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import ray | |
from ray import serve | |
ray.init(address='auto', namespace="serve-example", ignore_reinit_error=True) | |
serve.start(detached=True) | |
SentimentDeployment.deploy() |
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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)) |
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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. |
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import requests | |
input_text = "Ray Serve eases the pain of model serving" | |
result = requests.get("http://127.0.0.1:8000/sentiment", data=input_text).text | |
print("Result for '{}': {}".format(input_text, result)) |
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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() |
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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) |
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import ray | |
from ray import serve | |
# Connect to the running Ray Serve instance. | |
ray.init(address='auto', ignore_reinit_error=True) | |
serve.init() | |
# Deploy the model. | |
serve.create_backend("sklearn_backend", SKLearnBackend) | |
serve.create_endpoint("sentiment_endpoint", backend="sklearn_backend", route="/sentiment") |
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import requests | |
from ray import serve | |
serve.init() | |
# Main concepts | |
## Endpoints |
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import argparse | |
import time | |
import random | |
import math | |
parser = argparse.ArgumentParser(description="Approximate digits of Pi using Monte Carlo simulation.") | |
parser.add_argument("--num-samples", type=int, default=1000000) | |
parser.add_argument("--parallel", default=False, action="store_true") | |
parser.add_argument("--distributed", default=False, action="store_true") |
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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 |