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Production Guide

doc/source/serve/production-guide/index.md

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(serve-in-production)=

Production Guide

{toctree}
:hidden:

config
kubernetes
docker
fault-tolerance
handling-dependencies
best-practices

The recommended way to run Ray Serve in production is on Kubernetes using the KubeRay RayService custom resource. The RayService custom resource automatically handles important production requirements such as health checking, status reporting, failure recovery, and upgrades. If you're not running on Kubernetes, you can also run Ray Serve on a Ray cluster directly using the Serve CLI.

This section will walk you through a quickstart of how to generate a Serve config file and deploy it using the Serve CLI. For more details, you can check out the other pages in the production guide:

For deploying on VMs instead of Kubernetes, see Deploy on VM.

(serve-in-production-example)=

Working example: Text summarization and translation application

Throughout the production guide, we will use the following Serve application as a working example. The application takes in a string of text in English, then summarizes and translates it into French (default), German, or Romanian.

{literalinclude}
:language: python
:start-after: __example_start__
:end-before: __example_end__

Save this code locally in text_ml.py. In development, we would likely use the serve run command to iteratively run, develop, and repeat (see the Development Workflow for more information). When we're ready to go to production, we will generate a structured config file that acts as the single source of truth for the application.

This config file can be generated using serve build:

$ serve build text_ml:app -o serve_config.yaml

The generated version of this file contains an import_path, runtime_env, and configuration options for each deployment in the application. The application needs the torch and transformers packages, so modify the runtime_env field of the generated config to include these two pip packages. Save this config locally in serve_config.yaml.

yaml
proxy_location: EveryNode

http_options:
  host: 0.0.0.0
  port: 8000

applications:
- name: default
  route_prefix: /
  import_path: text_ml:app
  runtime_env:
    pip:
      - torch
      - transformers
  deployments:
  - name: Translator
    num_replicas: 1
    user_config:
      language: french
  - name: Summarizer
    num_replicas: 1

You can use serve deploy to deploy the application to a local Ray cluster and serve status to get the status at runtime:

console
# Start a local Ray cluster.
ray start --head

# Deploy the Text ML application to the local Ray cluster.
serve deploy serve_config.yaml
2022-08-16 12:51:22,043 SUCC scripts.py:180 --
Sent deploy request successfully!
 * Use `serve status` to check deployments' statuses.
 * Use `serve config` to see the running app's config.

$ serve status
proxies:
  cef533a072b0f03bf92a6b98cb4eb9153b7b7c7b7f15954feb2f38ec: HEALTHY
applications:
  default:
    status: RUNNING
    message: ''
    last_deployed_time_s: 1694041157.2211847
    deployments:
      Translator:
        status: HEALTHY
        replica_states:
          RUNNING: 1
        message: ''
      Summarizer:
        status: HEALTHY
        replica_states:
          RUNNING: 1
        message: ''

Test the application using Python requests:

{literalinclude}
:language: python
:start-after: __start_client__
:end-before: __end_client__

To update the application, modify the config file and use serve deploy again.

Next Steps

For a deeper dive into how to deploy, update, and monitor Serve applications, see the following pages: