mirror of
https://github.com/wassname/ray.git
synced 2026-06-30 01:06:07 +08:00
Ian Rodney
5bc2ba38fd
Co-authored-by: simon-mo <simon.mo@hey.com> Co-authored-by: Richard Liaw <rliaw@berkeley.edu> Co-authored-by: Alex Wu <itswu.alex@gmail.com>
103 lines
2.8 KiB
Python
103 lines
2.8 KiB
Python
# yapf: disable
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# __doc_import_begin__
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import ray
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from ray import serve
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from typing import List
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import time
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import numpy as np
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import requests
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# __doc_import_end__
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# yapf: enable
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# __doc_define_servable_v0_begin__
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@serve.accept_batch
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def batch_adder_v0(flask_requests: List):
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numbers = [int(request.args["number"]) for request in flask_requests]
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input_array = np.array(numbers)
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print("Our input array has shape:", input_array.shape)
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# Sleep for 200ms, this could be performing CPU intensive computation
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# in real models
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time.sleep(0.2)
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output_array = input_array + 1
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return output_array.astype(int).tolist()
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# __doc_define_servable_v0_end__
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ray.init(num_cpus=8)
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# __doc_deploy_begin__
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client = serve.start()
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client.create_backend("adder:v0", batch_adder_v0, config={"max_batch_size": 4})
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client.create_endpoint(
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"adder", backend="adder:v0", route="/adder", methods=["GET"])
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# __doc_deploy_end__
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# __doc_query_begin__
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@ray.remote
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def send_query(number):
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resp = requests.get("http://localhost:8000/adder?number={}".format(number))
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return int(resp.text)
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# Let's use Ray to send all queries in parallel
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results = ray.get([send_query.remote(i) for i in range(9)])
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print("Result returned:", results)
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# Output
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# (pid=...) Our input array has shape: (1,)
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# (pid=...) Our input array has shape: (4,)
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# (pid=...) Our input array has shape: (4,)
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# Result returned: [1, 2, 3, 4, 5, 6, 7, 8, 9]
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# __doc_query_end__
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# __doc_define_servable_v1_begin__
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@serve.accept_batch
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def batch_adder_v1(requests: List):
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numbers = [int(request.args["number"]) for request in requests]
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input_array = np.array(numbers)
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print("Our input array has shape:", input_array.shape)
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# Sleep for 200ms, this could be performing CPU intensive computation
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# in real models
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time.sleep(0.2)
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output_array = input_array + 1
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return output_array.astype(int).tolist()
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# __doc_define_servable_v1_end__
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# __doc_deploy_v1_begin__
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client.create_backend("adder:v1", batch_adder_v1, config={"max_batch_size": 4})
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client.set_traffic("adder", {"adder:v1": 1})
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# __doc_deploy_v1_end__
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# __doc_query_handle_begin__
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handle = client.get_handle("adder")
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print(handle)
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# Output
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# RayServeHandle(
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# Endpoint="adder",
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# Traffic={'adder:v1': 1}
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# )
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input_batch = list(range(9))
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print("Input batch is", input_batch)
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# Input batch is [0, 1, 2, 3, 4, 5, 6, 7, 8]
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result_batch = ray.get([handle.remote(number=i) for i in input_batch])
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# Output
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# (pid=...) Current context is python
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# (pid=...) Our input array has shape: (1,)
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# (pid=...) Current context is python
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# (pid=...) Our input array has shape: (4,)
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# (pid=...) Current context is python
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# (pid=...) Our input array has shape: (4,)
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print("Result batch is", result_batch)
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# Result batch is [1, 2, 3, 4, 5, 6, 7, 8, 9]
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# __doc_query_handle_end__
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