or HTTP proxies). The rest of these log messages are the actual log statements generated by the actor.

While serve run is running, we can query the deployment in a separate terminal window:

curl -X GET http://localhost:8000/

This causes the HTTP proxy and deployment replica to print log statements to the terminal running serve run:

(ServeReplica:SayHello pid=63520) INFO 2023-04-10 15:59:45,403 SayHello SayHello#kTBlTj HzIYOzaEgN / monitoring.py:16 - Hello world!
(ServeReplica:SayHello pid=63520) INFO 2023-04-10 15:59:45,403 SayHello SayHello#kTBlTj HzIYOzaEgN / replica.py:527 - __CALL__ OK 0.5ms

Note

Log messages include the logging level, timestamp, deployment name, replica tag, request ID, route, file name, and line number.

Find a copy of these logs at /tmp/ray/session_latest/logs/serve/. You can parse these stored logs with a logging stack such as ELK or Loki to be able to search by deployment or replica.

Serve supports Log Rotation of these logs through setting the environment variables RAY_ROTATION_MAX_BYTES and RAY_ROTATION_BACKUP_COUNT.

To silence the replica-level logs or otherwise configure logging, configure the "ray.serve" logger inside the deployment constructor:

import logging

logger = logging.getLogger("ray.serve")

@serve.deployment
class Silenced:
    def __init__(self):
        logger.setLevel(logging.ERROR)

This controls which logs are written to STDOUT or files on disk. In addition to the standard Python logger, Serve supports custom logging. Custom logging lets you control what messages are written to STDOUT/STDERR, files on disk, or both.

For a detailed overview of logging in Ray, see Ray Logging.

Filtering logs with Loki

You can explore and filter your logs using Loki. Setup and configuration are straightforward on Kubernetes, but as a tutorial, let’s set up Loki manually.

For this walkthrough, you need both Loki and Promtail, which are both supported by Grafana Labs. Follow the installation instructions at Grafana’s website to get executables for Loki and Promtail. For convenience, save the Loki and Promtail executables in the same directory, and then navigate to this directory in your terminal.

Now let’s get your logs into Loki using Promtail.

Save the following file as promtail-local-config.yaml:

server:
  http_listen_port: 9080
  grpc_listen_port: 0

positions:
  filename: /tmp/positions.yaml

clients:
  - url: http://localhost:3100/loki/api/v1/push

scrape_configs:
  - job_name: ray
    static_configs:
      - labels:
        job: ray
        __path__: /tmp/ray/session_latest/logs/serve/*.*

The relevant part for Ray Serve is the static_configs field, where we have indicated the location of our log files with __path__. The expression *.* will match all files, but it won’t match directories since they cause an error with Promtail.

We’ll run Loki locally. Grab the default config file for Loki with the following command in your terminal:

wget https://raw.githubusercontent.com/grafana/loki/v2.1.0/cmd/loki/loki-local-config.yaml

Now start Loki:

./loki-darwin-amd64 -config.file=loki-local-config.yaml

Here you may need to replace ./loki-darwin-amd64 with the path to your Loki executable file, which may have a different name depending on your operating system.

Start Promtail and pass in the path to the config file we saved earlier:

./promtail-darwin-amd64 -config.file=promtail-local-config.yaml

Once again, you may need to replace ./promtail-darwin-amd64 with your Promtail executable.

Run the following Python script to deploy a basic Serve deployment with a Serve deployment logger and to make some requests:

from ray import serve

import logging
import requests

logger = logging.getLogger("ray.serve")


@serve.deployment
class Counter:
    def __init__(self):
        self.count = 0

    def __call__(self, request):
        self.count += 1
        logger.info(f"count: {self.count}")
        return {"count": self.count}


counter = Counter.bind()
serve.run(counter)

for i in range(10):
    requests.get("http://127.0.0.1:8000/")

Now install and run Grafana and navigate to http://localhost:3000, where you can log in with default credentials:

• Username: admin

• Password: admin

On the welcome page, click “Add your first data source” and click “Loki” to add Loki as a data source.

Now click “Explore” in the left-side panel. You are ready to run some queries!

To filter all these Ray logs for the ones relevant to our deployment, use the following LogQL query:

{job="ray"} |= "Counter"

You should see something similar to the following:

You can use Loki to filter your Ray Serve logs and gather insights quicker.

Built-in Ray Serve metrics

You can leverage built-in Ray Serve metrics to get a closer look at your application’s performance.

Ray Serve exposes important system metrics like the number of successful and failed requests through the Ray metrics monitoring infrastructure. By default, the metrics are exposed in Prometheus format on each node.

Note

Different metrics are collected when Deployments are called via Python ServeHandle and when they are called via HTTP.

See the list of metrics below marked for each.

The following metrics are exposed by Ray Serve:

Name	Fields	Description
serve_deployment_request_counter [**]	deployment replica route application	The number of queries that have been processed in this replica.
serve_deployment_error_counter [**]	deployment replica route application	The number of exceptions that have occurred in the deployment.
serve_deployment_replica_starts [**]	deployment replica application	The number of times this replica has been restarted due to failure.
serve_deployment_replica_healthy	deployment replica application	Whether this deployment replica is healthy. 1 means healthy, 0 unhealthy.
serve_deployment_processing_latency_ms [**]	deployment replica route application	The latency for queries to be processed.
serve_replica_processing_queries [**]	deployment replica application	The current number of queries being processed.
serve_num_http_requests [*]	route method application	The number of HTTP requests processed.
serve_num_http_error_requests [*]	route error_code method	The number of non-200 HTTP responses.
serve_num_router_requests [*]	deployment route application	The number of requests processed by the router.
serve_handle_request_counter [**]	handle deployment route application	The number of requests processed by this ServeHandle.
serve_deployment_queued_queries [*]	deployment route	The number of queries for this deployment waiting to be assigned to a replica.
serve_num_deployment_http_error_requests [*]	deployment error_code method route application	The number of non-200 HTTP responses returned by each deployment.
serve_http_request_latency_ms [*]	route application	The end-to-end latency of HTTP requests (measured from the Serve HTTP proxy).
serve_multiplexed_model_load_latency_s	deployment replica application	The time it takes to load a model.
serve_multiplexed_model_unload_latency_s	deployment replica application	The time it takes to unload a model.
serve_num_multiplexed_models	deployment replica application	The number of models loaded on the current replica.
serve_multiplexed_models_unload_counter	deployment replica application	The number of times models unloaded on the current replica.
serve_multiplexed_models_load_counter	deployment replica application	The number of times models loaded on the current replica.

[*] - only available when using HTTP calls [**] - only available when using Python ServeHandle calls

To see this in action, first run the following command to start Ray and set up the metrics export port:

ray start --head --metrics-export-port=8080

Then run the following script:

from ray import serve

import time
import requests


@serve.deployment
def sleeper():
    time.sleep(1)


s = sleeper.bind()

serve.run(s)

while True:
    requests.get("http://localhost:8000/")

The requests loop until canceled with Control-C.

While this script is running, go to localhost:8080 in your web browser. In the output there, you can search for serve_ to locate the metrics above. The metrics are updated once every ten seconds, so you need to refresh the page to see new values.

For example, after running the script for some time and refreshing localhost:8080 you should find metrics similar to the following:

ray_serve_deployment_processing_latency_ms_count{..., replica="sleeper#jtzqhX"} 48.0
ray_serve_deployment_processing_latency_ms_sum{..., replica="sleeper#jtzqhX"} 48160.6719493866

which indicates that the average processing latency is just over one second, as expected.

You can even define a custom metric for your deployment and tag it with deployment or replica metadata. Here’s an example:

from ray import serve
from ray.serve import metrics

import time
import requests


@serve.deployment
class MyDeployment:
    def __init__(self):
        self.num_requests = 0
        self.my_counter = metrics.Counter(
            "my_counter",
            description=("The number of odd-numbered requests to this deployment."),
            tag_keys=("model",),
        )
        self.my_counter.set_default_tags({"model": "123"})

    def __call__(self):
        self.num_requests += 1
        if self.num_requests % 2 == 1:
            self.my_counter.inc()


my_deployment = MyDeployment.bind()
serve.run(my_deployment)

while True:
    requests.get("http://localhost:8000/")
    time.sleep(1)

The emitted logs include:

# HELP ray_my_counter The number of odd-numbered requests to this deployment.
# TYPE ray_my_counter gauge
ray_my_counter{..., deployment="MyDeployment",model="123",replica="MyDeployment#rUVqKh"} 5.0

See the Ray Metrics documentation for more details, including instructions for scraping these metrics using Prometheus.

Exporting metrics into Arize

Besides using Prometheus to check out Ray metrics, Ray Serve also has the flexibility to export the metrics into other observability platforms.

Arize is a machine learning observability platform which can help you to monitor real-time model performance, root cause model failures/performance degradation using explainability & slice analysis and surface drift, data quality, data consistency issues etc.

To integrate with Arize, add Arize client code directly into your Serve deployment code. (Example code)