mirror of
https://github.com/kvcache-ai/sglang.git
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1853 lines
79 KiB
Python
1853 lines
79 KiB
Python
"""
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SGLang CI Consecutive Failures Analyzer
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Monitors GitHub Actions workflows for consecutive test failures and runner issues.
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Detects failure streaks, tracks job health, identifies problematic runners, and generates alerts.
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Features:
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- Analyzes all jobs in PR Test workflow (excluding administrative jobs)
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- Tracks consecutive failure streaks for each job
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- Monitors runner health and failure rates
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- Identifies whether failures are code-related or infrastructure-related
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- Generates detailed reports with actionable recommendations
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Usage:
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python ci_failures_analysis.py --token <GITHUB_TOKEN> --limit 100
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"""
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import argparse
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import json
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import os
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import sys
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import time
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from collections import defaultdict
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from datetime import datetime
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from typing import Dict, List, Optional, Tuple
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import requests
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class SGLangFailuresAnalyzer:
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"""Analyzes consecutive failures in GitHub Actions workflows."""
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def __init__(self, token: str):
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self.token = token
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self.base_url = "https://api.github.com"
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self.repo = "sgl-project/sglang"
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self.headers = {
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"Authorization": f"token {token}",
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"Accept": "application/vnd.github.v3+json",
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"User-Agent": "SGLang-Failures-Analyzer/1.0",
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}
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self.session = requests.Session()
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self.session.headers.update(self.headers)
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# Jobs to EXCLUDE from analysis (administrative/setup jobs, not actual tests)
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self.excluded_jobs = [
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"check-changes",
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"pr-test-finish", # Nvidia workflow teardown
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"pr-test-amd-finish", # AMD workflow teardown
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"call-gate",
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"pr-gate",
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"check-all-jobs",
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]
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def get_recent_runs(
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self,
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limit: int = 500,
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workflow_filter: List[str] = None,
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filters: Optional[Dict[str, str]] = None,
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) -> List[Dict]:
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"""
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Fetch recent workflow runs from GitHub API using workflow file names.
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Args:
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limit: Number of runs to fetch per workflow
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workflow_filter: List of workflow filenames
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filters: Optional dict of API filters (e.g., {"event": "schedule"}, {"branch": "main"})
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"""
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filter_desc = f"workflows: {', '.join(workflow_filter)}"
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if filters:
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filter_desc += f", filters: {filters}"
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print(f"Fetching {limit} runs per workflow ({filter_desc})...")
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all_runs = []
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for workflow_file in workflow_filter:
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print(f"Fetching runs for {workflow_file}...")
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# Use workflow filename directly - much simpler!
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url = f"{self.base_url}/repos/{self.repo}/actions/workflows/{workflow_file}/runs"
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params = {"per_page": min(limit, 100), "status": "completed"}
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# Apply any additional filters
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if filters:
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params.update(filters)
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try:
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response = self.session.get(url, params=params, timeout=30)
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response.raise_for_status()
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data = response.json()
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runs = data.get("workflow_runs", [])
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print(f" Found {len(runs)} runs for {workflow_file}")
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all_runs.extend(runs[:limit])
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except requests.exceptions.RequestException as e:
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print(f"Error fetching runs for {workflow_file}: {e}")
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continue
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print(f"Collected {len(all_runs)} total runs")
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return all_runs
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def get_jobs_for_run(self, run_id: int) -> List[Dict]:
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"""Get all jobs for a specific workflow run, handling pagination."""
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try:
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all_jobs = []
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url = f"{self.base_url}/repos/{self.repo}/actions/runs/{run_id}/jobs"
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params = {"per_page": 100} # Max per page
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while url:
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response = self.session.get(url, params=params, timeout=30)
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response.raise_for_status()
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data = response.json()
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jobs = data.get("jobs", [])
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all_jobs.extend(jobs)
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# Check for next page in Link header
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link_header = response.headers.get("Link", "")
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next_url = None
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if link_header:
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links = link_header.split(", ")
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for link in links:
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if 'rel="next"' in link:
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next_url = link.split(";")[0].strip("<>")
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break
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url = next_url
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params = {} # Clear params for subsequent requests (URL has them)
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return all_jobs
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except requests.exceptions.RequestException as e:
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print(f"Error fetching jobs for run {run_id}: {e}")
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return []
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def analyze_runner_health(
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self, runs: List[Dict]
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) -> Tuple[Dict[str, Dict], Dict[str, Dict], Dict[str, Dict], Dict[str, Dict]]:
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"""
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Analyze runner health by tracking failures per runner and consecutive failure streaks.
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Returns:
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Tuple of (runner_stats, runner_instance_data, runner_streak_data, runner_instance_streak_data)
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- runner_stats: Overall stats per runner (failure rate, total jobs, etc.)
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- runner_instance_data: Per-instance breakdown of failures
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- runner_streak_data: Consecutive failure streaks per runner label
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- runner_instance_streak_data: Consecutive failure streaks per runner instance
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"""
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print("\nAnalyzing runner health and consecutive failures...")
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# Sort runs by created_at (oldest first)
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sorted_runs = sorted(runs, key=lambda x: x.get("created_at", ""))
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# Track runner statistics (overall)
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runner_total_jobs: Dict[str, int] = defaultdict(int)
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runner_failed_jobs: Dict[str, int] = defaultdict(int)
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runner_job_failures: Dict[str, Dict[str, int]] = defaultdict(
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lambda: defaultdict(int)
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)
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runner_job_totals: Dict[str, Dict[str, int]] = defaultdict(
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lambda: defaultdict(int)
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)
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# Track queue times per runner instance (can aggregate for runner labels if needed)
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runner_instance_queue_times: Dict[str, List[float]] = defaultdict(list)
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# Track individual runner instances (runner_name + runner_id)
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runner_instance_stats: Dict[str, Dict] = defaultdict(
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lambda: {"total_jobs": 0, "failed_jobs": 0, "jobs_failed": defaultdict(int)}
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)
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# Track consecutive failures per runner (by labels)
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runner_current_streak: Dict[str, int] = defaultdict(int)
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runner_max_streak: Dict[str, int] = defaultdict(int)
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runner_first_failure_in_streak: Dict[str, Optional[Dict]] = {}
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runner_last_failure_in_streak: Dict[str, Optional[Dict]] = {}
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runner_recovery_info: Dict[str, Optional[Dict]] = {}
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# Track consecutive failures per runner instance
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runner_instance_current_streak: Dict[str, int] = defaultdict(int)
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runner_instance_max_streak: Dict[str, int] = defaultdict(int)
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runner_instance_first_failure: Dict[str, Optional[Dict]] = {}
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runner_instance_last_failure: Dict[str, Optional[Dict]] = {}
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runner_instance_recovery: Dict[str, Optional[Dict]] = {}
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total_runs_processed = len(sorted_runs)
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for i, run in enumerate(sorted_runs, 1):
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if i % 50 == 0 or i == total_runs_processed:
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print(
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f"Processing run {i}/{total_runs_processed} for runner analysis: #{run.get('run_number')}"
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)
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head_commit = run.get("head_commit") or {}
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run_info = {
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"run_number": run.get("run_number"),
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"run_id": run.get("id"),
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"created_at": run.get("created_at"),
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"head_sha": run.get("head_sha", "")[:8],
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"author": head_commit.get("author", {}).get("name", "Unknown"),
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"url": f"https://github.com/{self.repo}/actions/runs/{run.get('id')}",
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}
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pull_requests = run.get("pull_requests", [])
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if pull_requests:
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run_info["pr_number"] = pull_requests[0].get("number")
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# Get jobs for this run
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jobs = self.get_jobs_for_run(run.get("id"))
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# Track whether each runner had at least one failure in this run
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runner_had_failure: Dict[str, bool] = defaultdict(bool)
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runner_had_success: Dict[str, bool] = defaultdict(bool)
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runner_instance_had_failure: Dict[str, bool] = defaultdict(bool)
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runner_instance_had_success: Dict[str, bool] = defaultdict(bool)
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# Track first failed job for each runner in this run (for linking)
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runner_first_failed_job: Dict[str, Dict] = {}
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runner_instance_first_failed_job: Dict[str, Dict] = {}
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for job in jobs:
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job_name = job.get("name", "")
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# Skip excluded jobs (administrative/setup jobs)
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if any(
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job_name.startswith(excluded) for excluded in self.excluded_jobs
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):
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continue
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# Extract runner information
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# GitHub API might use different fields for runner info
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runner_name = (
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job.get("runner_name")
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or job.get("runner", {}).get("name")
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or "unknown"
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)
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runner_id = job.get("runner_id") or job.get("runner", {}).get("id")
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# Get runner labels (from runs-on field in workflow)
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runner_labels = job.get("labels", [])
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runner_labels_str = (
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", ".join(runner_labels) if runner_labels else "unknown"
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)
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# Skip jobs without runner information (likely skipped/queued jobs)
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if not runner_labels_str or runner_labels_str == "unknown":
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continue
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# Track by runner labels (primary identifier)
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# Use labels as the key since they're more informative than runner_name
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runner_key = runner_labels_str
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runner_total_jobs[runner_key] += 1
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runner_job_totals[runner_key][job_name] += 1
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# Track by specific runner instance
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if runner_id:
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runner_instance_key = f"{runner_labels_str}_{runner_id}"
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runner_instance_stats[runner_instance_key]["total_jobs"] += 1
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# Store runner name for reference
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runner_instance_stats[runner_instance_key][
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"runner_name"
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] = runner_name
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# Calculate queue time (time from created to started) per instance
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created_at = job.get("created_at")
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started_at = job.get("started_at")
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if created_at and started_at:
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try:
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from datetime import datetime
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created_time = datetime.fromisoformat(
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created_at.replace("Z", "+00:00")
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)
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started_time = datetime.fromisoformat(
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started_at.replace("Z", "+00:00")
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)
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queue_time_seconds = (
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started_time - created_time
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).total_seconds()
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if queue_time_seconds >= 0: # Sanity check
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runner_instance_queue_times[runner_instance_key].append(
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queue_time_seconds
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)
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except (ValueError, AttributeError):
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pass # Skip if timestamp parsing fails
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conclusion = job.get("conclusion")
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if conclusion == "failure":
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# Failure detected
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runner_failed_jobs[runner_key] += 1
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runner_job_failures[runner_key][job_name] += 1
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runner_had_failure[runner_key] = True
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# Track first failed job for this runner in this run (for linking)
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if runner_key not in runner_first_failed_job:
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runner_first_failed_job[runner_key] = {
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"job_id": job.get("id"),
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"job_url": job.get("html_url", run_info["url"]),
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"job_name": job_name,
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}
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if runner_id:
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runner_instance_stats[runner_instance_key]["failed_jobs"] += 1
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runner_instance_stats[runner_instance_key]["jobs_failed"][
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job_name
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] += 1
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runner_instance_had_failure[runner_instance_key] = True
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# Track first failed job for this runner instance in this run
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if runner_instance_key not in runner_instance_first_failed_job:
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runner_instance_first_failed_job[runner_instance_key] = {
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"job_id": job.get("id"),
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"job_url": job.get("html_url", run_info["url"]),
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"job_name": job_name,
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}
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elif conclusion == "success":
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runner_had_success[runner_key] = True
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if runner_id:
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runner_instance_had_success[runner_instance_key] = True
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# Update consecutive failure streaks based on run-level results
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# A runner is considered "failing" if it had at least one failure in the run
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for runner_key in set(
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list(runner_had_failure.keys()) + list(runner_had_success.keys())
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):
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if runner_had_failure[runner_key]:
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runner_current_streak[runner_key] += 1
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failure_info = {
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**run_info,
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"runner_key": runner_key,
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}
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# Include job URL if we have it
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if runner_key in runner_first_failed_job:
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failure_info.update(runner_first_failed_job[runner_key])
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# Track if this is the first failure in a new streak
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if runner_current_streak[runner_key] == 1:
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runner_first_failure_in_streak[runner_key] = failure_info
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# Always update last failure to the most recent one
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runner_last_failure_in_streak[runner_key] = failure_info
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# Update max streak
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if (
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runner_current_streak[runner_key]
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> runner_max_streak[runner_key]
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):
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runner_max_streak[runner_key] = runner_current_streak[
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runner_key
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]
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elif runner_had_success[runner_key]:
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# Success - streak broken
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if runner_current_streak[runner_key] > 0:
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runner_recovery_info[runner_key] = {
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**run_info,
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"runner_key": runner_key,
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"streak_length": runner_current_streak[runner_key],
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}
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runner_current_streak[runner_key] = 0
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runner_first_failure_in_streak[runner_key] = None
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runner_last_failure_in_streak[runner_key] = None
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# Update instance streaks
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for runner_instance_key in set(
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list(runner_instance_had_failure.keys())
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+ list(runner_instance_had_success.keys())
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):
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if runner_instance_had_failure[runner_instance_key]:
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runner_instance_current_streak[runner_instance_key] += 1
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if runner_instance_current_streak[runner_instance_key] == 1:
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failure_info = {
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**run_info,
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"runner_instance": runner_instance_key,
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}
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# Include job URL if we have it
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if runner_instance_key in runner_instance_first_failed_job:
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failure_info.update(
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runner_instance_first_failed_job[runner_instance_key]
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)
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runner_instance_first_failure[runner_instance_key] = (
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failure_info
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)
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# Always update last failure to the most recent one
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failure_info = {
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**run_info,
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"runner_instance": runner_instance_key,
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}
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# Include job URL if we have it
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if runner_instance_key in runner_instance_first_failed_job:
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failure_info.update(
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runner_instance_first_failed_job[runner_instance_key]
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)
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runner_instance_last_failure[runner_instance_key] = failure_info
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if (
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runner_instance_current_streak[runner_instance_key]
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> runner_instance_max_streak[runner_instance_key]
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):
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runner_instance_max_streak[runner_instance_key] = (
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runner_instance_current_streak[runner_instance_key]
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)
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elif runner_instance_had_success[runner_instance_key]:
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if runner_instance_current_streak[runner_instance_key] > 0:
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runner_instance_recovery[runner_instance_key] = {
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**run_info,
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"runner_instance": runner_instance_key,
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"streak_length": runner_instance_current_streak[
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runner_instance_key
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],
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}
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runner_instance_current_streak[runner_instance_key] = 0
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runner_instance_first_failure[runner_instance_key] = None
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runner_instance_last_failure[runner_instance_key] = None
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|
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time.sleep(0.05)
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|
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# Build final runner stats
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runner_stats = {}
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for runner_key in runner_total_jobs.keys():
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total = runner_total_jobs[runner_key]
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failed = runner_failed_jobs[runner_key]
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failure_rate = (failed / total * 100) if total > 0 else 0
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# Calculate queue time statistics by aggregating from runner instances
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# Find all instances that match this runner label
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aggregated_queue_times = []
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for instance_key, queue_times in runner_instance_queue_times.items():
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# Extract the labels part from "labels_id"
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instance_labels = (
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instance_key.rsplit("_", 1)[0]
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if "_" in instance_key
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else instance_key
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)
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if instance_labels == runner_key:
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aggregated_queue_times.extend(queue_times)
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avg_queue_time = (
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sum(aggregated_queue_times) / len(aggregated_queue_times)
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if aggregated_queue_times
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else 0
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)
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p90_queue_time = 0
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if aggregated_queue_times:
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sorted_queue_times = sorted(aggregated_queue_times)
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p90_index = int(len(sorted_queue_times) * 0.9)
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p90_queue_time = (
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sorted_queue_times[p90_index]
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if p90_index < len(sorted_queue_times)
|
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else sorted_queue_times[-1]
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)
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runner_stats[runner_key] = {
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"total_jobs": total,
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"failed_jobs": failed,
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"failure_rate": failure_rate,
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"unique_jobs_with_failures": len(runner_job_failures[runner_key]),
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"jobs_failed": dict(runner_job_failures[runner_key]),
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"jobs_total": dict(runner_job_totals[runner_key]),
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"avg_queue_time_seconds": avg_queue_time,
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"p90_queue_time_seconds": p90_queue_time,
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"queue_time_samples": len(aggregated_queue_times),
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}
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# Convert runner instance stats to regular dicts with queue time stats
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runner_instance_data = {}
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for instance_key, stats in runner_instance_stats.items():
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# Calculate queue time statistics for this instance
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queue_times = runner_instance_queue_times[instance_key]
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avg_queue_time = sum(queue_times) / len(queue_times) if queue_times else 0
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p90_queue_time = 0
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if queue_times:
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sorted_queue_times = sorted(queue_times)
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p90_index = int(len(sorted_queue_times) * 0.9)
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p90_queue_time = (
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sorted_queue_times[p90_index]
|
|
if p90_index < len(sorted_queue_times)
|
|
else sorted_queue_times[-1]
|
|
)
|
|
|
|
runner_instance_data[instance_key] = {
|
|
"total_jobs": stats["total_jobs"],
|
|
"failed_jobs": stats["failed_jobs"],
|
|
"failure_rate": (
|
|
stats["failed_jobs"] / stats["total_jobs"] * 100
|
|
if stats["total_jobs"] > 0
|
|
else 0
|
|
),
|
|
"jobs_failed": dict(stats["jobs_failed"]),
|
|
"runner_name": stats.get("runner_name", "unknown"),
|
|
"avg_queue_time_seconds": avg_queue_time,
|
|
"p90_queue_time_seconds": p90_queue_time,
|
|
"queue_time_samples": len(queue_times),
|
|
}
|
|
|
|
# Build runner streak data
|
|
runner_streak_data = {}
|
|
for runner_key in runner_total_jobs.keys():
|
|
runner_streak_data[runner_key] = {
|
|
"current_streak": runner_current_streak[runner_key],
|
|
"max_streak": runner_max_streak[runner_key],
|
|
"total_failures": runner_failed_jobs[runner_key],
|
|
"total_jobs": runner_total_jobs[runner_key],
|
|
"failure_rate": (
|
|
runner_failed_jobs[runner_key] / runner_total_jobs[runner_key] * 100
|
|
if runner_total_jobs[runner_key] > 0
|
|
else 0
|
|
),
|
|
"jobs_failed": dict(runner_job_failures[runner_key]),
|
|
"first_failure_in_streak": runner_first_failure_in_streak.get(
|
|
runner_key
|
|
),
|
|
"last_failure_in_streak": runner_last_failure_in_streak.get(runner_key),
|
|
"recovery_info": runner_recovery_info.get(runner_key),
|
|
}
|
|
|
|
# Build runner instance streak data
|
|
runner_instance_streak_data = {}
|
|
for instance_key in runner_instance_stats.keys():
|
|
runner_instance_streak_data[instance_key] = {
|
|
"current_streak": runner_instance_current_streak[instance_key],
|
|
"max_streak": runner_instance_max_streak[instance_key],
|
|
"total_failures": runner_instance_stats[instance_key]["failed_jobs"],
|
|
"total_jobs": runner_instance_stats[instance_key]["total_jobs"],
|
|
"failure_rate": (
|
|
runner_instance_stats[instance_key]["failed_jobs"]
|
|
/ runner_instance_stats[instance_key]["total_jobs"]
|
|
* 100
|
|
if runner_instance_stats[instance_key]["total_jobs"] > 0
|
|
else 0
|
|
),
|
|
"runner_name": runner_instance_stats[instance_key].get(
|
|
"runner_name", "unknown"
|
|
),
|
|
"jobs_failed": dict(runner_instance_stats[instance_key]["jobs_failed"]),
|
|
"first_failure_in_streak": runner_instance_first_failure.get(
|
|
instance_key
|
|
),
|
|
"last_failure_in_streak": runner_instance_last_failure.get(
|
|
instance_key
|
|
),
|
|
"recovery_info": runner_instance_recovery.get(instance_key),
|
|
}
|
|
|
|
return (
|
|
runner_stats,
|
|
runner_instance_data,
|
|
runner_streak_data,
|
|
runner_instance_streak_data,
|
|
)
|
|
|
|
def analyze_consecutive_failures(
|
|
self, runs: List[Dict]
|
|
) -> Tuple[Dict[str, Dict], Dict[str, int]]:
|
|
"""
|
|
Analyze consecutive failures for each job.
|
|
|
|
"Current Streak" = consecutive failures ending at the most recent run (NOW)
|
|
If the most recent run succeeded, current streak = 0 (streak is broken)
|
|
"Max Streak" = the longest consecutive failure streak seen in the analyzed period
|
|
|
|
Returns:
|
|
Tuple of (job_streak_data, job_current_streaks)
|
|
"""
|
|
print("\nAnalyzing consecutive failures...")
|
|
|
|
# Sort runs by created_at (oldest first) to track streaks chronologically
|
|
sorted_runs = sorted(runs, key=lambda x: x.get("created_at", ""))
|
|
|
|
# Track current streak for each job
|
|
job_current_streak: Dict[str, int] = defaultdict(int)
|
|
job_max_streak: Dict[str, int] = defaultdict(int)
|
|
job_total_failures: Dict[str, int] = defaultdict(int)
|
|
job_total_runs: Dict[str, int] = defaultdict(int)
|
|
job_first_failure_in_streak: Dict[str, Optional[Dict]] = {}
|
|
job_last_failure_in_streak: Dict[str, Optional[Dict]] = {}
|
|
job_recovery_info: Dict[str, Optional[Dict]] = {}
|
|
job_recent_runs: Dict[str, List[Dict]] = defaultdict(list) # Track last 5 runs
|
|
|
|
total_runs_processed = len(sorted_runs)
|
|
for i, run in enumerate(sorted_runs, 1):
|
|
if i % 50 == 0 or i == total_runs_processed:
|
|
print(
|
|
f"Processing run {i}/{total_runs_processed}: #{run.get('run_number')}"
|
|
)
|
|
|
|
head_commit = run.get("head_commit") or {}
|
|
run_info = {
|
|
"run_number": run.get("run_number"),
|
|
"run_id": run.get("id"),
|
|
"created_at": run.get("created_at"),
|
|
"head_sha": run.get("head_sha", "")[:8],
|
|
"author": head_commit.get("author", {}).get("name", "Unknown"),
|
|
"url": f"https://github.com/{self.repo}/actions/runs/{run.get('id')}",
|
|
}
|
|
|
|
pull_requests = run.get("pull_requests", [])
|
|
if pull_requests:
|
|
run_info["pr_number"] = pull_requests[0].get("number")
|
|
|
|
# Get jobs for this run
|
|
jobs = self.get_jobs_for_run(run.get("id"))
|
|
|
|
for job in jobs:
|
|
job_name = job.get("name", "")
|
|
|
|
# Skip excluded jobs (administrative/setup jobs)
|
|
if any(
|
|
job_name.startswith(excluded) for excluded in self.excluded_jobs
|
|
):
|
|
continue
|
|
|
|
job_total_runs[job_name] += 1
|
|
conclusion = job.get("conclusion")
|
|
|
|
if conclusion == "failure":
|
|
# Failure detected
|
|
job_total_failures[job_name] += 1
|
|
job_current_streak[job_name] += 1
|
|
|
|
# Track if this is the first failure in a new streak
|
|
if job_current_streak[job_name] == 1:
|
|
job_first_failure_in_streak[job_name] = {
|
|
**run_info,
|
|
"job_name": job_name,
|
|
"job_id": job.get("id"),
|
|
"job_url": job.get("html_url", run_info["url"]),
|
|
"conclusion": conclusion,
|
|
}
|
|
|
|
# Always update last failure to the most recent one
|
|
job_last_failure_in_streak[job_name] = {
|
|
**run_info,
|
|
"job_name": job_name,
|
|
"job_id": job.get("id"),
|
|
"job_url": job.get("html_url", run_info["url"]),
|
|
"conclusion": conclusion,
|
|
}
|
|
|
|
# Update max streak
|
|
if job_current_streak[job_name] > job_max_streak[job_name]:
|
|
job_max_streak[job_name] = job_current_streak[job_name]
|
|
|
|
elif conclusion == "success":
|
|
# Success - streak broken
|
|
if job_current_streak[job_name] > 0:
|
|
# Record recovery
|
|
job_recovery_info[job_name] = {
|
|
**run_info,
|
|
"job_name": job_name,
|
|
"streak_length": job_current_streak[job_name],
|
|
}
|
|
|
|
job_current_streak[job_name] = 0
|
|
job_first_failure_in_streak[job_name] = None
|
|
job_last_failure_in_streak[job_name] = None
|
|
|
|
# Track recent runs (last 5 for each job)
|
|
run_attempt = job.get("run_attempt", 1)
|
|
|
|
# Create status emoji with superscript if retry attempt > 1
|
|
if conclusion == "success":
|
|
status = "✅"
|
|
elif conclusion == "failure":
|
|
status = "❌"
|
|
else:
|
|
status = "⚪"
|
|
|
|
# Add superscript for retry attempts (2+ only)
|
|
if run_attempt > 1:
|
|
superscript_map = {
|
|
"2": "²",
|
|
"3": "³",
|
|
"4": "⁴",
|
|
"5": "⁵",
|
|
"6": "⁶",
|
|
"7": "⁷",
|
|
"8": "⁸",
|
|
"9": "⁹",
|
|
}
|
|
status += superscript_map.get(str(run_attempt), f"^{run_attempt}")
|
|
|
|
job_recent_runs[job_name].append(
|
|
{
|
|
"run_number": run_info["run_number"],
|
|
"job_url": job.get("html_url", run_info["url"]),
|
|
"conclusion": conclusion,
|
|
"status": status,
|
|
"run_attempt": run_attempt,
|
|
}
|
|
)
|
|
|
|
time.sleep(0.05)
|
|
|
|
# Build final results
|
|
job_streak_data = {}
|
|
for job_name in job_current_streak.keys():
|
|
# Get last 5 runs (most recent first)
|
|
recent_runs = job_recent_runs.get(job_name, [])[-5:][
|
|
::-1
|
|
] # Last 5, reversed
|
|
|
|
job_streak_data[job_name] = {
|
|
"current_streak": job_current_streak[job_name],
|
|
"max_streak": job_max_streak[job_name],
|
|
"total_failures": job_total_failures[job_name],
|
|
"total_runs": job_total_runs[job_name],
|
|
"failure_rate": (
|
|
job_total_failures[job_name] / job_total_runs[job_name] * 100
|
|
if job_total_runs[job_name] > 0
|
|
else 0
|
|
),
|
|
"first_failure_in_streak": job_first_failure_in_streak.get(job_name),
|
|
"last_failure_in_streak": job_last_failure_in_streak.get(job_name),
|
|
"recovery_info": job_recovery_info.get(job_name),
|
|
"recent_runs": recent_runs, # Last 5 runs with status emoji
|
|
}
|
|
|
|
return job_streak_data, job_current_streak
|
|
|
|
# print statements here mainly for local testing
|
|
def generate_failure_report(
|
|
self,
|
|
# Scheduled runs (9 workflows)
|
|
pr_test_nvidia_scheduled_data: Dict[str, Dict],
|
|
pr_test_amd_scheduled_data: Dict[str, Dict],
|
|
pr_test_xeon_scheduled_data: Dict[str, Dict],
|
|
pr_test_xpu_scheduled_data: Dict[str, Dict],
|
|
pr_test_npu_scheduled_data: Dict[str, Dict],
|
|
nightly_nvidia_scheduled_data: Dict[str, Dict],
|
|
nightly_amd_scheduled_data: Dict[str, Dict],
|
|
nightly_intel_scheduled_data: Dict[str, Dict],
|
|
nightly_npu_scheduled_data: Dict[str, Dict],
|
|
# General runs (9 workflows)
|
|
pr_test_nvidia_general_data: Dict[str, Dict],
|
|
pr_test_amd_general_data: Dict[str, Dict],
|
|
pr_test_xeon_general_data: Dict[str, Dict],
|
|
pr_test_xpu_general_data: Dict[str, Dict],
|
|
pr_test_npu_general_data: Dict[str, Dict],
|
|
nightly_nvidia_general_data: Dict[str, Dict],
|
|
nightly_amd_general_data: Dict[str, Dict],
|
|
nightly_intel_general_data: Dict[str, Dict],
|
|
nightly_npu_general_data: Dict[str, Dict],
|
|
# Runners
|
|
runner_stats: Optional[Dict[str, Dict]] = None,
|
|
runner_instance_data: Optional[Dict[str, Dict]] = None,
|
|
runner_streak_data: Optional[Dict[str, Dict]] = None,
|
|
runner_instance_streak_data: Optional[Dict[str, Dict]] = None,
|
|
# Config
|
|
output_file: Optional[str] = None,
|
|
pr_test_scheduled_limit: int = 12,
|
|
nightly_scheduled_limit: int = 6,
|
|
general_limit: int = 100,
|
|
):
|
|
"""Generate detailed failure analysis report."""
|
|
print("\n" + "=" * 80)
|
|
print("SGLang Consecutive Failures Analysis Report")
|
|
print("=" * 80)
|
|
|
|
# Combine all general data for summary stats
|
|
combined_general_data = {
|
|
**pr_test_nvidia_general_data,
|
|
**pr_test_amd_general_data,
|
|
**pr_test_xeon_general_data,
|
|
**pr_test_xpu_general_data,
|
|
**pr_test_npu_general_data,
|
|
**nightly_nvidia_general_data,
|
|
**nightly_amd_general_data,
|
|
**nightly_intel_general_data,
|
|
**nightly_npu_general_data,
|
|
}
|
|
|
|
# Sort jobs by current streak (descending)
|
|
sorted_jobs = sorted(
|
|
combined_general_data.items(),
|
|
key=lambda x: (x[1]["current_streak"], x[1]["failure_rate"]),
|
|
reverse=True,
|
|
)
|
|
|
|
# Summary Statistics
|
|
print("\n## Summary Statistics")
|
|
print(f"Total (unique) jobs analyzed: {len(sorted_jobs)}")
|
|
print(
|
|
f"Jobs with Active Failure Streaks: {sum(1 for j in sorted_jobs if j[1]['current_streak'] > 0)}"
|
|
)
|
|
|
|
if runner_stats:
|
|
print(f"Total Runners Analyzed: {len(runner_stats)}")
|
|
|
|
# Queue Time Summary
|
|
if runner_stats:
|
|
all_avg_queue_times = []
|
|
all_p90_queue_times = []
|
|
for stats in runner_stats.values():
|
|
if stats["queue_time_samples"] > 0:
|
|
all_avg_queue_times.append(stats["avg_queue_time_seconds"])
|
|
all_p90_queue_times.append(stats["p90_queue_time_seconds"])
|
|
|
|
if all_avg_queue_times:
|
|
overall_avg = sum(all_avg_queue_times) / len(all_avg_queue_times)
|
|
overall_p90 = sum(all_p90_queue_times) / len(all_p90_queue_times)
|
|
print("\n## Queue Time Summary")
|
|
print(
|
|
f"Average Queue Time (across all runners): {overall_avg / 60:.1f} minutes ({overall_avg:.0f}s)"
|
|
)
|
|
print(
|
|
f"P90 Queue Time (across all runners): {overall_p90 / 60:.1f} minutes ({overall_p90:.0f}s)"
|
|
)
|
|
|
|
# Helper function to print job section
|
|
def print_job_section(
|
|
title: str, data: Dict[str, Dict], color_failures: bool = False
|
|
):
|
|
sorted_data = sorted(
|
|
data.items(),
|
|
key=lambda x: (x[1]["current_streak"], x[1]["failure_rate"]),
|
|
reverse=True,
|
|
)
|
|
broken = [(name, d) for name, d in sorted_data if d["current_streak"] >= 2]
|
|
recently_failed = [
|
|
(name, d)
|
|
for name, d in sorted_data
|
|
if d["current_streak"] < 2 and d["total_failures"] > 0
|
|
]
|
|
|
|
# Always show section header
|
|
print("\n" + "=" * 130)
|
|
if broken:
|
|
print(f"## {title} ({len(broken)} jobs with active streaks)")
|
|
print("=" * 130)
|
|
print(
|
|
f"\n{'Job Name':<40} {'Current':<8} {'Max':<6} {'Runs':<6} {'First':<13} {'Last':<13} {'Recent History':<30}"
|
|
)
|
|
print("-" * 130)
|
|
for job_name, d in broken[:15]:
|
|
display_name = (
|
|
job_name if len(job_name) <= 38 else job_name[:35] + "..."
|
|
)
|
|
|
|
first_failure = d.get("first_failure_in_streak")
|
|
first_str = (
|
|
f"Run #{first_failure['run_number']}"
|
|
if first_failure
|
|
else "N/A"
|
|
)
|
|
|
|
last_failure = d.get("last_failure_in_streak")
|
|
last_str = (
|
|
f"Run #{last_failure['run_number']}" if last_failure else "N/A"
|
|
)
|
|
|
|
# Recent history (last 5 runs as emoji)
|
|
recent_runs = d.get("recent_runs", [])
|
|
history_str = (
|
|
" ".join([r["status"] for r in recent_runs])
|
|
if recent_runs
|
|
else "N/A"
|
|
)
|
|
|
|
# Color red if color_failures is True (for critical sections)
|
|
if color_failures:
|
|
print(
|
|
f"\033[91m{display_name:<40}\033[0m {d['current_streak']:<8} {d['max_streak']:<6} {d['total_runs']:<6} {first_str:<13} {last_str:<13} {history_str:<30}"
|
|
)
|
|
else:
|
|
print(
|
|
f"{display_name:<40} {d['current_streak']:<8} {d['max_streak']:<6} {d['total_runs']:<6} {first_str:<13} {last_str:<13} {history_str:<30}"
|
|
)
|
|
else:
|
|
print(f"## {title}")
|
|
print("=" * 130)
|
|
print("\n✅ No jobs with active failure streaks (streak >= 2)")
|
|
|
|
# Show recently failed jobs in a collapsed section (terminal doesn't support collapse, so just show as separate section)
|
|
if recently_failed:
|
|
print(
|
|
f"\n Recently failed jobs (no active streak): {len(recently_failed)} jobs"
|
|
)
|
|
print(
|
|
f" {'Job Name':<38} {'Failures':<12} {'Fail Rate':<12} {'Total Runs':<12} {'Recent History (last 5)':<30}"
|
|
)
|
|
print(" " + "-" * 120)
|
|
for job_name, d in recently_failed[:10]:
|
|
display_name = (
|
|
job_name if len(job_name) <= 36 else job_name[:33] + "..."
|
|
)
|
|
recent_runs = d.get("recent_runs", [])
|
|
history_str = (
|
|
" ".join([r["status"] for r in recent_runs])
|
|
if recent_runs
|
|
else "N/A"
|
|
)
|
|
print(
|
|
f" {display_name:<38} {d['total_failures']:<12} {d['failure_rate']:.1f}%{'':<7} {d['total_runs']:<12} {history_str:<30}"
|
|
)
|
|
|
|
# ========== SCHEDULED/MAIN BRANCH RUNS (9 sections) ==========
|
|
print("\n" + "█" * 130)
|
|
print("SCHEDULED RUNS (Main Branch)")
|
|
print("█" * 130)
|
|
|
|
# PR Tests - Scheduled (5 workflows)
|
|
print_job_section(
|
|
f"1. PR Test NVIDIA - Scheduled (latest {pr_test_scheduled_limit} runs)",
|
|
pr_test_nvidia_scheduled_data,
|
|
color_failures=True,
|
|
)
|
|
print_job_section(
|
|
f"2. PR Test AMD - Scheduled (latest {pr_test_scheduled_limit} runs)",
|
|
pr_test_amd_scheduled_data,
|
|
color_failures=True,
|
|
)
|
|
print_job_section(
|
|
f"3. PR Test Xeon - Scheduled (latest {pr_test_scheduled_limit} runs)",
|
|
pr_test_xeon_scheduled_data,
|
|
color_failures=True,
|
|
)
|
|
print_job_section(
|
|
f"4. PR Test XPU - Scheduled (latest {pr_test_scheduled_limit} runs)",
|
|
pr_test_xpu_scheduled_data,
|
|
color_failures=True,
|
|
)
|
|
print_job_section(
|
|
f"5. PR Test NPU - Scheduled (latest {pr_test_scheduled_limit} runs)",
|
|
pr_test_npu_scheduled_data,
|
|
color_failures=True,
|
|
)
|
|
|
|
# Nightly Tests - Scheduled (4 workflows)
|
|
print_job_section(
|
|
f"6. Nightly NVIDIA - Scheduled (latest {nightly_scheduled_limit} runs)",
|
|
nightly_nvidia_scheduled_data,
|
|
color_failures=True,
|
|
)
|
|
print_job_section(
|
|
f"7. Nightly AMD - Scheduled (latest {nightly_scheduled_limit} runs)",
|
|
nightly_amd_scheduled_data,
|
|
color_failures=True,
|
|
)
|
|
print_job_section(
|
|
f"8. Nightly Intel - Scheduled (latest {nightly_scheduled_limit} runs)",
|
|
nightly_intel_scheduled_data,
|
|
color_failures=True,
|
|
)
|
|
print_job_section(
|
|
f"9. Nightly NPU - Scheduled (latest {nightly_scheduled_limit} runs)",
|
|
nightly_npu_scheduled_data,
|
|
color_failures=True,
|
|
)
|
|
|
|
# ========== GENERAL RUNS (9 sections) ==========
|
|
print("\n" + "█" * 130)
|
|
print("GENERAL RUNS (All Branches)")
|
|
print("█" * 130)
|
|
|
|
# PR Tests - General (5 workflows)
|
|
print_job_section(
|
|
f"10. PR Test NVIDIA - General (latest {general_limit} runs)",
|
|
pr_test_nvidia_general_data,
|
|
color_failures=False,
|
|
)
|
|
print_job_section(
|
|
f"11. PR Test AMD - General (latest {general_limit} runs)",
|
|
pr_test_amd_general_data,
|
|
color_failures=False,
|
|
)
|
|
print_job_section(
|
|
f"12. PR Test Xeon - General (latest {general_limit} runs)",
|
|
pr_test_xeon_general_data,
|
|
color_failures=False,
|
|
)
|
|
print_job_section(
|
|
f"13. PR Test XPU - General (latest {general_limit} runs)",
|
|
pr_test_xpu_general_data,
|
|
color_failures=False,
|
|
)
|
|
print_job_section(
|
|
f"14. PR Test NPU - General (latest {general_limit} runs)",
|
|
pr_test_npu_general_data,
|
|
color_failures=False,
|
|
)
|
|
|
|
# Nightly Tests - General (4 workflows)
|
|
print_job_section(
|
|
f"15. Nightly NVIDIA - General (latest {general_limit} runs)",
|
|
nightly_nvidia_general_data,
|
|
color_failures=False,
|
|
)
|
|
print_job_section(
|
|
f"16. Nightly AMD - General (latest {general_limit} runs)",
|
|
nightly_amd_general_data,
|
|
color_failures=False,
|
|
)
|
|
print_job_section(
|
|
f"17. Nightly Intel - General (latest {general_limit} runs)",
|
|
nightly_intel_general_data,
|
|
color_failures=False,
|
|
)
|
|
print_job_section(
|
|
f"18. Nightly NPU - General (latest {general_limit} runs)",
|
|
nightly_npu_general_data,
|
|
color_failures=False,
|
|
)
|
|
|
|
# ========== RUNNERS ==========
|
|
print("\n" + "█" * 130)
|
|
print("RUNNER HEALTH")
|
|
print("█" * 130)
|
|
|
|
# 5. Workers (at the very bottom) - Use machine names from runner instances (streak >= 2)
|
|
if runner_instance_data and runner_instance_streak_data:
|
|
# Combine instance stats with streak data and sort by consecutive failures first
|
|
combined_data = []
|
|
for instance_key, stats in runner_instance_data.items():
|
|
streak_data = runner_instance_streak_data.get(instance_key, {})
|
|
combined_data.append(
|
|
{
|
|
"runner_name": stats.get("runner_name", "unknown"),
|
|
"instance_key": instance_key,
|
|
"current_streak": streak_data.get("current_streak", 0),
|
|
"max_streak": streak_data.get("max_streak", 0),
|
|
"failure_rate": stats["failure_rate"],
|
|
"total_jobs": stats["total_jobs"],
|
|
"unique_jobs": len(stats.get("jobs_failed", {})),
|
|
"avg_queue": stats.get("avg_queue_time_seconds", 0),
|
|
"p90_queue": stats.get("p90_queue_time_seconds", 0),
|
|
"queue_samples": stats.get("queue_time_samples", 0),
|
|
"first_failure": streak_data.get("first_failure_in_streak"),
|
|
"last_failure": streak_data.get("last_failure_in_streak"),
|
|
}
|
|
)
|
|
|
|
# Sort by current streak (descending), then max streak, then failure rate
|
|
sorted_runners = sorted(
|
|
combined_data,
|
|
key=lambda x: (x["current_streak"], x["max_streak"], x["failure_rate"]),
|
|
reverse=True,
|
|
)
|
|
|
|
# Only show runners with streak >= 2
|
|
runners_with_issues = [
|
|
r for r in sorted_runners if r["current_streak"] >= 2
|
|
]
|
|
|
|
# Always show section header
|
|
print("\n" + "=" * 140)
|
|
print("## 5. Top 15 Workers by Consecutive Failures")
|
|
print("=" * 140)
|
|
|
|
if runners_with_issues:
|
|
print(
|
|
f"\n{'Machine Name':<30} {'Curr':<5} {'Max':<5} {'Fail%':<7} {'AvgQ':<7} {'Total':<7} {'Unique':<8} {'First':<13} {'Last':<13}"
|
|
)
|
|
print("-" * 140)
|
|
|
|
for runner_data in runners_with_issues[:15]:
|
|
# Truncate machine name if too long for display
|
|
display_name = (
|
|
runner_data["runner_name"]
|
|
if len(runner_data["runner_name"]) <= 28
|
|
else runner_data["runner_name"][:25] + "..."
|
|
)
|
|
|
|
# Format streaks
|
|
streak_str = str(runner_data["current_streak"])
|
|
max_str = str(runner_data["max_streak"])
|
|
|
|
# Format queue time
|
|
avg_queue_str = (
|
|
f"{runner_data['avg_queue'] / 60:.1f}m"
|
|
if runner_data["queue_samples"] > 0
|
|
else "N/A"
|
|
)
|
|
|
|
# Get first and last failure info
|
|
first_failure = runner_data.get("first_failure")
|
|
first_failure_str = (
|
|
f"Run #{first_failure['run_number']}"
|
|
if first_failure
|
|
else "N/A"
|
|
)
|
|
|
|
last_failure = runner_data.get("last_failure")
|
|
last_failure_str = (
|
|
f"Run #{last_failure['run_number']}" if last_failure else "N/A"
|
|
)
|
|
|
|
# Color red for workers with failures
|
|
print(
|
|
f"\033[91m{display_name:<30}\033[0m {streak_str:<5} {max_str:<5} {runner_data['failure_rate']:>5.1f}% {avg_queue_str:<7} {runner_data['total_jobs']:<7} {runner_data['unique_jobs']:<8} {first_failure_str:<13} {last_failure_str:<13}"
|
|
)
|
|
else:
|
|
print("\n✅ No runners with active failure streaks (streak >= 2)")
|
|
|
|
# Build report data (always needed for GitHub summary)
|
|
# Calculate overall queue time for summary
|
|
overall_avg_queue = 0
|
|
overall_p90_queue = 0
|
|
if runner_stats:
|
|
all_avg_queue_times = [
|
|
stats["avg_queue_time_seconds"]
|
|
for stats in runner_stats.values()
|
|
if stats["queue_time_samples"] > 0
|
|
]
|
|
all_p90_queue_times = [
|
|
stats["p90_queue_time_seconds"]
|
|
for stats in runner_stats.values()
|
|
if stats["queue_time_samples"] > 0
|
|
]
|
|
if all_avg_queue_times:
|
|
overall_avg_queue = sum(all_avg_queue_times) / len(all_avg_queue_times)
|
|
overall_p90_queue = sum(all_p90_queue_times) / len(all_p90_queue_times)
|
|
|
|
report_data = {
|
|
"summary": {
|
|
"total_jobs": len(sorted_jobs),
|
|
"jobs_with_streaks": sum(
|
|
1 for j in sorted_jobs if j[1]["current_streak"] > 0
|
|
),
|
|
"total_runners": len(runner_stats) if runner_stats else 0,
|
|
"analysis_timestamp": datetime.now().isoformat(),
|
|
"avg_queue_time_seconds": overall_avg_queue,
|
|
"p90_queue_time_seconds": overall_p90_queue,
|
|
},
|
|
"pr_test_scheduled_limit": pr_test_scheduled_limit,
|
|
"nightly_scheduled_limit": nightly_scheduled_limit,
|
|
"general_limit": general_limit,
|
|
# Scheduled data
|
|
"pr_test_nvidia_scheduled_data": pr_test_nvidia_scheduled_data,
|
|
"pr_test_amd_scheduled_data": pr_test_amd_scheduled_data,
|
|
"pr_test_xeon_scheduled_data": pr_test_xeon_scheduled_data,
|
|
"pr_test_xpu_scheduled_data": pr_test_xpu_scheduled_data,
|
|
"pr_test_npu_scheduled_data": pr_test_npu_scheduled_data,
|
|
"nightly_nvidia_scheduled_data": nightly_nvidia_scheduled_data,
|
|
"nightly_amd_scheduled_data": nightly_amd_scheduled_data,
|
|
"nightly_intel_scheduled_data": nightly_intel_scheduled_data,
|
|
"nightly_npu_scheduled_data": nightly_npu_scheduled_data,
|
|
# General data
|
|
"pr_test_nvidia_general_data": pr_test_nvidia_general_data,
|
|
"pr_test_amd_general_data": pr_test_amd_general_data,
|
|
"pr_test_xeon_general_data": pr_test_xeon_general_data,
|
|
"pr_test_xpu_general_data": pr_test_xpu_general_data,
|
|
"pr_test_npu_general_data": pr_test_npu_general_data,
|
|
"nightly_nvidia_general_data": nightly_nvidia_general_data,
|
|
"nightly_amd_general_data": nightly_amd_general_data,
|
|
"nightly_intel_general_data": nightly_intel_general_data,
|
|
"nightly_npu_general_data": nightly_npu_general_data,
|
|
"runner_stats": runner_stats if runner_stats else {},
|
|
"runner_instance_data": (
|
|
runner_instance_data if runner_instance_data else {}
|
|
),
|
|
"runner_streak_data": runner_streak_data if runner_streak_data else {},
|
|
"runner_instance_streak_data": (
|
|
runner_instance_streak_data if runner_instance_streak_data else {}
|
|
),
|
|
}
|
|
|
|
# Save to JSON only if output file is specified
|
|
if output_file:
|
|
with open(output_file, "w", encoding="utf-8") as f:
|
|
json.dump(report_data, f, ensure_ascii=False, indent=2)
|
|
print(f"\nDetailed report saved to: {output_file}")
|
|
|
|
print("=" * 80)
|
|
|
|
return report_data
|
|
|
|
def generate_github_summary(self, report_data: Dict):
|
|
"""Generate GitHub Actions Step Summary."""
|
|
try:
|
|
github_step_summary = os.environ.get("GITHUB_STEP_SUMMARY")
|
|
if not github_step_summary:
|
|
print("Not running in GitHub Actions, skipping summary generation")
|
|
return
|
|
|
|
print("Generating GitHub Actions summary...")
|
|
|
|
summary_lines = []
|
|
summary_lines.append("# SGLang Consecutive Failures Analysis")
|
|
summary_lines.append("")
|
|
summary_lines.append(
|
|
f"**Analysis Timestamp:** {report_data['summary']['analysis_timestamp']}"
|
|
)
|
|
summary_lines.append("_Note: Recent runs are shown left to right_")
|
|
summary_lines.append("")
|
|
|
|
# Summary stats - COLLAPSIBLE
|
|
summary_lines.append("<details>")
|
|
summary_lines.append(
|
|
"<summary>📊 Summary Statistics (click to expand)</summary>"
|
|
)
|
|
summary_lines.append("")
|
|
summary_lines.append("| Metric | Count |")
|
|
summary_lines.append("|--------|-------|")
|
|
summary_lines.append(
|
|
f"| Total (unique) jobs analyzed | {report_data['summary']['total_jobs']} |"
|
|
)
|
|
summary_lines.append(
|
|
f"| Jobs with Active Failure Streaks | {report_data['summary']['jobs_with_streaks']} |"
|
|
)
|
|
|
|
# Add main branch job counters
|
|
pr_main_count = report_data["summary"].get("pr_main_count", 0)
|
|
pr_main_with_streaks = report_data["summary"].get("pr_main_with_streaks", 0)
|
|
nightly_main_count = report_data["summary"].get("nightly_main_count", 0)
|
|
nightly_main_with_streaks = report_data["summary"].get(
|
|
"nightly_main_with_streaks", 0
|
|
)
|
|
|
|
summary_lines.append(
|
|
f"| PR Test Jobs on Main (scheduled) | {pr_main_count} ({pr_main_with_streaks} with streaks) |"
|
|
)
|
|
summary_lines.append(
|
|
f"| Nightly Test Jobs on Main (scheduled) | {nightly_main_count} ({nightly_main_with_streaks} with streaks) |"
|
|
)
|
|
|
|
summary_lines.append(
|
|
f"| Total Runners Analyzed | {report_data['summary']['total_runners']} |"
|
|
)
|
|
summary_lines.append("")
|
|
summary_lines.append("</details>")
|
|
summary_lines.append("")
|
|
|
|
# Queue Time Summary - COLLAPSIBLE
|
|
if report_data.get("summary", {}).get("avg_queue_time_seconds") is not None:
|
|
avg_queue = report_data["summary"]["avg_queue_time_seconds"]
|
|
p90_queue = report_data["summary"]["p90_queue_time_seconds"]
|
|
summary_lines.append("<details>")
|
|
summary_lines.append(
|
|
"<summary>📊 Queue Time Summary (click to expand)</summary>"
|
|
)
|
|
summary_lines.append("")
|
|
summary_lines.append("| Metric | Value |")
|
|
summary_lines.append("|--------|-------|")
|
|
summary_lines.append(
|
|
f"| Average Queue Time (across all runners) | {avg_queue / 60:.1f} minutes ({avg_queue:.0f}s) |"
|
|
)
|
|
summary_lines.append(
|
|
f"| P90 Queue Time (across all runners) | {p90_queue / 60:.1f} minutes ({p90_queue:.0f}s) |"
|
|
)
|
|
summary_lines.append("")
|
|
summary_lines.append("</details>")
|
|
summary_lines.append("")
|
|
|
|
# Helper function to generate job section for GitHub markdown
|
|
def generate_job_section_md(title: str, data: Dict[str, Dict]):
|
|
sorted_data = sorted(
|
|
data.items(),
|
|
key=lambda x: (x[1]["current_streak"], x[1]["failure_rate"]),
|
|
reverse=True,
|
|
)
|
|
broken = [
|
|
(name, d) for name, d in sorted_data if d["current_streak"] >= 2
|
|
]
|
|
recently_failed = [
|
|
(name, d)
|
|
for name, d in sorted_data
|
|
if d["current_streak"] < 2 and d["total_failures"] > 0
|
|
]
|
|
|
|
# Always show section header
|
|
summary_lines.append(f"## {title}")
|
|
summary_lines.append("")
|
|
|
|
if broken:
|
|
summary_lines.append(
|
|
"| Job Name | Current | Max | Runs | First | Last | Recent History |"
|
|
)
|
|
summary_lines.append(
|
|
"|----------|---------|-----|------|-------|------|----------------|"
|
|
)
|
|
for job_name, d in broken[:15]:
|
|
display_name = (
|
|
job_name if len(job_name) <= 35 else job_name[:32] + "..."
|
|
)
|
|
|
|
first_failure = d.get("first_failure_in_streak")
|
|
first_str = (
|
|
f"[Run #{first_failure['run_number']}]({first_failure.get('job_url', first_failure['url'])})"
|
|
if first_failure
|
|
else "N/A"
|
|
)
|
|
|
|
last_failure = d.get("last_failure_in_streak")
|
|
last_str = (
|
|
f"[Run #{last_failure['run_number']}]({last_failure.get('job_url', last_failure['url'])})"
|
|
if last_failure
|
|
else "N/A"
|
|
)
|
|
|
|
# Recent history (last 5 runs as clickable emoji)
|
|
recent_runs = d.get("recent_runs", [])
|
|
if recent_runs:
|
|
history_links = " ".join(
|
|
[
|
|
f"[{r['status']}]({r['job_url']})"
|
|
for r in recent_runs
|
|
]
|
|
)
|
|
else:
|
|
history_links = "N/A"
|
|
|
|
# Make entire row red if current streak >= 3
|
|
if d["current_streak"] >= 3:
|
|
summary_lines.append(
|
|
f"| <span style='color:red'>`{display_name}`</span> | <span style='color:red'>{d['current_streak']}</span> | <span style='color:red'>{d['max_streak']}</span> | <span style='color:red'>{d['total_runs']}</span> | "
|
|
f"<span style='color:red'>{first_str}</span> | <span style='color:red'>{last_str}</span> | <span style='color:red'>{history_links}</span> |"
|
|
)
|
|
else:
|
|
summary_lines.append(
|
|
f"| `{display_name}` | {d['current_streak']} | {d['max_streak']} | {d['total_runs']} | "
|
|
f"{first_str} | {last_str} | {history_links} |"
|
|
)
|
|
summary_lines.append("")
|
|
else:
|
|
summary_lines.append(
|
|
"✅ **No jobs with active failure streaks (streak >= 2)**"
|
|
)
|
|
summary_lines.append("")
|
|
|
|
# Show recently failed jobs in a collapsible section
|
|
if recently_failed:
|
|
summary_lines.append("<details>")
|
|
summary_lines.append(
|
|
f"<summary>Recently failed jobs (no active streak) - {len(recently_failed)} jobs</summary>"
|
|
)
|
|
summary_lines.append("")
|
|
summary_lines.append(
|
|
"| Job Name | Failures | Fail Rate | Total Runs | Recent History (last 5) |"
|
|
)
|
|
summary_lines.append(
|
|
"|----------|----------|-----------|------------|-------------------------|"
|
|
)
|
|
for job_name, d in recently_failed[:15]:
|
|
display_name = (
|
|
job_name if len(job_name) <= 35 else job_name[:32] + "..."
|
|
)
|
|
recent_runs = d.get("recent_runs", [])
|
|
if recent_runs:
|
|
history_links = " ".join(
|
|
[
|
|
f"[{r['status']}]({r['job_url']})"
|
|
for r in recent_runs
|
|
]
|
|
)
|
|
else:
|
|
history_links = "N/A"
|
|
|
|
summary_lines.append(
|
|
f"| `{display_name}` | {d['total_failures']} | {d['failure_rate']:.1f}% | {d['total_runs']} | {history_links} |"
|
|
)
|
|
summary_lines.append("")
|
|
summary_lines.append("</details>")
|
|
summary_lines.append("")
|
|
|
|
# ========== SCHEDULED RUNS (9 sections) ==========
|
|
summary_lines.append("---")
|
|
summary_lines.append("# 📅 SCHEDULED RUNS (Main Branch)")
|
|
summary_lines.append("")
|
|
|
|
# Get limits
|
|
pr_sched_limit = report_data.get("pr_test_scheduled_limit", 12)
|
|
nightly_sched_limit = report_data.get("nightly_scheduled_limit", 6)
|
|
|
|
# PR Tests - Scheduled (5 workflows)
|
|
generate_job_section_md(
|
|
f"1. PR Test NVIDIA - Scheduled (latest {pr_sched_limit} runs)",
|
|
report_data.get("pr_test_nvidia_scheduled_data", {}),
|
|
)
|
|
generate_job_section_md(
|
|
f"2. PR Test AMD - Scheduled (latest {pr_sched_limit} runs)",
|
|
report_data.get("pr_test_amd_scheduled_data", {}),
|
|
)
|
|
generate_job_section_md(
|
|
f"3. PR Test Xeon - Scheduled (latest {pr_sched_limit} runs)",
|
|
report_data.get("pr_test_xeon_scheduled_data", {}),
|
|
)
|
|
generate_job_section_md(
|
|
f"4. PR Test XPU - Scheduled (latest {pr_sched_limit} runs)",
|
|
report_data.get("pr_test_xpu_scheduled_data", {}),
|
|
)
|
|
generate_job_section_md(
|
|
f"5. PR Test NPU - Scheduled (latest {pr_sched_limit} runs)",
|
|
report_data.get("pr_test_npu_scheduled_data", {}),
|
|
)
|
|
|
|
# Nightly Tests - Scheduled (4 workflows)
|
|
generate_job_section_md(
|
|
f"6. Nightly NVIDIA - Scheduled (latest {nightly_sched_limit} runs)",
|
|
report_data.get("nightly_nvidia_scheduled_data", {}),
|
|
)
|
|
generate_job_section_md(
|
|
f"7. Nightly AMD - Scheduled (latest {nightly_sched_limit} runs)",
|
|
report_data.get("nightly_amd_scheduled_data", {}),
|
|
)
|
|
generate_job_section_md(
|
|
f"8. Nightly Intel - Scheduled (latest {nightly_sched_limit} runs)",
|
|
report_data.get("nightly_intel_scheduled_data", {}),
|
|
)
|
|
generate_job_section_md(
|
|
f"9. Nightly NPU - Scheduled (latest {nightly_sched_limit} runs)",
|
|
report_data.get("nightly_npu_scheduled_data", {}),
|
|
)
|
|
|
|
# ========== GENERAL RUNS (9 sections) ==========
|
|
summary_lines.append("---")
|
|
summary_lines.append("# 🌍 GENERAL RUNS (All Branches)")
|
|
summary_lines.append("")
|
|
|
|
gen_limit = report_data.get("general_limit", 100)
|
|
|
|
# PR Tests - General (5 workflows)
|
|
generate_job_section_md(
|
|
f"10. PR Test NVIDIA - General (latest {gen_limit} runs)",
|
|
report_data.get("pr_test_nvidia_general_data", {}),
|
|
)
|
|
generate_job_section_md(
|
|
f"11. PR Test AMD - General (latest {gen_limit} runs)",
|
|
report_data.get("pr_test_amd_general_data", {}),
|
|
)
|
|
generate_job_section_md(
|
|
f"12. PR Test Xeon - General (latest {gen_limit} runs)",
|
|
report_data.get("pr_test_xeon_general_data", {}),
|
|
)
|
|
generate_job_section_md(
|
|
f"13. PR Test XPU - General (latest {gen_limit} runs)",
|
|
report_data.get("pr_test_xpu_general_data", {}),
|
|
)
|
|
generate_job_section_md(
|
|
f"14. PR Test NPU - General (latest {gen_limit} runs)",
|
|
report_data.get("pr_test_npu_general_data", {}),
|
|
)
|
|
|
|
# Nightly Tests - General (4 workflows)
|
|
generate_job_section_md(
|
|
f"15. Nightly NVIDIA - General (latest {gen_limit} runs)",
|
|
report_data.get("nightly_nvidia_general_data", {}),
|
|
)
|
|
generate_job_section_md(
|
|
f"16. Nightly AMD - General (latest {gen_limit} runs)",
|
|
report_data.get("nightly_amd_general_data", {}),
|
|
)
|
|
generate_job_section_md(
|
|
f"17. Nightly Intel - General (latest {gen_limit} runs)",
|
|
report_data.get("nightly_intel_general_data", {}),
|
|
)
|
|
generate_job_section_md(
|
|
f"18. Nightly NPU - General (latest {gen_limit} runs)",
|
|
report_data.get("nightly_npu_general_data", {}),
|
|
)
|
|
|
|
# ========== RUNNERS ==========
|
|
summary_lines.append("---")
|
|
summary_lines.append("# 🖥️ RUNNER HEALTH")
|
|
summary_lines.append("")
|
|
|
|
# 5. Workers section
|
|
if report_data.get("runner_instance_data") and report_data.get(
|
|
"runner_instance_streak_data"
|
|
):
|
|
# Combine instance stats with streak data
|
|
combined_data = []
|
|
for instance_key, stats in report_data["runner_instance_data"].items():
|
|
streak_data = report_data["runner_instance_streak_data"].get(
|
|
instance_key, {}
|
|
)
|
|
combined_data.append(
|
|
{
|
|
"runner_name": stats.get("runner_name", "unknown"),
|
|
"current_streak": streak_data.get("current_streak", 0),
|
|
"max_streak": streak_data.get("max_streak", 0),
|
|
"failure_rate": stats["failure_rate"],
|
|
"total_jobs": stats["total_jobs"],
|
|
"unique_jobs": len(stats.get("jobs_failed", {})),
|
|
"avg_queue": stats.get("avg_queue_time_seconds", 0),
|
|
"first_failure": streak_data.get("first_failure_in_streak"),
|
|
"last_failure": streak_data.get("last_failure_in_streak"),
|
|
}
|
|
)
|
|
|
|
sorted_runners = sorted(
|
|
combined_data,
|
|
key=lambda x: (
|
|
x["current_streak"],
|
|
x["max_streak"],
|
|
x["failure_rate"],
|
|
),
|
|
reverse=True,
|
|
)
|
|
|
|
runners_with_issues = [
|
|
r for r in sorted_runners if r["current_streak"] >= 2
|
|
]
|
|
|
|
# Always show section header
|
|
summary_lines.append("## 5. Workers")
|
|
summary_lines.append("")
|
|
|
|
if runners_with_issues:
|
|
summary_lines.append(
|
|
"| Machine Name | Current Streak | Max | Fail Rate | Avg Queue | Total Jobs | Unique Jobs | First Failure | Last Failure |"
|
|
)
|
|
summary_lines.append(
|
|
"|--------------|----------------|-----|-----------|-----------|------------|-------------|---------------|--------------|"
|
|
)
|
|
|
|
for runner_data in runners_with_issues[:15]:
|
|
display_name = (
|
|
runner_data["runner_name"]
|
|
if len(runner_data["runner_name"]) <= 28
|
|
else runner_data["runner_name"][:25] + "..."
|
|
)
|
|
|
|
avg_queue_str = (
|
|
f"{runner_data['avg_queue'] / 60:.1f}m"
|
|
if runner_data["avg_queue"] > 0
|
|
else "N/A"
|
|
)
|
|
|
|
first_failure = runner_data.get("first_failure")
|
|
first_str = (
|
|
f"[Run #{first_failure['run_number']}]({first_failure.get('job_url', first_failure['url'])})"
|
|
if first_failure
|
|
else "N/A"
|
|
)
|
|
|
|
last_failure = runner_data.get("last_failure")
|
|
last_str = (
|
|
f"[Run #{last_failure['run_number']}]({last_failure.get('job_url', last_failure['url'])})"
|
|
if last_failure
|
|
else "N/A"
|
|
)
|
|
|
|
# Make entire row red if current streak >= 3
|
|
if runner_data["current_streak"] >= 3:
|
|
summary_lines.append(
|
|
f"| <span style='color:red'>`{display_name}`</span> | <span style='color:red'>{runner_data['current_streak']}</span> | <span style='color:red'>{runner_data['max_streak']}</span> | "
|
|
f"<span style='color:red'>{runner_data['failure_rate']:.1f}%</span> | <span style='color:red'>{avg_queue_str}</span> | <span style='color:red'>{runner_data['total_jobs']}</span> | <span style='color:red'>{runner_data.get('unique_jobs', 0)}</span> | <span style='color:red'>{first_str}</span> | <span style='color:red'>{last_str}</span> |"
|
|
)
|
|
else:
|
|
summary_lines.append(
|
|
f"| `{display_name}` | {runner_data['current_streak']} | {runner_data['max_streak']} | "
|
|
f"{runner_data['failure_rate']:.1f}% | {avg_queue_str} | {runner_data['total_jobs']} | {runner_data.get('unique_jobs', 0)} | {first_str} | {last_str} |"
|
|
)
|
|
|
|
summary_lines.append("")
|
|
else:
|
|
summary_lines.append(
|
|
"✅ **No runners with active failure streaks (streak >= 2)**"
|
|
)
|
|
summary_lines.append("")
|
|
|
|
# Write summary
|
|
with open(github_step_summary, "a", encoding="utf-8") as f:
|
|
f.write("\n".join(summary_lines))
|
|
|
|
print("GitHub Actions summary generated successfully")
|
|
|
|
except Exception as e:
|
|
print(f"Failed to generate GitHub Actions summary: {e}")
|
|
import traceback
|
|
|
|
traceback.print_exc()
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser(description="SGLang Consecutive Failures Analyzer")
|
|
parser.add_argument("--token", required=True, help="GitHub Personal Access Token")
|
|
parser.add_argument(
|
|
"--limit",
|
|
type=int,
|
|
default=100,
|
|
help="Number of workflow runs to analyze per workflow for general analysis (default: 100)",
|
|
)
|
|
parser.add_argument(
|
|
"--output",
|
|
default=None,
|
|
help="Output JSON file (optional, only writes if specified)",
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
|
|
analyzer = SGLangFailuresAnalyzer(args.token)
|
|
|
|
try:
|
|
# Fetch runs for each category separately
|
|
print("\n" + "=" * 80)
|
|
print("FETCHING WORKFLOW RUNS")
|
|
print("=" * 80)
|
|
|
|
# Fixed limits for scheduled runs
|
|
pr_test_scheduled_limit = 12 # Past 12 scheduled PR Test runs
|
|
nightly_scheduled_limit = 6 # Past 6 scheduled Nightly Test runs
|
|
|
|
# === SCHEDULED RUNS (9 workflows) ===
|
|
# PR Tests - Scheduled (5 workflows)
|
|
pr_test_nvidia_scheduled_runs = analyzer.get_recent_runs(
|
|
limit=pr_test_scheduled_limit,
|
|
workflow_filter=["pr-test.yml"],
|
|
filters={"event": "schedule"},
|
|
)
|
|
# These 4 don't have scheduled events, so filter by main branch instead
|
|
pr_test_amd_scheduled_runs = analyzer.get_recent_runs(
|
|
limit=pr_test_scheduled_limit,
|
|
workflow_filter=["pr-test-amd.yml"],
|
|
filters={"branch": "main"},
|
|
)
|
|
pr_test_xeon_scheduled_runs = analyzer.get_recent_runs(
|
|
limit=pr_test_scheduled_limit,
|
|
workflow_filter=["pr-test-xeon.yml"],
|
|
filters={"branch": "main"},
|
|
)
|
|
pr_test_xpu_scheduled_runs = analyzer.get_recent_runs(
|
|
limit=pr_test_scheduled_limit,
|
|
workflow_filter=["pr-test-xpu.yml"],
|
|
filters={"branch": "main"},
|
|
)
|
|
pr_test_npu_scheduled_runs = analyzer.get_recent_runs(
|
|
limit=pr_test_scheduled_limit,
|
|
workflow_filter=["pr-test-npu.yml"],
|
|
filters={"branch": "main"},
|
|
)
|
|
|
|
# Nightly Tests - Scheduled (4 workflows)
|
|
nightly_nvidia_scheduled_runs = analyzer.get_recent_runs(
|
|
limit=nightly_scheduled_limit,
|
|
workflow_filter=["nightly-test-nvidia.yml"],
|
|
filters={"event": "schedule"},
|
|
)
|
|
nightly_amd_scheduled_runs = analyzer.get_recent_runs(
|
|
limit=nightly_scheduled_limit,
|
|
workflow_filter=["nightly-test-amd.yml"],
|
|
filters={"event": "schedule"},
|
|
)
|
|
nightly_intel_scheduled_runs = analyzer.get_recent_runs(
|
|
limit=nightly_scheduled_limit,
|
|
workflow_filter=["nightly-test-intel.yml"],
|
|
filters={"event": "schedule"},
|
|
)
|
|
nightly_npu_scheduled_runs = analyzer.get_recent_runs(
|
|
limit=nightly_scheduled_limit,
|
|
workflow_filter=["nightly-test-npu.yml"],
|
|
filters={"event": "schedule"},
|
|
)
|
|
|
|
# === GENERAL RUNS (9 workflows) ===
|
|
# PR Tests - General (5 workflows)
|
|
pr_test_nvidia_general_runs = analyzer.get_recent_runs(
|
|
limit=args.limit,
|
|
workflow_filter=["pr-test.yml"],
|
|
)
|
|
pr_test_amd_general_runs = analyzer.get_recent_runs(
|
|
limit=args.limit,
|
|
workflow_filter=["pr-test-amd.yml"],
|
|
)
|
|
pr_test_xeon_general_runs = analyzer.get_recent_runs(
|
|
limit=args.limit,
|
|
workflow_filter=["pr-test-xeon.yml"],
|
|
)
|
|
pr_test_xpu_general_runs = analyzer.get_recent_runs(
|
|
limit=args.limit,
|
|
workflow_filter=["pr-test-xpu.yml"],
|
|
)
|
|
pr_test_npu_general_runs = analyzer.get_recent_runs(
|
|
limit=args.limit,
|
|
workflow_filter=["pr-test-npu.yml"],
|
|
)
|
|
|
|
# Nightly Tests - General (4 workflows)
|
|
nightly_nvidia_general_runs = analyzer.get_recent_runs(
|
|
limit=args.limit,
|
|
workflow_filter=["nightly-test-nvidia.yml"],
|
|
)
|
|
nightly_amd_general_runs = analyzer.get_recent_runs(
|
|
limit=args.limit,
|
|
workflow_filter=["nightly-test-amd.yml"],
|
|
)
|
|
nightly_intel_general_runs = analyzer.get_recent_runs(
|
|
limit=args.limit,
|
|
workflow_filter=["nightly-test-intel.yml"],
|
|
)
|
|
nightly_npu_general_runs = analyzer.get_recent_runs(
|
|
limit=args.limit,
|
|
workflow_filter=["nightly-test-npu.yml"],
|
|
)
|
|
|
|
# Choosing nvidia pr test and nightly for runner health analysis
|
|
runner_runs = pr_test_nvidia_general_runs + nightly_nvidia_general_runs
|
|
|
|
if not runner_runs:
|
|
print("No workflow runs found")
|
|
return
|
|
|
|
print("\n" + "=" * 80)
|
|
print("ANALYZING CONSECUTIVE FAILURES")
|
|
print("=" * 80)
|
|
|
|
# Analyze SCHEDULED runs
|
|
pr_test_nvidia_scheduled_data, _ = (
|
|
analyzer.analyze_consecutive_failures(pr_test_nvidia_scheduled_runs)
|
|
if pr_test_nvidia_scheduled_runs
|
|
else ({}, {})
|
|
)
|
|
pr_test_amd_scheduled_data, _ = (
|
|
analyzer.analyze_consecutive_failures(pr_test_amd_scheduled_runs)
|
|
if pr_test_amd_scheduled_runs
|
|
else ({}, {})
|
|
)
|
|
pr_test_xeon_scheduled_data, _ = (
|
|
analyzer.analyze_consecutive_failures(pr_test_xeon_scheduled_runs)
|
|
if pr_test_xeon_scheduled_runs
|
|
else ({}, {})
|
|
)
|
|
pr_test_xpu_scheduled_data, _ = (
|
|
analyzer.analyze_consecutive_failures(pr_test_xpu_scheduled_runs)
|
|
if pr_test_xpu_scheduled_runs
|
|
else ({}, {})
|
|
)
|
|
pr_test_npu_scheduled_data, _ = (
|
|
analyzer.analyze_consecutive_failures(pr_test_npu_scheduled_runs)
|
|
if pr_test_npu_scheduled_runs
|
|
else ({}, {})
|
|
)
|
|
|
|
nightly_nvidia_scheduled_data, _ = (
|
|
analyzer.analyze_consecutive_failures(nightly_nvidia_scheduled_runs)
|
|
if nightly_nvidia_scheduled_runs
|
|
else ({}, {})
|
|
)
|
|
nightly_amd_scheduled_data, _ = (
|
|
analyzer.analyze_consecutive_failures(nightly_amd_scheduled_runs)
|
|
if nightly_amd_scheduled_runs
|
|
else ({}, {})
|
|
)
|
|
nightly_intel_scheduled_data, _ = (
|
|
analyzer.analyze_consecutive_failures(nightly_intel_scheduled_runs)
|
|
if nightly_intel_scheduled_runs
|
|
else ({}, {})
|
|
)
|
|
nightly_npu_scheduled_data, _ = (
|
|
analyzer.analyze_consecutive_failures(nightly_npu_scheduled_runs)
|
|
if nightly_npu_scheduled_runs
|
|
else ({}, {})
|
|
)
|
|
|
|
# Analyze GENERAL runs
|
|
pr_test_nvidia_general_data, _ = (
|
|
analyzer.analyze_consecutive_failures(pr_test_nvidia_general_runs)
|
|
if pr_test_nvidia_general_runs
|
|
else ({}, {})
|
|
)
|
|
pr_test_amd_general_data, _ = (
|
|
analyzer.analyze_consecutive_failures(pr_test_amd_general_runs)
|
|
if pr_test_amd_general_runs
|
|
else ({}, {})
|
|
)
|
|
pr_test_xeon_general_data, _ = (
|
|
analyzer.analyze_consecutive_failures(pr_test_xeon_general_runs)
|
|
if pr_test_xeon_general_runs
|
|
else ({}, {})
|
|
)
|
|
pr_test_xpu_general_data, _ = (
|
|
analyzer.analyze_consecutive_failures(pr_test_xpu_general_runs)
|
|
if pr_test_xpu_general_runs
|
|
else ({}, {})
|
|
)
|
|
pr_test_npu_general_data, _ = (
|
|
analyzer.analyze_consecutive_failures(pr_test_npu_general_runs)
|
|
if pr_test_npu_general_runs
|
|
else ({}, {})
|
|
)
|
|
|
|
nightly_nvidia_general_data, _ = (
|
|
analyzer.analyze_consecutive_failures(nightly_nvidia_general_runs)
|
|
if nightly_nvidia_general_runs
|
|
else ({}, {})
|
|
)
|
|
nightly_amd_general_data, _ = (
|
|
analyzer.analyze_consecutive_failures(nightly_amd_general_runs)
|
|
if nightly_amd_general_runs
|
|
else ({}, {})
|
|
)
|
|
nightly_intel_general_data, _ = (
|
|
analyzer.analyze_consecutive_failures(nightly_intel_general_runs)
|
|
if nightly_intel_general_runs
|
|
else ({}, {})
|
|
)
|
|
nightly_npu_general_data, _ = (
|
|
analyzer.analyze_consecutive_failures(nightly_npu_general_runs)
|
|
if nightly_npu_general_runs
|
|
else ({}, {})
|
|
)
|
|
|
|
# Analyze runner health and consecutive failures on all runs
|
|
(
|
|
runner_stats,
|
|
runner_instance_data,
|
|
runner_streak_data,
|
|
runner_instance_streak_data,
|
|
) = analyzer.analyze_runner_health(runner_runs)
|
|
|
|
# Generate report with all datasets
|
|
report_data = analyzer.generate_failure_report(
|
|
# Scheduled runs (9 workflows)
|
|
pr_test_nvidia_scheduled_data,
|
|
pr_test_amd_scheduled_data,
|
|
pr_test_xeon_scheduled_data,
|
|
pr_test_xpu_scheduled_data,
|
|
pr_test_npu_scheduled_data,
|
|
nightly_nvidia_scheduled_data,
|
|
nightly_amd_scheduled_data,
|
|
nightly_intel_scheduled_data,
|
|
nightly_npu_scheduled_data,
|
|
# General runs (9 workflows)
|
|
pr_test_nvidia_general_data,
|
|
pr_test_amd_general_data,
|
|
pr_test_xeon_general_data,
|
|
pr_test_xpu_general_data,
|
|
pr_test_npu_general_data,
|
|
nightly_nvidia_general_data,
|
|
nightly_amd_general_data,
|
|
nightly_intel_general_data,
|
|
nightly_npu_general_data,
|
|
# Runners
|
|
runner_stats,
|
|
runner_instance_data,
|
|
runner_streak_data,
|
|
runner_instance_streak_data,
|
|
# Config
|
|
args.output,
|
|
pr_test_scheduled_limit,
|
|
nightly_scheduled_limit,
|
|
args.limit,
|
|
)
|
|
|
|
# Generate GitHub Actions summary
|
|
analyzer.generate_github_summary(report_data)
|
|
|
|
except Exception as e:
|
|
print(f"Error during analysis: {e}")
|
|
import traceback
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traceback.print_exc()
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sys.exit(1)
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if __name__ == "__main__":
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main()
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