isagellm-control-plane 0.1.0.1

sageLLM Control Plane - Intelligent request routing, scheduling, and engine lifecycle management

sageLLM Control Plane

Intelligent request routing, scheduling, and engine lifecycle management for sageLLM.

Features

• 🎯 Scheduling Policies - FIFO, Priority, SLO-aware, Cost-optimized, Adaptive
• ⚖️ Load Balancing - Intelligent request routing across multiple engine instances
• 📈 Autoscaling - SLA-based autoscaling for Prefill/Decode instances
• 🔄 Engine Lifecycle - Spawn, stop, health check, auto-restart
• 📊 Observability - Metrics collection, performance monitoring
• 🧩 Parallelism - TP, PP, DP, EP strategy optimization

Installation

# 从 PyPI 安装
pip install isagellm-control-plane

# With GPU monitoring
pip install isagellm-control-plane[gpu]

# With Prometheus metrics
pip install isagellm-control-plane[metrics]

🚀 开发者快速开始

git clone [e-mail]:intellistream/sagellm-control-plane.git
cd sagellm-control-plane
./quickstart.sh   # 一键安装开发环境（含依赖）

# 或手动安装
pip install -e ".[dev]"

运行测试：

pytest tests/ -v

Quick Start

from sagellm_control import ControlPlaneManager

# Create manager with mock mode (no GPU required)
manager = ControlPlaneManager(
    scheduling_policy="adaptive",
    routing_strategy="load_balanced",
    mode="local",  # Use local async executor
)

# Register a mock engine
manager.register_engine(
    engine_id="engine-001",
    model_id="mock-model",
    host="localhost",
    port=8000,
)

# Schedule a request
decision = await manager.schedule_request(
    request_id="req-001",
    prompt="Hello, world!",
    max_tokens=128,
)

print(f"Scheduled to: {decision.instance_id}")

执行层 API (Task0.8)

Control Plane 提供完整的推理执行接口：

from sagellm_control import ControlPlaneManager, MockControlPlane
from sagellm_protocol import Request

# 使用 Mock 模式（无 GPU 依赖）
cp = MockControlPlane()
cp.register_engine("engine-001", model_id="test-model", host="localhost", port=8000)

# 1. 非流式推理
request = Request(
    request_id="req-001",
    trace_id="trace-001",
    model="test-model",
    prompt="Hello, how are you?",
    max_tokens=100,
    stream=False,
)
response = await cp.execute_request(request)
print(f"Output: {response.output_text}")
print(f"TTFT: {response.metrics.ttft_ms:.2f} ms")

# 2. 流式推理
async for event in cp.stream_request(request):
    if event.event == "delta":
        print(event.chunk, end="", flush=True)

# 3. 文本嵌入
embeddings = await cp.get_embeddings(
    texts=["Text 1", "Text 2", "Text 3"],
    model_id="embedding-model"
)
print(f"Generated {len(embeddings)} embeddings of dimension {len(embeddings[0])}")

更多示例请参考 examples/execution_layer_demo.py。

Architecture

sagellm_control/
├── types.py           # Core data types (RequestMetadata, EngineInfo, etc.)
├── strategies/        # Scheduling policies (FIFO, Priority, SLO, etc.)
├── executors/         # Execution coordinators (HTTP, LocalAsync, Mock)
├── router.py          # Request routing and load balancing
├── autoscaler.py      # SLA-based autoscaling
├── parallelism.py     # Parallelism strategy optimization
├── manager.py         # Main ControlPlaneManager
└── engine_lifecycle.py # Engine lifecycle management

Mock-First Development

All modules support mock mode for testing without GPU:

from sagellm_control.executors import MockExecutionCoordinator

# Use mock executor for CI/CD
executor = MockExecutionCoordinator()
result = await executor.execute(request)

License

Proprietary - IntelliStream