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ray-project--ray/doc/source/serve/doc_code/class_based_autoscaling.py
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2026-07-13 13:17:40 +08:00

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Python

# __serve_example_begin__
import json
import tempfile
from ray import serve
from ray.serve.config import AutoscalingConfig, AutoscalingPolicy
# Create a JSON file with the initial target replica count.
# In production this file would be written by an external system.
scaling_file = tempfile.NamedTemporaryFile(
mode="w", suffix=".json", delete=False
)
json.dump({"replicas": 2}, scaling_file)
scaling_file.close()
@serve.deployment(
autoscaling_config=AutoscalingConfig(
min_replicas=1,
max_replicas=10,
upscale_delay_s=3,
downscale_delay_s=10,
policy=AutoscalingPolicy(
policy_function="class_based_autoscaling_policy:FileBasedAutoscalingPolicy",
policy_kwargs={
"file_path": scaling_file.name,
"poll_interval_s": 2.0,
},
),
),
max_ongoing_requests=100,
)
class MyDeployment:
async def __call__(self) -> str:
return "Hello, world!"
app = MyDeployment.bind()
# __serve_example_end__
if __name__ == "__main__":
import requests # noqa
serve.run(app)
resp = requests.get("http://localhost:8000/")
assert resp.text == "Hello, world!"