# SOLLOL Unified Observability System

**Date:** October 18, 2025
**Feature:** Unified observability and control for both task distribution and RPC distributed inference

## Overview

SOLLOL now provides **the same level of observability and control** for RPC distributed inference as it does for task distribution. Whether you're using `distributed task`, `distributed model`, or `distributed both`, you get:

✅ **Automatic coordinator startup**
✅ **Health monitoring for all nodes**
✅ **Real-time metrics and performance tracking**
✅ **Unified CLI commands**
✅ **Dashboard integration**

---

## Architecture

### Task Distribution Mode (`distributed task`)

```
User Request
    ↓
Ollama Pool (3 nodes)
    ↓
Parallel execution across Ollama instances
    ↓
Metrics tracked per node
```

**Observability:**
- `nodes` - Show all Ollama nodes + metrics
- `stats` - Request distribution statistics
- Dashboard - Real-time routing and performance

### RPC Distributed Inference Mode (`distributed model`)

```
User Request
    ↓
Coordinator Manager
    ↓
Auto-detect GGUF from Ollama blobs
    ↓
Auto-start coordinator (if needed)
    ↓
llama.cpp coordinator (port 18080)
    ↓
RPC backends (GPU/CPU workers)
    ↓
Distributed inference
```

**Observability:**
- `nodes` - Show coordinator + RPC backends + metrics
- Coordinator auto-start with health checks
- Model auto-detection from Ollama storage
- Dashboard - Coordinator metrics + RPC backend status

### Hybrid Mode (`distributed both`)

```
User Request
    ↓
Route by model size
    ↓
Small models → Ollama Pool (parallel tasks)
Large models → Coordinator (RPC distributed inference)
    ↓
Unified metrics for both paths
```

**Observability:**
- `nodes` - Show BOTH Ollama nodes AND coordinator + RPC backends
- Routing decision transparency
- Metrics per routing path

---

## Features

### 1. Auto-Coordinator Startup

When you enable `distributed model`, the system automatically:

1. **Detects if coordinator is already running**
   ```
   🔍 Checking coordinator status...
   ✅ Coordinator already running at 127.0.0.1:18080
   ```

2. **Auto-detects GGUF model** from Ollama blob storage
   - Priority 1: `SOLLOL_MODEL_PATH` environment variable
   - Priority 2: `codellama:13b` (known hash)
   - Priority 3: Largest GGUF file in Ollama blobs (>1GB)

3. **Auto-discovers RPC backends** from Redis registry
   ```
   🔍 Discovered 2 RPC backend(s):
      • 10.9.66.45:50052
      • 10.9.66.48:50052
   ```

4. **Starts coordinator** if not running
   ```
   🚀 Starting coordinator: llama-server --model <path> --rpc 10.9.66.45:50052,10.9.66.48:50052
   ⏳ Waiting for coordinator to be ready (model loading ~40s)...
   ✅ Coordinator started successfully on 127.0.0.1:18080
   ```

### 2. Unified `nodes` Command

**Before (limited visibility):**
```bash
SynapticLlamas> nodes
🔀 OLLAMA NODES (Task Distribution)
  • 10.9.66.45:11434
  • 10.9.66.48:11434
```

**After (complete visibility):**
```bash
SynapticLlamas> nodes

🎯 COORDINATOR (RPC Distributed Inference)
──────────────────────────────────────────────────────────────────────
  URL: http://127.0.0.1:18080
  Status: ✅ HEALTHY
  PID: 3318509
  Model: codellama:13b
  RPC Backends: 2 configured

🔀 OLLAMA NODES (Task Distribution - Parallel Agents)
┌────────────────────┬─────────┬──────────┬────────┬─────────┐
│ URL                │ Status  │ Requests │ Errors │ Latency │
├────────────────────┼─────────┼──────────┼────────┼─────────┤
│ 10.9.66.45:11434   │ ✅ UP   │ 127      │ 0      │ 234ms   │
│ 10.9.66.48:11434   │ ✅ UP   │ 134      │ 1      │ 198ms   │
└────────────────────┴─────────┴──────────┴────────┴─────────┘

🔗 RPC BACKENDS (Distributed Inference - Large Models)
┌──────────────────┬──────────┬──────────┬──────────────┬─────────────┐
│ Address          │ Status   │ Requests │ Success Rate │ Avg Latency │
├──────────────────┼──────────┼──────────┼──────────────┼─────────────┤
│ 10.9.66.45:50052 │ ✅ HEALTHY│ 45       │ 100.0%       │ 1234ms      │
│ 10.9.66.48:50052 │ ✅ HEALTHY│ 47       │ 100.0%       │ 1189ms      │
└──────────────────┴──────────┴──────────┴──────────────┴─────────────┘
```

### 3. Health Monitoring

**Automatic health checks:**
- Coordinator: HTTP `/health` endpoint every 30s
- RPC backends: Tracked via request success/failure
- Ollama nodes: Load-based health scores

**Metrics tracked:**
- Total requests
- Success rate
- Average latency
- Error counts
- Load scores

### 4. SOLLOL Dashboard Integration

The coordinator metrics are automatically integrated into the SOLLOL dashboard:

**Dashboard URL:** `http://localhost:8080`

**New Metrics:**
- Coordinator status (up/down)
- Model loaded
- RPC backend count
- Request distribution (Ollama vs RPC)
- Latency comparison (task distribution vs distributed inference)

---

## Configuration

### Environment Variables

```bash
# Model path (optional - auto-detected if not set)
export SOLLOL_MODEL_PATH="/path/to/model.gguf"

# Coordinator location (supports remote coordinator)
export SOLLOL_COORDINATOR_HOST="192.168.1.10"
export SOLLOL_COORDINATOR_PORT="18080"

# Redis for backend discovery
export REDIS_URL="redis://localhost:6379"
```

### SynapticLlamas Config (`~/.synapticllamas.json`)

```json
{
  "coordinator_url": "http://127.0.0.1:18080",
  "model_sharding_enabled": true,
  "task_distribution_enabled": false,
  "rpc_backends": []
}
```

**Note:** `rpc_backends` can be empty - they will be auto-discovered from Redis if available.

---

## Usage Examples

### Example 1: Pure RPC Sharding (No Ollama)

```bash
# Stop all Ollama instances
systemctl stop ollama

# Start RPC backends on worker nodes
ssh 10.9.66.45 'rpc-server --host 0.0.0.0 --port 50052'
ssh 10.9.66.48 'rpc-server --host 0.0.0.0 --port 50052'

# Register backends in Redis
PYTHONPATH=src python3 src/sollol/scripts/register_gpu_node.py --host 10.9.66.45 --port 50052
PYTHONPATH=src python3 src/sollol/scripts/register_gpu_node.py --host 10.9.66.48 --port 50052

# Start SynapticLlamas
cd ~/SynapticLlamas
python main.py

# Enable RPC distributed inference
SynapticLlamas> distributed model
✅ MODEL SHARDING MODE
   Using 2 RPC backend(s)
   Model: codellama:13b (all phases, sharded via RPC)

🔍 Checking coordinator status...
🎯 Found codellama:13b at /usr/share/ollama/.ollama/models/blobs/...
🔍 Discovered 2 RPC backend(s):
   • 10.9.66.45:50052
   • 10.9.66.48:50052
🚀 Starting coordinator...
✅ Coordinator ready at 127.0.0.1:18080

# Check status
SynapticLlamas> nodes
🎯 COORDINATOR (RPC Distributed Inference)
  URL: http://127.0.0.1:18080
  Status: ✅ HEALTHY
  Model: codellama:13b
  RPC Backends: 2 configured

# Query
SynapticLlamas> Explain quantum entanglement
📝 Content Detection: research (confidence: 0.80, chunks: 5)
🔀 Using HybridRouter for codellama:13b
📍 Routing codellama:13b to llama.cpp coordinator for RPC distributed inference
✅ Response from distributed inference across 2 CPU nodes
```

### Example 2: Hybrid Mode (Ollama + RPC)

```bash
# Start SynapticLlamas with both enabled
SynapticLlamas> distributed both
✅ HYBRID MODE (Task Distribution + Distributed Inference)
   Task distribution: 3 Ollama nodes
   Model sharding: 2 RPC backends

🔍 Checking coordinator status...
✅ Coordinator ready at 127.0.0.1:18080

# Small model → Ollama pool
SynapticLlamas> What is 2+2? (use llama3:8b)
🔀 Using HybridRouter for llama3:8b
📍 Routing llama3:8b to Ollama pool (estimated small model)
✅ Response from 10.9.66.45:11434

# Large model → RPC coordinator
SynapticLlamas> Explain quantum physics (use codellama:13b)
🔀 Using HybridRouter for codellama:13b
📍 Routing codellama:13b to llama.cpp coordinator for RPC distributed inference
✅ Response from distributed inference

# Check routing decisions
SynapticLlamas> nodes
[Shows both Ollama nodes AND coordinator + RPC backends]
```

---

## Implementation Details

### CoordinatorManager Class

**File:** `/home/joker/SOLLOL/src/sollol/coordinator_manager.py`

**Key Methods:**
- `ensure_running()` - Check if coordinator is running, start if needed
- `_detect_ollama_model()` - Auto-detect GGUF from Ollama blobs
- `_discover_rpc_backends()` - Auto-discover RPC backends from Redis
- `start()` - Start coordinator process
- `check_health()` - HTTP health check
- `get_metrics()` - Get coordinator metrics
- `get_status()` - Get comprehensive status for CLI

**Features:**
- Process management with subprocess
- Automatic model detection
- RPC backend discovery
- Health monitoring
- Metrics collection

### Integration Points

**1. DistributedOrchestrator** (`/home/joker/SynapticLlamas/distributed_orchestrator.py`)
- Lines 124-156: Coordinator auto-start logic
- Creates CoordinatorManager when `enable_distributed_inference=True`
- Ensures coordinator is running before creating RayHybridRouter

**2. Main CLI** (`/home/joker/SynapticLlamas/main.py`)
- Lines 1123-1140: Enhanced `nodes` command to show coordinator
- Passes `coordinator_url` to DistributedOrchestrator
- Displays coordinator status alongside Ollama nodes

**3. RayHybridRouter** (`/home/joker/SOLLOL/src/sollol/ray_hybrid_router.py`)
- Uses `coordinator_host` and `coordinator_base_port` for routing
- Routes large models to coordinator HTTP API
- Fallback to Ollama if coordinator unavailable

---

## Benefits

### For Users

1. **Zero Configuration** - Auto-detection of models and backends
2. **Automatic Startup** - Coordinator starts when needed
3. **Unified Interface** - Same commands for all modes
4. **Complete Visibility** - See all nodes, backends, and metrics
5. **Graceful Degradation** - Falls back to Ollama if coordinator fails

### For Developers

1. **Modular Design** - CoordinatorManager is reusable
2. **Async-First** - Built with asyncio for scalability
3. **Health Monitoring** - Built-in metrics and health checks
4. **Extensible** - Easy to add new backends or coordinators
5. **Observable** - Comprehensive logging and metrics

---

## Future Enhancements

### Planned Features

1. **Multi-Coordinator Support**
   - Load balance across multiple coordinators
   - Failover to backup coordinators
   - Geographic routing

2. **Advanced Health Checks**
   - gRPC health checks for RPC backends
   - Model-specific health metrics
   - Performance-based routing

3. **Dashboard Enhancements**
   - Real-time coordinator metrics charts
   - RPC backend latency graphs
   - Request distribution heatmaps

4. **Auto-Scaling**
   - Auto-start additional RPC backends on demand
   - Scale down when load decreases
   - Cost optimization for cloud deployments

5. **Enhanced Metrics**
   - Token throughput per backend
   - Memory usage per RPC worker
   - Model shard distribution visualization

---

## Troubleshooting

### Coordinator Won't Start

**Symptom:**
```
❌ Coordinator failed to start within 60 seconds
```

**Solutions:**
1. Check model path exists:
   ```bash
   ls /usr/share/ollama/.ollama/models/blobs/
   ```

2. Verify RPC backends are reachable:
   ```bash
   nc -zv 10.9.66.45 50052
   nc -zv 10.9.66.48 50052
   ```

3. Check coordinator logs:
   ```bash
   tail -f /tmp/coordinator-18080.log
   ```

### RPC Backends Not Discovered

**Symptom:**
```
ℹ️  No RPC backends discovered
```

**Solutions:**
1. Check Redis is running:
   ```bash
   redis-cli ping
   ```

2. Verify backends are registered:
   ```bash
   redis-cli keys "rpc:backend:*"
   ```

3. Manually add backends:
   ```bash
   SynapticLlamas> rpc add 10.9.66.45:50052
   SynapticLlamas> rpc add 10.9.66.48:50052
   ```

### Model Auto-Detection Fails

**Symptom:**
```
⚠️  No suitable model found in Ollama blobs
```

**Solutions:**
1. Set model path explicitly:
   ```bash
   export SOLLOL_MODEL_PATH="/path/to/codellama-13b.gguf"
   ```

2. Pull model via Ollama first:
   ```bash
   ollama pull codellama:13b
   ```

3. Check Ollama blob directory:
   ```bash
   ls -lh /usr/share/ollama/.ollama/models/blobs/
   ```

---

## Performance Comparison

### Task Distribution (3 Ollama nodes)
- **Use Case:** Parallel research, multiple small tasks
- **Throughput:** ~30 req/min
- **Latency:** 200-300ms per request
- **Best For:** Small models (<8B), parallel workflows

### RPC Distributed Inference (1 coordinator + 2 workers)
- **Use Case:** Large model inference, single complex task
- **Throughput:** ~5 req/min
- **Latency:** 1000-2000ms per request
- **Best For:** Large models (>13B), complex reasoning

### Hybrid Mode (Both)
- **Use Case:** Mixed workload
- **Throughput:** Optimal for both
- **Routing:** Automatic based on model size
- **Best For:** Production deployments

---

## Conclusion

The unified observability system provides:

✅ **Automatic coordinator management**
✅ **Intelligent model auto-detection**
✅ **Unified node visibility**
✅ **Same CLI experience for all modes**
✅ **Production-ready monitoring**

Whether you're using task distribution, RPC distributed inference, or both, you now have complete visibility and control over your distributed inference infrastructure.

**Next Steps:**
1. Restart SynapticLlamas to load the new features
2. Try `distributed model` to see auto-coordinator startup
3. Run `nodes` to see the unified view
4. Check the dashboard at `http://localhost:8080`

🎯 **The future is distributed, observable, and automatic!**
