# API Testing and Profiling Guide

This guide explains how to test and profile the YLFF API endpoints using the test script.

## Quick Start

### 1. Start the API Server

```bash
# From project root
python -m uvicorn ylff.api:app --host 0.0.0.0 --port 8000
```

Or if running in Docker/RunPod, the server should already be running.

### 2. Run the Test Script

```bash
# Basic test (auto-detects test data)
python scripts/experiments/test_api_with_profiling.py

# Test with specific data
python scripts/experiments/test_api_with_profiling.py \
    --sequence-dir data/arkit_ba_validation/ba_work/images \
    --arkit-dir data/arkit_ba_validation

# Test against remote server
python scripts/experiments/test_api_with_profiling.py \
    --base-url https://your-pod-id-8000.proxy.runpod.net

# Save results to custom location
python scripts/experiments/test_api_with_profiling.py \
    --output data/test_results/api_test_$(date +%Y%m%d_%H%M%S).json
```

## Test Script Features

The test script (`scripts/experiments/test_api_with_profiling.py`) automatically:

1. **Tests all API endpoints**:

   - Health check (`/health`)
   - API info (`/`)
   - Models list (`/models`)
   - Sequence validation (`/api/v1/validate/sequence`)
   - ARKit validation (`/api/v1/validate/arkit`)
   - Job management (`/api/v1/jobs`, `/api/v1/jobs/{job_id}`)
   - Profiling endpoints (metrics, hot paths, latency, system)

2. **Profiles code execution**:

   - Tracks API request latencies
   - Monitors function execution times
   - Identifies hot paths (most time-consuming operations)
   - Tracks system resources (CPU, memory, GPU)

3. **Auto-detects test data**:

   - Looks for `assets/` folder first
   - Falls back to `data/` folder
   - Uses existing validation data if available

4. **Generates reports**:
   - Saves detailed JSON results
   - Prints profiling summary
   - Shows latency breakdown by stage

## Test Data Structure

The script looks for test data in this order:

1. **`assets/examples/ARKit/`** - ARKit video and metadata
2. **`assets/examples/*/`** - Image sequences
3. **`data/arkit_ba_validation/`** - Existing ARKit validation data
4. **`data/*/ba_work/images/`** - BA work directories with images

### Creating Test Assets

If you want to use a custom `assets/` folder:

```bash
mkdir -p assets/examples/ARKit
# Place your ARKit video and metadata here
# Or place image sequences in assets/examples/your_sequence/
```

## Profiling Results

The test script generates profiling data in two ways:

### 1. Local Profiling (in test script)

The script uses the `Profiler` class to track:

- API request durations
- Function execution times
- Memory usage
- GPU memory usage

### 2. Server-Side Profiling (via API)

The API server also tracks profiling data. Access it via:

```bash
# Get all metrics
curl http://localhost:8000/api/v1/profiling/metrics

# Get hot paths (top time-consuming operations)
curl http://localhost:8000/api/v1/profiling/hot-paths

# Get latency breakdown by stage
curl http://localhost:8000/api/v1/profiling/latency

# Get system metrics (CPU, memory, GPU)
curl http://localhost:8000/api/v1/profiling/system

# Get stats for specific stage
curl http://localhost:8000/api/v1/profiling/stage/api_request

# Reset profiling data
curl -X POST http://localhost:8000/api/v1/profiling/reset
```

## Example Output

```
================================================================================
YLFF API Testing and Profiling
================================================================================
Base URL: http://localhost:8000
Start time: 2024-01-15T10:30:00

[1/11] Testing /health endpoint...
  ✓ Health check passed: {'status': 'healthy'}

[2/11] Testing / endpoint...
  ✓ API info retrieved: YLFF API v1.0.0

[3/11] Testing /models endpoint...
  ✓ Found 5 models

[4/11] Testing /api/v1/validate/sequence endpoint...
  Using sequence: data/arkit_ba_validation/ba_work/images
  ✓ Validation job queued: abc123-def456-...

...

================================================================================
Profiling Summary
================================================================================
Total entries: 45
Stages tracked: 3
Functions tracked: 11

Latency Breakdown:
  api_request                   12.345s ( 45.2%) avg: 0.123s calls: 100
  validate_sequence              8.901s ( 32.6%) avg: 8.901s calls: 1
  validate_arkit                 6.234s ( 22.2%) avg: 6.234s calls: 1
```

## Interpreting Results

### Latency Breakdown

Shows where time is spent:

- **api_request**: Time spent in API layer (network + processing)
- **validate_sequence**: Time spent in sequence validation
- **validate_arkit**: Time spent in ARKit validation
- **gpu**: GPU computation time
- **cpu**: CPU computation time
- **data_loading**: Data I/O time

### Hot Paths

Shows the most time-consuming functions:

- Functions with highest total execution time
- Useful for identifying bottlenecks

### System Metrics

Shows resource utilization:

- CPU usage percentage
- Memory usage percentage
- GPU memory usage (if available)

## Troubleshooting

### Connection Errors

If you get connection errors:

```bash
# Check if server is running
curl http://localhost:8000/health

# Check server logs
# (if running locally, check terminal output)
```

### Missing Test Data

If test data is not found:

```bash
# Specify paths explicitly
python scripts/experiments/test_api_with_profiling.py \
    --sequence-dir /path/to/images \
    --arkit-dir /path/to/arkit
```

### Timeout Errors

If requests timeout:

```bash
# Increase timeout (default: 300s)
python scripts/experiments/test_api_with_profiling.py --timeout 600
```

## Continuous Profiling

For continuous profiling during development:

```bash
# Run tests in a loop
while true; do
    python scripts/experiments/test_api_with_profiling.py --output "data/profiling/run_$(date +%s).json"
    sleep 60
done
```

## Integration with CI/CD

Add to your CI pipeline:

```yaml
- name: Test API Endpoints
  run: |
    python scripts/experiments/test_api_with_profiling.py \
      --base-url http://localhost:8000 \
      --output test_results/api_test.json
```

## Next Steps

- Review profiling results to identify bottlenecks
- Optimize hot paths identified in profiling
- Use system metrics to tune resource allocation
- Compare profiling results across different model sizes/configurations