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Unverified Commit 6f16bf9d authored by Xiaoyu Zhang's avatar Xiaoyu Zhang Committed by GitHub
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[Ci Monitor] Auto uploaded performance data to sglang_ci_data repo (#10976)

parent 5942fdb4
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.github/workflows/ci-monitor.yml
View file @ 6f16bf9d
......@@ -2,8 +2,7 @@ name: CI Monitor
on:
schedule:
# Run every 6 hours at 00:00, 06:00, 12:00, 18:00 UTC
- cron: '0 */6 * * *'
- cron: '0 */12 * * *'
workflow_dispatch:
inputs:
limit:
......@@ -16,6 +15,10 @@ concurrency:
group: ci-monitor-${{ github.ref }}
cancel-in-progress: true
permissions:
contents: write
actions: read
jobs:
ci-monitor:
if: github.repository == 'sgl-project/sglang'|| github.event_name == 'pull_request'
......@@ -50,7 +53,7 @@ jobs:
PYTHONIOENCODING: utf-8
run: |
cd scripts/ci_monitor
python ci_analyzer_perf.py --token $GITHUB_TOKEN --limit 500 --output-dir performance_tables_$(date +%Y%m%d_%H%M%S)
python ci_analyzer_perf.py --token $GITHUB_TOKEN --limit ${{ github.event.inputs.limit || '1000' }} --output-dir performance_tables_$(date +%Y%m%d_%H%M%S) --upload-to-github
- name: Upload Analysis Results
uses: actions/upload-artifact@v4
......
scripts/ci_monitor/README.md
View file @ 6f16bf9d
......@@ -24,6 +24,7 @@ A comprehensive toolkit to analyze CI failures and performance trends for the SG
- **CSV Export**: Export performance data in structured CSV format
- **Trend Analysis**: Visualize performance trends with interactive charts
- **Comprehensive Metrics**: Track output throughput, E2E latency, TTFT, accept length, and more
- **Time-Based Sampling**: Intelligent sampling strategy to cover extended time periods (up to 30 days) with limited API calls
### Common Features
- **Automated Monitoring**: GitHub Actions workflow for continuous CI and performance monitoring
......@@ -77,15 +78,90 @@ python ci_analyzer_perf.py --token YOUR_GITHUB_TOKEN
#### Advanced Usage
```bash
# Analyze last 1000 PR Test runs
# Analyze last 1000 PR Test runs (auto-enables uniform sampling for ~30 days coverage)
python ci_analyzer_perf.py --token YOUR_GITHUB_TOKEN --limit 1000
# Custom output directory
python ci_analyzer_perf.py --token YOUR_GITHUB_TOKEN --limit 500 --output-dir my_performance_data
# Use sampling with 500 runs (will use sequential mode since < 500 threshold)
python ci_analyzer_perf.py --token YOUR_GITHUB_TOKEN --limit 500
# Get ALL performance data within a specific date range (recommended for historical analysis)
python ci_analyzer_perf.py --token YOUR_GITHUB_TOKEN --start-date 2024-12-01 --end-date 2024-12-31
# Get complete data for the last week
python ci_analyzer_perf.py --token YOUR_GITHUB_TOKEN --start-date $(date -d '7 days ago' +%Y-%m-%d) --end-date $(date +%Y-%m-%d)
# Upload results to GitHub repository for sharing
python ci_analyzer_perf.py --token YOUR_GITHUB_TOKEN --limit 1000 --upload-to-github
```
**Important**: Make sure your GitHub token has `repo` and `workflow` permissions, otherwise you'll get 404 errors.
## Data Collection Strategies
The Performance Analyzer offers multiple strategies for collecting performance data to suit different analysis needs.
### 1. Uniform Sampling Strategy
**When to use**: Daily monitoring and trend analysis over extended periods.
- **Automatically enabled** when `--limit >= 500`
- **Disabled** for smaller limits (< 500) to maintain backward compatibility
#### How it works:
- Collects data uniformly across a 30-day period
- Ensures even time distribution of samples
- Provides consistent coverage for trend analysis
#### Example with 1000 Runs:
- **Time Range**: Last 30 days
- **Distribution**: 1000 samples evenly distributed across the period
- **Coverage**: ~33 samples per day on average
### 2. Date Range Collection
**When to use**: Historical analysis, specific period investigation, or complete data collection.
Use `--start-date` and `--end-date` parameters to get **ALL** CI runs within a specific time range.
#### Features:
- **Complete Data**: Gets every CI run in the specified range (no sampling)
- **No Limit**: Ignores the `--limit` parameter
- **Flexible Range**: Specify any date range you need
- **Historical Analysis**: Perfect for investigating specific time periods
#### Date Format:
- Use `YYYY-MM-DD` format (e.g., `2024-12-01`)
- Both parameters are optional:
- Only `--start-date`: Gets all runs from that date to now
- Only `--end-date`: Gets all runs from 30 days ago to that date
- Both: Gets all runs in the specified range
### 3. Sequential Collection (Traditional)
**When to use**: Quick checks or when you only need recent data.
- **Default behavior** for `--limit < 500`
- Gets the most recent CI runs in chronological order
- Fast and simple for immediate analysis
### Comparison
| Strategy | Use Case | Time Coverage | Data Completeness | API Efficiency |
|----------|----------|---------------|-------------------|----------------|
| **Uniform Sampling** | Daily monitoring, trends | ~30 days | Sampled | High |
| **Date Range** | Historical analysis | Any range | Complete | Variable |
| **Sequential** | Quick checks | 3-4 days | Complete (recent) | High |
### Benefits
- **Flexible Analysis**: Choose the right strategy for your needs
- **Extended Coverage**: Up to 30 days with sampling, unlimited with date ranges
- **Complete Data**: Get every run in a specific period when needed
- **API Efficiency**: Optimized for different use patterns
## Parameters
### CI Analyzer Parameters
......@@ -101,8 +177,11 @@ python ci_analyzer_perf.py --token YOUR_GITHUB_TOKEN --limit 500 --output-dir my
| Parameter | Default | Description |
|-----------|---------|-------------|
| `--token` | Required | GitHub Personal Access Token |
| `--limit` | 100 | Number of PR Test runs to analyze |
| `--limit` | 100 | Number of PR Test runs to analyze (ignored when using date range) |
| `--output-dir` | performance_tables | Output directory for CSV tables and PNG charts |
| `--start-date` | None | Start date for date range query (YYYY-MM-DD format) |
| `--end-date` | None | End date for date range query (YYYY-MM-DD format) |
| `--upload-to-github` | False | Upload results to sglang-bot/sglang-ci-data repository |
## Getting GitHub Token
......
scripts/ci_monitor/ci_analyzer_perf.py
View file @ 6f16bf9d
......@@ -5,6 +5,7 @@ Collect performance data based on actual log format
"""
import argparse
import base64
import csv
import os
import re
......@@ -109,6 +110,10 @@ class SGLangPerfAnalyzer:
# Setup matplotlib fonts and styles
self._setup_matplotlib()
# GitHub data repository settings
self.data_repo = "sglang-bot/sglang-ci-data"
self.data_branch = "main"
def _setup_matplotlib(self):
"""Setup matplotlib fonts and styles"""
# Set fonts
......@@ -122,9 +127,27 @@ class SGLangPerfAnalyzer:
rcParams["axes.grid"] = True
rcParams["grid.alpha"] = 0.3
def get_recent_runs(self, limit: int = 100) -> List[Dict]:
"""Get recent CI run data"""
print(f"Getting recent {limit} PR Test runs...")
def get_recent_runs(
self, limit: int = 100, start_date: str = None, end_date: str = None
) -> List[Dict]:
"""Get recent CI run data with multiple collection strategies"""
# If date range is specified, get all data in that range
if start_date or end_date:
return self._get_date_range_runs(start_date, end_date)
print(f"Getting PR Test runs (limit: {limit})...")
# Use sampling strategy if limit >= 500, otherwise use sequential
if limit >= 500:
print(f"Using uniform sampling for {limit} runs to cover ~30 days...")
return self._get_sampled_runs(limit)
else:
return self._get_sequential_runs(limit)
def _get_sequential_runs(self, limit: int) -> List[Dict]:
"""Original sequential method for smaller limits"""
print(f"Using sequential sampling for {limit} runs...")
pr_test_runs = []
page = 1
......@@ -169,6 +192,288 @@ class SGLangPerfAnalyzer:
return pr_test_runs
def _get_sampled_runs(self, limit: int) -> List[Dict]:
"""Uniform sampling method for 30-day coverage"""
from datetime import datetime, timedelta
# Uniform sampling across 30 days
sampled_runs = self._sample_time_period(limit, days_back=30, uniform=True)
print(
f"Sampled {len(sampled_runs)} runs from 30-day period (requested: {limit})"
)
return sampled_runs
def _sample_time_period(
self,
target_samples: int,
days_back: int,
skip_recent_days: int = 0,
uniform: bool = False,
) -> List[Dict]:
"""Sample runs from a specific time period"""
from datetime import datetime, timedelta
# Calculate time range
end_time = datetime.utcnow() - timedelta(days=skip_recent_days)
start_time = end_time - timedelta(days=days_back - skip_recent_days)
sampling_type = "uniform" if uniform else "systematic"
print(
f" {sampling_type.title()} sampling {target_samples} runs from {start_time.strftime('%Y-%m-%d')} to {end_time.strftime('%Y-%m-%d')}"
)
collected_runs = []
page = 1
per_page = 100
total_in_period = 0
while True:
url = f"{self.base_url}/repos/{self.repo}/actions/runs"
params = {"per_page": per_page, "page": page}
try:
response = self.session.get(url, params=params)
response.raise_for_status()
data = response.json()
if not data.get("workflow_runs"):
break
period_runs = []
for run in data["workflow_runs"]:
if run.get("name") != "PR Test":
continue
created_at = run.get("created_at", "")
if created_at:
try:
run_time = datetime.fromisoformat(
created_at.replace("Z", "+00:00")
).replace(tzinfo=None)
if start_time <= run_time <= end_time:
period_runs.append(run)
total_in_period += 1
except:
continue
collected_runs.extend(period_runs)
# Progress indicator every 5 pages
if page % 5 == 0:
print(
f" Page {page}: Found {total_in_period} runs in target period, collected {len(collected_runs)} total"
)
# Check if we've gone past our time window
if data["workflow_runs"]:
last_run_time_str = data["workflow_runs"][-1].get("created_at", "")
if last_run_time_str:
try:
last_run_time = datetime.fromisoformat(
last_run_time_str.replace("Z", "+00:00")
).replace(tzinfo=None)
if last_run_time < start_time:
print(f" Reached time boundary at page {page}")
break
except:
pass
if len(data["workflow_runs"]) < per_page:
break
page += 1
time.sleep(0.1)
except requests.exceptions.RequestException as e:
print(f" Error getting data for time period: {e}")
break
print(
f" Found {total_in_period} runs in time period, collected {len(collected_runs)} for sampling"
)
# Debug: Show time range of collected data
if collected_runs:
collected_runs_sorted = sorted(
collected_runs, key=lambda x: x.get("created_at", "")
)
earliest = (
collected_runs_sorted[0].get("created_at", "")[:10]
if collected_runs_sorted
else "N/A"
)
latest = (
collected_runs_sorted[-1].get("created_at", "")[:10]
if collected_runs_sorted
else "N/A"
)
print(f" Collected data spans from {earliest} to {latest}")
# Sample from collected runs
if len(collected_runs) <= target_samples:
return collected_runs
if uniform:
# Uniform sampling: sort by time and select evenly distributed samples
collected_runs.sort(key=lambda x: x.get("created_at", ""))
step = len(collected_runs) / target_samples
sampled_runs = []
for i in range(target_samples):
index = int(i * step)
if index < len(collected_runs):
sampled_runs.append(collected_runs[index])
else:
# Systematic sampling for even distribution
step = len(collected_runs) / target_samples
sampled_runs = []
for i in range(target_samples):
index = int(i * step)
if index < len(collected_runs):
sampled_runs.append(collected_runs[index])
print(
f" Sampled {len(sampled_runs)} runs from {len(collected_runs)} available"
)
# Debug: Show time range of sampled data
if sampled_runs:
sampled_runs_sorted = sorted(
sampled_runs, key=lambda x: x.get("created_at", "")
)
earliest = (
sampled_runs_sorted[0].get("created_at", "")[:10]
if sampled_runs_sorted
else "N/A"
)
latest = (
sampled_runs_sorted[-1].get("created_at", "")[:10]
if sampled_runs_sorted
else "N/A"
)
print(f" Sampled data spans from {earliest} to {latest}")
return sampled_runs
def _get_date_range_runs(
self, start_date: str = None, end_date: str = None
) -> List[Dict]:
"""Get all CI runs within specified date range"""
from datetime import datetime, timedelta
# Parse dates
if start_date:
try:
start_time = datetime.strptime(start_date, "%Y-%m-%d")
except ValueError:
raise ValueError(
f"Invalid start_date format. Use YYYY-MM-DD, got: {start_date}"
)
else:
# Default to 30 days ago if no start date
start_time = datetime.utcnow() - timedelta(days=30)
if end_date:
try:
end_time = datetime.strptime(end_date, "%Y-%m-%d") + timedelta(
days=1
) # Include the end date
except ValueError:
raise ValueError(
f"Invalid end_date format. Use YYYY-MM-DD, got: {end_date}"
)
else:
# Default to now if no end date
end_time = datetime.utcnow()
# Validate date range
if start_time >= end_time:
raise ValueError(
f"start_date ({start_date}) must be before end_date ({end_date})"
)
print(
f"Getting ALL CI runs from {start_time.strftime('%Y-%m-%d')} to {end_time.strftime('%Y-%m-%d')}"
)
collected_runs = []
page = 1
per_page = 100
total_in_period = 0
while True:
url = f"{self.base_url}/repos/{self.repo}/actions/runs"
params = {"per_page": per_page, "page": page}
try:
response = self.session.get(url, params=params)
response.raise_for_status()
data = response.json()
if not data.get("workflow_runs"):
break
# Filter runs in date range and PR Test runs
period_runs = []
for run in data["workflow_runs"]:
if run.get("name") != "PR Test":
continue
created_at = run.get("created_at", "")
if created_at:
try:
run_time = datetime.fromisoformat(
created_at.replace("Z", "+00:00")
).replace(tzinfo=None)
if start_time <= run_time <= end_time:
period_runs.append(run)
total_in_period += 1
except:
continue
collected_runs.extend(period_runs)
# Progress indicator every 5 pages
if page % 5 == 0:
print(
f" Page {page}: Found {total_in_period} runs in date range, collected {len(collected_runs)} total"
)
# Check if we've gone past our time window
if data["workflow_runs"]:
last_run_time_str = data["workflow_runs"][-1].get("created_at", "")
if last_run_time_str:
try:
last_run_time = datetime.fromisoformat(
last_run_time_str.replace("Z", "+00:00")
).replace(tzinfo=None)
if last_run_time < start_time:
print(f" Reached time boundary at page {page}")
break
except:
pass
if len(data["workflow_runs"]) < per_page:
break
page += 1
time.sleep(0.1)
except requests.exceptions.RequestException as e:
print(f" Error getting data for date range: {e}")
break
print(
f"Found {total_in_period} runs in date range {start_time.strftime('%Y-%m-%d')} to {end_time.strftime('%Y-%m-%d')}"
)
# Sort by creation time (newest first)
collected_runs.sort(key=lambda x: x.get("created_at", ""), reverse=True)
return collected_runs
def get_job_logs(self, run_id: int, job_name: str) -> Optional[str]:
"""Get logs for specific job with early exit optimization"""
try:
......@@ -349,8 +654,7 @@ class SGLangPerfAnalyzer:
f" Found {test_name} performance data: {list(perf_data.keys())}"
)
time.sleep(0.2) # Slightly longer delay between runs to be API-friendly
time.sleep(0.2)
return all_test_data
def generate_performance_tables(
......@@ -682,6 +986,321 @@ class SGLangPerfAnalyzer:
print("\n" + "=" * 60)
def upload_file_to_github(
self, file_path: str, github_path: str, commit_message: str
) -> bool:
"""Upload a file to GitHub repository with retry logic"""
max_retries = 30
retry_count = 0
while retry_count < max_retries:
try:
# Read file content
with open(file_path, "rb") as f:
content = f.read()
# Encode content to base64
content_encoded = base64.b64encode(content).decode("utf-8")
# Check if file exists to get SHA
check_url = (
f"{self.base_url}/repos/{self.data_repo}/contents/{github_path}"
)
check_response = self.session.get(check_url)
sha = None
if check_response.status_code == 200:
sha = check_response.json().get("sha")
# Prepare upload data
upload_data = {
"message": commit_message,
"content": content_encoded,
"branch": self.data_branch,
}
if sha:
upload_data["sha"] = sha
# Upload file
response = self.session.put(check_url, json=upload_data)
if response.status_code in [200, 201]:
print(f" ✅ Uploaded: {github_path}")
return True
elif response.status_code == 403:
retry_count += 1
wait_time = min(2**retry_count, 30)
print(
f" ⚠️ Upload forbidden (403) for {github_path}, retrying in {wait_time}s... (attempt {retry_count}/{max_retries})"
)
if retry_count >= max_retries:
print(
f" ❌ Failed to upload {github_path} after {max_retries} attempts (403 Forbidden)"
)
return False
time.sleep(wait_time)
else:
response.raise_for_status()
except requests.exceptions.RequestException as e:
retry_count += 1
wait_time = min(2**retry_count, 30)
print(
f" ⚠️ Upload error for {github_path} (attempt {retry_count}/{max_retries}): {e}"
)
if retry_count >= max_retries:
print(
f" ❌ Failed to upload {github_path} after {max_retries} attempts: {e}"
)
return False
print(f" Retrying in {wait_time}s...")
time.sleep(wait_time)
except Exception as e:
print(f" ❌ Failed to upload {github_path}: {e}")
return False
return False
def upload_performance_data_to_github(self, output_dir: str):
"""Upload performance_tables to GitHub with original structure"""
print("📤 Uploading performance data to GitHub...")
# Check if target repository exists with retry logic
repo_url = f"{self.base_url}/repos/{self.data_repo}"
max_retries = 30
retry_count = 0
print(f"🔍 Checking repository access to {self.data_repo}...")
while retry_count < max_retries:
try:
repo_response = self.session.get(repo_url)
if repo_response.status_code == 200:
print(f"✅ Repository {self.data_repo} is accessible")
break
elif repo_response.status_code == 404:
print(
f"❌ Repository {self.data_repo} does not exist or is not accessible"
)
print(" Please ensure:")
print(" 1. The repository exists")
print(" 2. Your GitHub token has access to this repository")
print(" 3. Your token has 'contents:write' permission")
return
elif repo_response.status_code == 403:
retry_count += 1
wait_time = min(2**retry_count, 60) # Exponential backoff, max 60s
print(
f"⚠️ Repository access forbidden (403), retrying in {wait_time}s... (attempt {retry_count}/{max_retries})"
)
if retry_count >= max_retries:
print(
f"❌ Failed to access repository after {max_retries} attempts"
)
print(" This might be due to:")
print(" 1. GitHub API rate limiting")
print(" 2. Token permissions issue")
print(" 3. Repository access restrictions")
return
time.sleep(wait_time)
else:
retry_count += 1
wait_time = min(2**retry_count, 60)
print(
f"⚠️ Repository access failed with status {repo_response.status_code}, retrying in {wait_time}s... (attempt {retry_count}/{max_retries})"
)
if retry_count >= max_retries:
print(
f"❌ Failed to access repository {self.data_repo} after {max_retries} attempts"
)
return
time.sleep(wait_time)
except Exception as e:
retry_count += 1
wait_time = min(2**retry_count, 60)
print(
f"⚠️ Error checking repository (attempt {retry_count}/{max_retries}): {e}"
)
if retry_count >= max_retries:
print(
f"❌ Failed to check repository after {max_retries} attempts: {e}"
)
return
print(f" Retrying in {wait_time}s...")
time.sleep(wait_time)
# Generate timestamp for this upload
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
uploaded_count = 0
# Upload all files maintaining original structure
for root, dirs, files in os.walk(output_dir):
for file in files:
local_path = os.path.join(root, file)
# Keep original directory structure
rel_path = os.path.relpath(local_path, output_dir)
github_path = f"performance_data/{timestamp}/{rel_path}".replace(
"\\", "/"
)
# Upload file
commit_msg = f"Add performance data: {rel_path} ({timestamp})"
if self.upload_file_to_github(local_path, github_path, commit_msg):
uploaded_count += 1
print(f"📤 Uploaded {uploaded_count} files to GitHub")
# Print access info
base_url = f"https://github.com/{self.data_repo}/tree/{self.data_branch}/performance_data/{timestamp}"
print(f"🔗 View uploaded data at: {base_url}")
# Generate GitHub Actions summary
self._generate_github_summary(output_dir, timestamp)
def _generate_github_summary(self, output_dir: str, timestamp: str):
"""Generate GitHub Actions summary with performance data"""
try:
# Check if running in GitHub Actions
github_step_summary = os.environ.get("GITHUB_STEP_SUMMARY")
if not github_step_summary:
print("ℹ️ Not running in GitHub Actions, skipping summary generation")
return
print("📊 Generating GitHub Actions summary...")
# Collect all CSV and PNG files
csv_files = []
png_files = []
for root, dirs, files in os.walk(output_dir):
for file in files:
file_path = os.path.join(root, file)
rel_path = os.path.relpath(file_path, output_dir)
if file.endswith(".csv"):
csv_files.append((file_path, rel_path))
elif file.endswith(".png"):
png_files.append((file_path, rel_path))
# Sort files by job and test name
csv_files.sort(key=lambda x: x[1])
png_files.sort(key=lambda x: x[1])
# Generate markdown summary
summary_lines = []
summary_lines.append("# 📊 SGLang Performance Analysis Report")
summary_lines.append("")
summary_lines.append(f"**Analysis Timestamp:** {timestamp}")
summary_lines.append(f"**Total CSV Files:** {len(csv_files)}")
summary_lines.append(f"**Total Chart Files:** {len(png_files)}")
summary_lines.append("")
# GitHub data repository link
base_url = f"https://github.com/{self.data_repo}/tree/{self.data_branch}/performance_data/{timestamp}"
summary_lines.append(f"🔗 **[View All Data on GitHub]({base_url})**")
summary_lines.append("")
# Group by job
job_groups = {}
for csv_path, rel_path in csv_files:
# Extract job name from path: job_summary/test_name.csv
parts = rel_path.split("/")
if len(parts) >= 2:
job_name = parts[0].replace("_summary", "")
test_name = parts[1].replace(".csv", "")
if job_name not in job_groups:
job_groups[job_name] = []
job_groups[job_name].append((csv_path, test_name, rel_path))
# Generate summary for each job
for job_name in sorted(job_groups.keys()):
summary_lines.append(f"## 🚀 {job_name}")
summary_lines.append("")
tests = job_groups[job_name]
tests.sort(key=lambda x: x[1]) # Sort by test name
for csv_path, test_name, rel_path in tests:
summary_lines.append(f"### 📈 {test_name}")
# Add CSV data preview
try:
with open(csv_path, "r", encoding="utf-8") as f:
lines = f.readlines()
if len(lines) > 1: # Has header and data
summary_lines.append("")
summary_lines.append("**Recent Performance Data:**")
summary_lines.append("")
# Show header
header = lines[0].strip()
summary_lines.append(
f"| {' | '.join(header.split(','))} |"
)
summary_lines.append(
f"| {' | '.join(['---'] * len(header.split(',')))} |"
)
# Show most recent 5 records (CSV is already sorted newest first)
data_lines = lines[1:]
for line in data_lines[
:5
]: # Take first 5 lines (most recent)
if line.strip():
summary_lines.append(
f"| {' | '.join(line.strip().split(','))} |"
)
summary_lines.append("")
except Exception as e:
summary_lines.append(f"*Error reading CSV data: {e}*")
summary_lines.append("")
# Add chart image if exists
test_prefix = rel_path.replace(".csv", "")
matching_charts = [
(png_path, png_rel)
for png_path, png_rel in png_files
if png_rel.startswith(test_prefix)
]
for png_path, chart_rel_path in matching_charts:
chart_url = f"https://github.com/{self.data_repo}/raw/{self.data_branch}/performance_data/{timestamp}/{chart_rel_path}"
# Extract metric name from filename: test_name_metric_name.png
filename = os.path.basename(chart_rel_path)
metric_name = filename.replace(f"{test_name}_", "").replace(
".png", ""
)
summary_lines.append(
f"**{self._format_metric_name(metric_name)} Trend:**"
)
summary_lines.append("")
summary_lines.append(
f"![{test_name}_{metric_name}]({chart_url})"
)
summary_lines.append("")
summary_lines.append("---")
summary_lines.append("")
# Write summary to GitHub Actions
with open(github_step_summary, "w", 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 CI Performance Analyzer")
......@@ -697,6 +1316,21 @@ def main():
default="performance_tables",
help="Output directory (default: performance_tables)",
)
parser.add_argument(
"--upload-to-github",
action="store_true",
help="Upload results to sglang-bot/sglang-ci-data repository",
)
parser.add_argument(
"--start-date",
type=str,
help="Start date for date range query (YYYY-MM-DD format). When specified with --end-date, gets ALL runs in range.",
)
parser.add_argument(
"--end-date",
type=str,
help="End date for date range query (YYYY-MM-DD format). When specified with --start-date, gets ALL runs in range.",
)
args = parser.parse_args()
......@@ -705,7 +1339,7 @@ def main():
try:
# Get CI run data
runs = analyzer.get_recent_runs(args.limit)
runs = analyzer.get_recent_runs(args.limit, args.start_date, args.end_date)
if not runs:
print("No CI run data found")
......@@ -717,6 +1351,10 @@ def main():
# Generate performance tables
analyzer.generate_performance_tables(test_data, args.output_dir)
# Upload to GitHub if requested
if args.upload_to_github:
analyzer.upload_performance_data_to_github(args.output_dir)
# Generate summary report
analyzer.generate_summary_report(test_data)
......
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