mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
553 lines
21 KiB
Python
553 lines
21 KiB
Python
import asyncio
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import datetime
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import json
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import logging
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import os
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import socket
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import subprocess
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import sys
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import traceback
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import warnings
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import aioredis
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import ray
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import ray.gcs_utils
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import ray.new_dashboard.modules.reporter.reporter_consts as reporter_consts
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from ray.new_dashboard import k8s_utils
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import ray.new_dashboard.utils as dashboard_utils
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import ray._private.services
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import ray._private.utils
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from ray.core.generated import reporter_pb2
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from ray.core.generated import reporter_pb2_grpc
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from ray.autoscaler._private.util import (DEBUG_AUTOSCALING_STATUS)
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from ray._private.metrics_agent import MetricsAgent, Gauge, Record
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from ray.util.debug import log_once
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import psutil
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logger = logging.getLogger(__name__)
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# Are we in a K8s pod?
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IN_KUBERNETES_POD = "KUBERNETES_SERVICE_HOST" in os.environ
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try:
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import gpustat.core as gpustat
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except (ModuleNotFoundError, ImportError):
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gpustat = None
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if log_once("gpustat_import_warning"):
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warnings.warn("`gpustat` package is not installed. GPU monitoring is "
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"not available. To have full functionality of the "
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"dashboard please install `pip install ray["
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"default]`.)")
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def recursive_asdict(o):
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if isinstance(o, tuple) and hasattr(o, "_asdict"):
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return recursive_asdict(o._asdict())
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if isinstance(o, (tuple, list)):
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L = []
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for k in o:
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L.append(recursive_asdict(k))
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return L
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if isinstance(o, dict):
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D = {k: recursive_asdict(v) for k, v in o.items()}
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return D
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return o
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def jsonify_asdict(o):
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return json.dumps(dashboard_utils.to_google_style(recursive_asdict(o)))
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# A list of gauges to record and export metrics.
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METRICS_GAUGES = {
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"node_cpu_utilization": Gauge("node_cpu_utilization",
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"Total CPU usage on a ray node",
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"percentage", ["ip"]),
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"node_cpu_count": Gauge("node_cpu_count",
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"Total CPUs available on a ray node", "cores",
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["ip"]),
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"node_mem_used": Gauge("node_mem_used", "Memory usage on a ray node",
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"bytes", ["ip"]),
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"node_mem_available": Gauge("node_mem_available",
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"Memory available on a ray node", "bytes",
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["ip"]),
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"node_mem_total": Gauge("node_mem_total", "Total memory on a ray node",
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"bytes", ["ip"]),
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"node_gpus_available": Gauge("node_gpus_available",
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"Total GPUs available on a ray node",
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"percentage", ["ip"]),
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"node_gpus_utilization": Gauge("node_gpus_utilization",
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"Total GPUs usage on a ray node",
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"percentage", ["ip"]),
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"node_gram_used": Gauge("node_gram_used",
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"Total GPU RAM usage on a ray node", "bytes",
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["ip"]),
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"node_gram_available": Gauge("node_gram_available",
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"Total GPU RAM available on a ray node",
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"bytes", ["ip"]),
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"node_disk_usage": Gauge("node_disk_usage",
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"Total disk usage (bytes) on a ray node", "bytes",
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["ip"]),
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"node_disk_free": Gauge("node_disk_free",
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"Total disk free (bytes) on a ray node", "bytes",
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["ip"]),
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"node_disk_utilization_percentage": Gauge(
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"node_disk_utilization_percentage",
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"Total disk utilization (percentage) on a ray node", "percentage",
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["ip"]),
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"node_network_sent": Gauge("node_network_sent", "Total network sent",
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"bytes", ["ip"]),
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"node_network_received": Gauge("node_network_received",
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"Total network received", "bytes", ["ip"]),
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"node_network_send_speed": Gauge(
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"node_network_send_speed", "Network send speed", "bytes/sec", ["ip"]),
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"node_network_receive_speed": Gauge("node_network_receive_speed",
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"Network receive speed", "bytes/sec",
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["ip"]),
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"raylet_cpu": Gauge("raylet_cpu", "CPU usage of the raylet on a node.",
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"percentage", ["ip", "pid"]),
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"raylet_mem": Gauge("raylet_mem", "Memory usage of the raylet on a node",
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"mb", ["ip", "pid"]),
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"cluster_active_nodes": Gauge("cluster_active_nodes",
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"Active nodes on the cluster", "count",
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["node_type"]),
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"cluster_failed_nodes": Gauge("cluster_failed_nodes",
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"Failed nodes on the cluster", "count",
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["node_type"]),
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"cluster_pending_nodes": Gauge("cluster_pending_nodes",
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"Pending nodes on the cluster", "count",
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["node_type"]),
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}
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class ReporterAgent(dashboard_utils.DashboardAgentModule,
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reporter_pb2_grpc.ReporterServiceServicer):
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"""A monitor process for monitoring Ray nodes.
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Attributes:
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dashboard_agent: The DashboardAgent object contains global config
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"""
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def __init__(self, dashboard_agent):
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"""Initialize the reporter object."""
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super().__init__(dashboard_agent)
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if IN_KUBERNETES_POD:
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# psutil does not compute this correctly when in a K8s pod.
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# Use ray._private.utils instead.
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cpu_count = ray._private.utils.get_num_cpus()
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self._cpu_counts = (cpu_count, cpu_count)
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else:
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self._cpu_counts = (psutil.cpu_count(),
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psutil.cpu_count(logical=False))
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self._ip = ray.util.get_node_ip_address()
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self._redis_address, _ = dashboard_agent.redis_address
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self._is_head_node = (self._ip == self._redis_address)
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self._hostname = socket.gethostname()
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self._workers = set()
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self._network_stats_hist = [(0, (0.0, 0.0))] # time, (sent, recv)
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self._metrics_agent = MetricsAgent(dashboard_agent.metrics_export_port)
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self._key = f"{reporter_consts.REPORTER_PREFIX}" \
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f"{self._dashboard_agent.node_id}"
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async def GetProfilingStats(self, request, context):
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pid = request.pid
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duration = request.duration
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profiling_file_path = os.path.join(
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ray._private.utils.get_ray_temp_dir(), f"{pid}_profiling.txt")
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sudo = "sudo" if ray._private.utils.get_user() != "root" else ""
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process = await asyncio.create_subprocess_shell(
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f"{sudo} $(which py-spy) record "
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f"-o {profiling_file_path} -p {pid} -d {duration} -f speedscope",
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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shell=True)
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stdout, stderr = await process.communicate()
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if process.returncode != 0:
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profiling_stats = ""
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else:
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with open(profiling_file_path, "r") as f:
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profiling_stats = f.read()
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return reporter_pb2.GetProfilingStatsReply(
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profiling_stats=profiling_stats, std_out=stdout, std_err=stderr)
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async def ReportOCMetrics(self, request, context):
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# This function receives a GRPC containing OpenCensus (OC) metrics
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# from a Ray process, then exposes those metrics to Prometheus.
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try:
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self._metrics_agent.record_metric_points_from_protobuf(
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request.metrics)
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except Exception:
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logger.error(traceback.format_exc())
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return reporter_pb2.ReportOCMetricsReply()
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@staticmethod
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def _get_cpu_percent():
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if IN_KUBERNETES_POD:
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return k8s_utils.cpu_percent()
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else:
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return psutil.cpu_percent()
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@staticmethod
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def _get_gpu_usage():
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if gpustat is None:
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return []
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gpu_utilizations = []
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gpus = []
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try:
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gpus = gpustat.new_query().gpus
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except Exception as e:
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logger.debug(f"gpustat failed to retrieve GPU information: {e}")
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for gpu in gpus:
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# Note the keys in this dict have periods which throws
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# off javascript so we change .s to _s
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gpu_data = {
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"_".join(key.split(".")): val
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for key, val in gpu.entry.items()
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}
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gpu_utilizations.append(gpu_data)
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return gpu_utilizations
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@staticmethod
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def _get_boot_time():
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if IN_KUBERNETES_POD:
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# Return start time of container entrypoint
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return psutil.Process(pid=1).create_time()
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else:
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return psutil.boot_time()
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@staticmethod
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def _get_network_stats():
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ifaces = [
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v for k, v in psutil.net_io_counters(pernic=True).items()
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if k[0] == "e"
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]
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sent = sum((iface.bytes_sent for iface in ifaces))
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recv = sum((iface.bytes_recv for iface in ifaces))
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return sent, recv
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@staticmethod
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def _get_mem_usage():
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total = ray._private.utils.get_system_memory()
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used = ray._private.utils.get_used_memory()
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available = total - used
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percent = round(used / total, 3) * 100
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return total, available, percent, used
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@staticmethod
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def _get_disk_usage():
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dirs = [
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os.environ["USERPROFILE"] if sys.platform == "win32" else os.sep,
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ray._private.utils.get_user_temp_dir(),
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]
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if IN_KUBERNETES_POD:
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# If in a K8s pod, disable disk display by passing in dummy values.
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return {
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x: psutil._common.sdiskusage(
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total=1, used=0, free=1, percent=0.0)
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for x in dirs
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}
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else:
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return {x: psutil.disk_usage(x) for x in dirs}
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def _get_workers(self):
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raylet_proc = self._get_raylet_proc()
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if raylet_proc is None:
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return []
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else:
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workers = set(raylet_proc.children())
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self._workers.intersection_update(workers)
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self._workers.update(workers)
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self._workers.discard(psutil.Process())
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return [
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w.as_dict(attrs=[
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"pid",
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"create_time",
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"cpu_percent",
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"cpu_times",
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"cmdline",
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"memory_info",
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]) for w in self._workers if w.status() != psutil.STATUS_ZOMBIE
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]
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@staticmethod
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def _get_raylet_proc():
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try:
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curr_proc = psutil.Process()
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# Here, parent is always raylet because the
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# dashboard agent is a child of the raylet process.
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parent = curr_proc.parent()
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if parent is not None:
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if parent.pid == 1:
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return None
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if parent.status() == psutil.STATUS_ZOMBIE:
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return None
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return parent
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except (psutil.AccessDenied, ProcessLookupError):
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pass
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return None
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def _get_raylet(self):
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raylet_proc = self._get_raylet_proc()
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if raylet_proc is None:
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return {}
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else:
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return raylet_proc.as_dict(attrs=[
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"pid",
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"create_time",
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"cpu_percent",
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"cpu_times",
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"cmdline",
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"memory_info",
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])
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def _get_load_avg(self):
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if sys.platform == "win32":
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cpu_percent = psutil.cpu_percent()
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load = (cpu_percent, cpu_percent, cpu_percent)
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else:
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load = os.getloadavg()
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per_cpu_load = tuple((round(x / self._cpu_counts[0], 2) for x in load))
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return load, per_cpu_load
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def _get_all_stats(self):
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now = dashboard_utils.to_posix_time(datetime.datetime.utcnow())
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network_stats = self._get_network_stats()
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self._network_stats_hist.append((now, network_stats))
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self._network_stats_hist = self._network_stats_hist[-7:]
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then, prev_network_stats = self._network_stats_hist[0]
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prev_send, prev_recv = prev_network_stats
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now_send, now_recv = network_stats
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network_speed_stats = ((now_send - prev_send) / (now - then),
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(now_recv - prev_recv) / (now - then))
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return {
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"now": now,
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"hostname": self._hostname,
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"ip": self._ip,
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"cpu": self._get_cpu_percent(),
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"cpus": self._cpu_counts,
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"mem": self._get_mem_usage(),
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"workers": self._get_workers(),
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"raylet": self._get_raylet(),
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"bootTime": self._get_boot_time(),
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"loadAvg": self._get_load_avg(),
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"disk": self._get_disk_usage(),
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"gpus": self._get_gpu_usage(),
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"network": network_stats,
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"network_speed": network_speed_stats,
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# Deprecated field, should be removed with frontend.
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"cmdline": self._get_raylet().get("cmdline", []),
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}
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def _record_stats(self, stats, cluster_stats):
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records_reported = []
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ip = stats["ip"]
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# -- Instance count of cluster --
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# Only report cluster stats on head node
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if "autoscaler_report" in cluster_stats and self._is_head_node:
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active_nodes = cluster_stats["autoscaler_report"]["active_nodes"]
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for node_type, active_node_count in active_nodes.items():
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records_reported.append(
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Record(
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gauge=METRICS_GAUGES["cluster_active_nodes"],
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value=active_node_count,
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tags={"node_type": node_type}))
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failed_nodes = cluster_stats["autoscaler_report"]["failed_nodes"]
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failed_nodes_dict = {}
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for node_ip, node_type in failed_nodes:
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if node_type in failed_nodes_dict:
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failed_nodes_dict[node_type] += 1
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else:
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failed_nodes_dict[node_type] = 1
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for node_type, failed_node_count in failed_nodes_dict.items():
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records_reported.append(
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Record(
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gauge=METRICS_GAUGES["cluster_failed_nodes"],
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value=failed_node_count,
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tags={"node_type": node_type}))
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pending_nodes = cluster_stats["autoscaler_report"]["pending_nodes"]
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pending_nodes_dict = {}
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for node_ip, node_type, status_message in pending_nodes:
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if node_type in pending_nodes_dict:
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pending_nodes_dict[node_type] += 1
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else:
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pending_nodes_dict[node_type] = 1
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for node_type, pending_node_count in pending_nodes_dict.items():
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records_reported.append(
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Record(
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gauge=METRICS_GAUGES["cluster_pending_nodes"],
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value=pending_node_count,
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tags={"node_type": node_type}))
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# -- CPU per node --
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cpu_usage = float(stats["cpu"])
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cpu_record = Record(
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gauge=METRICS_GAUGES["node_cpu_utilization"],
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value=cpu_usage,
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tags={"ip": ip})
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cpu_count, _ = stats["cpus"]
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cpu_count_record = Record(
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gauge=METRICS_GAUGES["node_cpu_count"],
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value=cpu_count,
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tags={"ip": ip})
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# -- Mem per node --
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mem_total, mem_available, _, mem_used = stats["mem"]
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mem_used_record = Record(
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gauge=METRICS_GAUGES["node_mem_used"],
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value=mem_used,
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tags={"ip": ip})
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mem_available_record = Record(
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gauge=METRICS_GAUGES["node_mem_available"],
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value=mem_available,
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tags={"ip": ip})
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mem_total_record = Record(
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gauge=METRICS_GAUGES["node_mem_total"],
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value=mem_total,
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tags={"ip": ip})
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# -- GPU per node --
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gpus = stats["gpus"]
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gpus_available = len(gpus)
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if gpus_available:
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gpus_utilization, gram_used, gram_total = 0, 0, 0
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for gpu in gpus:
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gpus_utilization += gpu["utilization_gpu"]
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gram_used += gpu["memory_used"]
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gram_total += gpu["memory_total"]
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gram_available = gram_total - gram_used
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gpus_available_record = Record(
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gauge=METRICS_GAUGES["node_gpus_available"],
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value=gpus_available,
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tags={"ip": ip})
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gpus_utilization_record = Record(
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gauge=METRICS_GAUGES["node_gpus_utilization"],
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value=gpus_utilization,
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tags={"ip": ip})
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gram_used_record = Record(
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gauge=METRICS_GAUGES["node_gram_used"],
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value=gram_used,
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tags={"ip": ip})
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gram_available_record = Record(
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gauge=METRICS_GAUGES["node_gram_available"],
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value=gram_available,
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tags={"ip": ip})
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records_reported.extend([
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gpus_available_record, gpus_utilization_record,
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gram_used_record, gram_available_record
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])
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# -- Disk per node --
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used, free = 0, 0
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for entry in stats["disk"].values():
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used += entry.used
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free += entry.free
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disk_utilization = float(used / (used + free)) * 100
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disk_usage_record = Record(
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gauge=METRICS_GAUGES["node_disk_usage"],
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value=used,
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tags={"ip": ip})
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disk_free_record = Record(
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gauge=METRICS_GAUGES["node_disk_free"],
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value=free,
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tags={"ip": ip})
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disk_utilization_percentage_record = Record(
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gauge=METRICS_GAUGES["node_disk_utilization_percentage"],
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value=disk_utilization,
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tags={"ip": ip})
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# -- Network speed (send/receive) stats per node --
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network_stats = stats["network"]
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network_sent_record = Record(
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gauge=METRICS_GAUGES["node_network_sent"],
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value=network_stats[0],
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tags={"ip": ip})
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network_received_record = Record(
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gauge=METRICS_GAUGES["node_network_received"],
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value=network_stats[1],
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tags={"ip": ip})
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# -- Network speed (send/receive) per node --
|
|
network_speed_stats = stats["network_speed"]
|
|
network_send_speed_record = Record(
|
|
gauge=METRICS_GAUGES["node_network_send_speed"],
|
|
value=network_speed_stats[0],
|
|
tags={"ip": ip})
|
|
network_receive_speed_record = Record(
|
|
gauge=METRICS_GAUGES["node_network_receive_speed"],
|
|
value=network_speed_stats[1],
|
|
tags={"ip": ip})
|
|
|
|
raylet_stats = stats["raylet"]
|
|
if raylet_stats:
|
|
raylet_pid = str(raylet_stats["pid"])
|
|
# -- raylet CPU --
|
|
raylet_cpu_usage = float(raylet_stats["cpu_percent"]) * 100
|
|
raylet_cpu_record = Record(
|
|
gauge=METRICS_GAUGES["raylet_cpu"],
|
|
value=raylet_cpu_usage,
|
|
tags={
|
|
"ip": ip,
|
|
"pid": raylet_pid
|
|
})
|
|
|
|
# -- raylet mem --
|
|
raylet_mem_usage = float(raylet_stats["memory_info"].rss) / 1e6
|
|
raylet_mem_record = Record(
|
|
gauge=METRICS_GAUGES["raylet_mem"],
|
|
value=raylet_mem_usage,
|
|
tags={
|
|
"ip": ip,
|
|
"pid": raylet_pid
|
|
})
|
|
records_reported.extend([raylet_cpu_record, raylet_mem_record])
|
|
|
|
records_reported.extend([
|
|
cpu_record, cpu_count_record, mem_used_record,
|
|
mem_available_record, mem_total_record, disk_usage_record,
|
|
disk_free_record, disk_utilization_percentage_record,
|
|
network_sent_record, network_received_record,
|
|
network_send_speed_record, network_receive_speed_record
|
|
])
|
|
return records_reported
|
|
|
|
async def _perform_iteration(self, aioredis_client):
|
|
"""Get any changes to the log files and push updates to Redis."""
|
|
while True:
|
|
try:
|
|
formatted_status_string = await aioredis_client.hget(
|
|
DEBUG_AUTOSCALING_STATUS, "value")
|
|
formatted_status = json.loads(formatted_status_string.decode(
|
|
)) if formatted_status_string else {}
|
|
|
|
stats = self._get_all_stats()
|
|
records_reported = self._record_stats(stats, formatted_status)
|
|
self._metrics_agent.record_reporter_stats(records_reported)
|
|
await aioredis_client.publish(self._key, jsonify_asdict(stats))
|
|
|
|
except Exception:
|
|
logger.exception("Error publishing node physical stats.")
|
|
await asyncio.sleep(
|
|
reporter_consts.REPORTER_UPDATE_INTERVAL_MS / 1000)
|
|
|
|
async def run(self, server):
|
|
aioredis_client = await aioredis.create_redis_pool(
|
|
address=self._dashboard_agent.redis_address,
|
|
password=self._dashboard_agent.redis_password)
|
|
reporter_pb2_grpc.add_ReporterServiceServicer_to_server(self, server)
|
|
await self._perform_iteration(aioredis_client)
|