feat: HSAP platform v2 — modular navigation, quality review, audit log, world model simulation
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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from __future__ import annotations
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import concurrent.futures
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import statistics
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import time
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class GCPRegions:
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"""A class for managing and analyzing Google Cloud Platform (GCP) regions.
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This class provides functionality to initialize, categorize, and analyze GCP regions based on their geographical
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location, tier classification, and network latency.
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Attributes:
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regions (dict[str, tuple[int, str, str]]): A dictionary of GCP regions with their tier, city, and country.
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Methods:
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tier1: Returns a list of tier 1 GCP regions.
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tier2: Returns a list of tier 2 GCP regions.
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lowest_latency: Determines the GCP region(s) with the lowest network latency.
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Examples:
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>>> from ultralytics.hub.google import GCPRegions
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>>> regions = GCPRegions()
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>>> lowest_latency_region = regions.lowest_latency(verbose=True, attempts=3)
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>>> print(f"Lowest latency region: {lowest_latency_region[0][0]}")
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"""
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def __init__(self):
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"""Initialize the GCPRegions class with predefined Google Cloud Platform regions and their details."""
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self.regions = {
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"asia-east1": (1, "Taiwan", "China"),
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"asia-east2": (2, "Hong Kong", "China"),
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"asia-northeast1": (1, "Tokyo", "Japan"),
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"asia-northeast2": (1, "Osaka", "Japan"),
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"asia-northeast3": (2, "Seoul", "South Korea"),
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"asia-south1": (2, "Mumbai", "India"),
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"asia-south2": (2, "Delhi", "India"),
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"asia-southeast1": (2, "Jurong West", "Singapore"),
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"asia-southeast2": (2, "Jakarta", "Indonesia"),
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"australia-southeast1": (2, "Sydney", "Australia"),
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"australia-southeast2": (2, "Melbourne", "Australia"),
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"europe-central2": (2, "Warsaw", "Poland"),
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"europe-north1": (1, "Hamina", "Finland"),
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"europe-southwest1": (1, "Madrid", "Spain"),
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"europe-west1": (1, "St. Ghislain", "Belgium"),
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"europe-west10": (2, "Berlin", "Germany"),
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"europe-west12": (2, "Turin", "Italy"),
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"europe-west2": (2, "London", "United Kingdom"),
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"europe-west3": (2, "Frankfurt", "Germany"),
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"europe-west4": (1, "Eemshaven", "Netherlands"),
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"europe-west6": (2, "Zurich", "Switzerland"),
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"europe-west8": (1, "Milan", "Italy"),
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"europe-west9": (1, "Paris", "France"),
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"me-central1": (2, "Doha", "Qatar"),
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"me-west1": (1, "Tel Aviv", "Israel"),
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"northamerica-northeast1": (2, "Montreal", "Canada"),
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"northamerica-northeast2": (2, "Toronto", "Canada"),
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"southamerica-east1": (2, "São Paulo", "Brazil"),
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"southamerica-west1": (2, "Santiago", "Chile"),
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"us-central1": (1, "Iowa", "United States"),
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"us-east1": (1, "South Carolina", "United States"),
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"us-east4": (1, "Northern Virginia", "United States"),
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"us-east5": (1, "Columbus", "United States"),
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"us-south1": (1, "Dallas", "United States"),
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"us-west1": (1, "Oregon", "United States"),
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"us-west2": (2, "Los Angeles", "United States"),
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"us-west3": (2, "Salt Lake City", "United States"),
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"us-west4": (2, "Las Vegas", "United States"),
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}
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def tier1(self) -> list[str]:
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"""Return a list of GCP regions classified as tier 1 based on predefined criteria."""
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return [region for region, info in self.regions.items() if info[0] == 1]
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def tier2(self) -> list[str]:
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"""Return a list of GCP regions classified as tier 2 based on predefined criteria."""
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return [region for region, info in self.regions.items() if info[0] == 2]
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@staticmethod
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def _ping_region(region: str, attempts: int = 1) -> tuple[str, float, float, float, float]:
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"""Ping a specified GCP region and measure network latency statistics.
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Args:
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region (str): The GCP region identifier to ping (e.g., 'us-central1').
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attempts (int, optional): Number of ping attempts to make for calculating statistics.
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Returns:
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region (str): The GCP region identifier that was pinged.
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mean_latency (float): Mean latency in milliseconds, or infinity if all pings failed.
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std_dev (float): Standard deviation of latencies in milliseconds, or infinity if all pings failed.
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min_latency (float): Minimum latency in milliseconds, or infinity if all pings failed.
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max_latency (float): Maximum latency in milliseconds, or infinity if all pings failed.
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Examples:
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>>> region, mean, std, min_lat, max_lat = GCPRegions._ping_region("us-central1", attempts=3)
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>>> print(f"Region {region} has mean latency: {mean:.2f}ms")
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"""
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import requests # scoped as slow import
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url = f"https://{region}-docker.pkg.dev"
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latencies = []
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for _ in range(attempts):
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try:
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start_time = time.time()
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_ = requests.head(url, timeout=5)
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latency = (time.time() - start_time) * 1000 # Convert latency to milliseconds
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if latency != float("inf"):
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latencies.append(latency)
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except requests.RequestException:
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pass
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if not latencies:
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return region, float("inf"), float("inf"), float("inf"), float("inf")
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std_dev = statistics.stdev(latencies) if len(latencies) > 1 else 0
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return region, statistics.mean(latencies), std_dev, min(latencies), max(latencies)
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def lowest_latency(
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self,
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top: int = 1,
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verbose: bool = False,
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tier: int | None = None,
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attempts: int = 1,
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) -> list[tuple[str, float, float, float, float]]:
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"""Determine the GCP regions with the lowest latency based on ping tests.
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Args:
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top (int, optional): Number of top regions to return.
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verbose (bool, optional): If True, prints detailed latency information for all tested regions.
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tier (int | None, optional): Filter regions by tier (1 or 2). If None, all regions are tested.
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attempts (int, optional): Number of ping attempts per region.
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Returns:
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(list[tuple[str, float, float, float, float]]): List of tuples containing region information and latency
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statistics. Each tuple contains (region, mean_latency, std_dev, min_latency, max_latency).
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Examples:
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>>> regions = GCPRegions()
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>>> results = regions.lowest_latency(top=3, verbose=True, tier=1, attempts=2)
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>>> print(results[0][0]) # Print the name of the lowest latency region
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"""
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if verbose:
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print(f"Testing GCP regions for latency (with {attempts} {'retry' if attempts == 1 else 'attempts'})...")
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regions_to_test = [k for k, v in self.regions.items() if v[0] == tier] if tier else list(self.regions.keys())
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with concurrent.futures.ThreadPoolExecutor(max_workers=50) as executor:
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results = list(executor.map(lambda r: self._ping_region(r, attempts), regions_to_test))
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sorted_results = sorted(results, key=lambda x: x[1])
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if verbose:
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print(f"{'Region':<25} {'Location':<35} {'Tier':<5} Latency (ms)")
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for region, mean, std, min_, max_ in sorted_results:
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tier, city, country = self.regions[region]
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location = f"{city}, {country}"
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if mean == float("inf"):
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print(f"{region:<25} {location:<35} {tier:<5} Timeout")
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else:
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print(f"{region:<25} {location:<35} {tier:<5} {mean:.0f} ± {std:.0f} ({min_:.0f} - {max_:.0f})")
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print(f"\nLowest latency region{'s' if top > 1 else ''}:")
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for region, mean, std, min_, max_ in sorted_results[:top]:
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tier, city, country = self.regions[region]
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location = f"{city}, {country}"
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print(f"{region} ({location}, {mean:.0f} ± {std:.0f} ms ({min_:.0f} - {max_:.0f}))")
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return sorted_results[:top]
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# Usage example
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if __name__ == "__main__":
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regions = GCPRegions()
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top_3_latency_tier1 = regions.lowest_latency(top=3, verbose=True, tier=1, attempts=3)
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