wehub-resource-sync
65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
import time
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import threading
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from typing import Tuple, Optional
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import ray
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from ray.data._internal.execution.interfaces import ExecutionResources
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class ClusterResourceMonitor:
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"""Monitor and validate cluster resources during benchmark execution.
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This class tracks the peak number of cluster resources during execution.
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This can be used to validate that the autoscaler behaves well.
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"""
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def __init__(self):
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if not ray.is_initialized():
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raise RuntimeError("You must start Ray before using this monitor")
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self._background_thread: Optional[threading.Thread] = None
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self._stop_background_thread_event: Optional[threading.Event] = None
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self._peak_cpu_count: float = 0
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self._peak_gpu_count: float = 0
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def __repr__(self):
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return "ClusterResourceMonitor()"
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def __enter__(self):
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(
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self._background_thread,
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self._stop_background_thread_event,
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) = self._start_background_thread()
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return self
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def get_peak_cluster_resources(self) -> ExecutionResources:
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return ExecutionResources(cpu=self._peak_cpu_count, gpu=self._peak_gpu_count)
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def _start_background_thread(
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self, interval_s: float = 5.0
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) -> Tuple[threading.Thread, threading.Event]:
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stop_event = threading.Event()
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def monitor_cluster_resources():
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while not stop_event.is_set():
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resources = ray.cluster_resources()
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self._peak_cpu_count = max(
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self._peak_cpu_count, resources.get("CPU", 0)
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)
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self._peak_gpu_count = max(
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self._peak_gpu_count, resources.get("GPU", 0)
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)
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time.sleep(interval_s)
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thread = threading.Thread(target=monitor_cluster_resources, daemon=True)
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thread.start()
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return thread, stop_event
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def __exit__(self, exc_type, exc_val, exc_tb):
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if self._background_thread is not None:
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self._stop_background_thread_event.set()
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self._background_thread.join()
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