mirror of
https://github.com/vale981/ray
synced 2025-03-08 11:31:40 -05:00
58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
import time
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import ray
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from ray.util.metrics import Counter, Gauge, Histogram
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ray.init(_metrics_export_port=8080)
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@ray.remote
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class MyActor:
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def __init__(self, name):
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self._curr_count = 0
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self.counter = Counter(
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"num_requests",
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description="Number of requests processed by the actor.",
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tag_keys=("actor_name", ))
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self.counter.set_default_tags({"actor_name": name})
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self.gauge = Gauge(
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"curr_count",
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description="Current count held by the actor. Goes up and down.",
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tag_keys=("actor_name", ))
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self.gauge.set_default_tags({"actor_name": name})
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self.histogram = Histogram(
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"request_latency",
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description="Latencies of requests in ms.",
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boundaries=[0.1, 1],
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tag_keys=("actor_name", ))
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self.histogram.set_default_tags({"actor_name": name})
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def process_request(self, num):
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start = time.time()
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self._curr_count += num
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# Increment the total request count.
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self.counter.inc()
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# Update the gauge to the new value.
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self.gauge.set(self._curr_count)
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# Record the latency for this request in ms.
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self.histogram.observe(1000 * (time.time() - start))
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return self._curr_count
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print("Starting actor.")
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my_actor = MyActor.remote("my_actor")
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print("Calling actor.")
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my_actor.process_request.remote(-10)
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print("Calling actor.")
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my_actor.process_request.remote(5)
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print("Metrics should be exported.")
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print("See http://localhost:8080 (this may take a few seconds to load).")
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# Sleep so we can look at the metrics before exiting.
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time.sleep(30)
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print("Exiting!")
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