edoakes

import argparse

parser = argparse.ArgumentParser()
parser.add_argument("train_s3_urls")
parser.add_argument("inference_s3_urls")
parser.add_argument("output_path")

import ray
from ray import serve

edoakes

ray.init()

@ray.remote
class Controller:
    def start_me_an_actor(self, cls):
        # Start an actor on behalf of the caller.
        ray.remote(cls).remote()

Controller.options(name="controller", lifetime="detached").remote()

edoakes

cluster_name: monte_carlo_pi

# The number of worker nodes to launch in addition to the head node.
min_workers: 9
max_workers: 9

provider:
 type: aws
 region: us-west-2
 availability_zone: us-west-2a

edoakes

import ray
from ray import serve
ray.init(address='auto', namespace="serve-example", ignore_reinit_error=True) # Connect to the local running Ray cluster.
serve.start(detached=True) # Start the Ray Serve processes within the Ray cluster.

edoakes

import ray
from ray import serve

ray.init(address='auto', namespace="serve-example", ignore_reinit_error=True)
serve.start(detached=True)

SentimentDeployment.deploy()

edoakes

import joblib
import s3fs
import sklearn

@serve.deployment(route_prefix="/sentiment", name="sentiment-deployment")
class SentimentDeployment:
    def __init__(self):
        fs = s3fs.S3FileSystem(anon=True)
        with fs.open('ray-serve-blog/unigram_vectorizer.joblib', 'rb') as f:
            self.vectorizer = joblib.load(f)

edoakes

import ray
from ray import serve

# Connect to the running Ray Serve instance.
ray.init(address='auto', namespace="serve-example", ignore_reinit_error=True)
serve.start(detached=True)

# Deploy the model.
SentimentDeployment.deploy()

edoakes

from transformers import pipeline

@serve.deployment(route_prefix="/sentiment", name="sentiment")
class SentimentDeployment:
    def __init__(self):
        self.classifier = pipeline("sentiment-analysis")

    async def __call__(self, request):
        data = await request.body()
        [result] = self.classifier(str(data))

edoakes

import dash
from dash import dcc, html
from dash.dependencies import Input, Output
import pandas as pd
import plotly.graph_objs as obj
import uvicorn as uvicorn
from fastapi import FastAPI
from starlette.middleware.wsgi import WSGIMiddleware

app = dash.Dash(__name__, requests_pathname_prefix="/dash/")

edoakes

import dash
from dash import dcc, html
from dash.dependencies import Input, Output
import pandas as pd
import plotly.graph_objs as obj
import uvicorn as uvicorn
from fastapi import FastAPI
from starlette.middleware.wsgi import WSGIMiddleware

import ray