Deploy Ray Serve Applications with NodeRed.

This is a prototype - I look forward to hearing your experience, feedback, and ideas for improvements.

Ray is a powerful distributed computing framework for Python that allows you to easily scale your applications. By pairing Ray with Node-RED, you can take advantage of Ray's distributed capabilities to handle large workloads and parallelize tasks, while leaving Node-RED to do what it does best - IO and lightweight data plumbing.

Docs:

Ray.io: Effortless distributed computing with Python
Ray Serve Docs: Run Ray with HTTP endpoints
Ray Serve Examples: Examples of deploying ML Inference to Ray Serve

Install

npm i node-red-contrib-ray-serve

Test Install

For full end-to-end testing (including a local ray cluster and grafana), you can use:

docker-compose up -d --build

You can access:

Node-Red dashboard here.
The Ray dashboard can be found here.
Access Grafana here

Screenshots

Deploy python functions to Ray Serve directly from NodeRed.

Configure your python functions

Add python dependencies and environment variables

Manage Ray servers

Can potentially access the dashboard (by proxying) but I can't get it to require permissions so it's currently disabled.

NOTE: This will overwrite existing apps deployed to Ray Serve. Could be fixed with #42974

You can query available routes:

http://ray:8000//-/routes

Examples

Speed of running ML model on CPU: replicas >> batching > vanilla

Vanilla english-to-french translator

from ray import serve
from transformers import pipeline

@serve.deployment
class Translator:
    def __init__(self):
        self.model = pipeline("translation_en_to_fr", model="t5-small")

    def translate(self, text: str) -> str:
        return self.model(text)[0]["translation_text"]

    async def __call__(self, http_request) -> str:
        english_text: str = await http_request.json()
        return self.translate(english_text)
         
app = Translator.bind()

Using batched inference (up to 8 requests at a time)

import asyncio
from ray import serve
from transformers import pipeline

@serve.deployment
class Translator:
    def __init__(self):
        self.model = pipeline("translation_en_to_fr", model="t5-small")

    def translate(self, text) -> str:
        return [t["translation_text"] for t in self.model(text)]

    @serve.batch(max_batch_size=8, batch_wait_timeout_s=0.1)
    async def __call__(self, requests) -> str:
        text: str = await asyncio.gather(*(r.json() for r in requests))
        return self.translate(text)
                
app = Translator.bind()

Using multiple replicas

from ray import serve
from transformers import pipeline

@serve.deployment(num_replicas=3)
class Translator:
    def __init__(self):
        self.model = pipeline("translation_en_to_fr", model="t5-small")

    def translate(self, text: str) -> str:
        return self.model(text)[0]["translation_text"]

    async def __call__(self, http_request) -> str:
        english_text: str = await http_request.json()
        return self.translate(english_text)
         
app = Translator.bind()

How it Works

This module offers two nodes:

Ray Serve node - a node to store a Ray Serve Application
- Ray Config node (which is basically just a wrapper to store the Ray Dashboard URL and Ray Serve URLs)

When the flow is deployed, this module will gather all of the Ray Serve applications and for each Server, it will

Create zip files of the code for each application and upload them to Ray's GCS storage
- Repeat uploads will be skipped based on a hash of its content
create a Serve application payload
Deploy the applications by doing PUT /api/serve/applications
- Serve Docs: The endpoint declaratively deploys a list of Serve applications. If Serve is already running on the Ray cluster, removes all applications not listed in the new config. If Serve is not running on the Ray cluster, starts Serve. See multi-app config schema for the request’s JSON schema.

When messages come in, they are sent to the deployed Ray Serve HTTP endpoint.

The nice thing about this is you can keep the Ray Serve endpoints private to your cluster and Node-RED can access them from the private network.

Example flow

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node-red-contrib-ray-serve

Deploy Ray Serve Applications with NodeRed.

Install

Test Install

Screenshots

Examples

How it Works

Example flow

Readme

Keywords

Package Sidebar

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node-red-contrib-ray-serve

Deploy Ray Serve Applications with NodeRed.

Install

Test Install

Screenshots

Examples

How it Works

Example flow

Readme

Keywords

Package Sidebar

Install

Repository

Homepage

DownloadsWeekly Downloads

Version

License

Unpacked Size

Total Files

Last publish

Collaborators

Weekly Downloads