vllm-manager 0.2.0

Multi-instance vLLM cluster orchestration and log management

vLLM Manager

Multi-Instance vLLM Cluster Management & Log Aggregation

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📖 About

vLLM Manager provides multi-instance vLLM cluster management, automatic log collection, and load balancing.

• Start vLLM: Uses official CLI (python -m vllm.entrypoints.openai.api_server)
• Send Requests: Uses official OpenAI SDK (from openai import OpenAI)
• Cluster Management: Auto start/stop, health checks, failover
• Log Collection: All instance logs automatically saved to files

✨ Features

• 🎯 Multi-Instance Management: Start/stop multiple vLLM instances with one command
• 📝 Automatic Logging: Log files named by model and port for easy identification
• 🔄 Failover: Auto-retry on other instances when request fails
• ❤️ Health Monitoring: Continuous instance health checks
• 🔧 OpenAI SDK: Returns standard OpenAI client, seamless integration
• ⚖️ Load Balancing: Round-robin request distribution

🛠️ Tech Stack

• Python 3.8+
• vLLM - LLM inference engine
• OpenAI SDK - API client
• Requests - HTTP client

📦 Installation

# 1. Install vLLM
pip install vllm

# 2. Install dependencies
pip install -r requirements.txt

# Or install individually
pip install openai requests

🚀 Quick Start

Basic Usage

from vllm_manager import VLLMCluster, VLLMInstance

# 1. Create cluster
cluster = VLLMCluster(log_dir="./vllm_logs")

# 2. Add instances
cluster.add_instance(VLLMInstance(
    name="server1",
    model="facebook/opt-125m",
    port=8000,
    gpu_memory_utilization=0.5,
))

# 3. Start all instances
cluster.start_all()

# 4. Get OpenAI client
client = cluster.get_openai_client()

# 5. Send requests (auto load-balanced)
response = client.completions.create(
    model="facebook/opt-125m",
    prompt="San Francisco is a",
)
print(response)

# 6. Stop cluster
cluster.stop_all()

Multi-Model Example

from vllm_manager import VLLMCluster, VLLMInstance

cluster = VLLMCluster()

# Add instances with different models
cluster.add_instance(VLLMInstance(
    name="qwen-server",
    model="Qwen/Qwen2.5-1.5B-Instruct",
    port=8000,
))

cluster.add_instance(VLLMInstance(
    name="llama-server",
    model="meta-llama/Llama-2-7b-chat",
    port=8001,
))

cluster.start_all()

# View model name for each instance
for instance in cluster.instances.values():
    print(f"{instance.name}: {instance.served_model_name}")
# qwen-server: Qwen2.5-1.5B-Instruct
# llama-server: Llama-2-7b-chat

# Log files automatically include model name
# vllm_Qwen2.5-1.5B-Instruct_8000_20260227_101234.log
# vllm_Llama-2-7b-chat_8001_20260227_101235.log

📖 API Reference

VLLMInstance

VLLMInstance(
    name: str,                    # Instance name
    model: str,                   # Model name/path
    port: int = 8000,             # Port
    host: str = "0.0.0.0",        # Host
    log_dir: Optional[Path] = None,
    
    # vLLM parameters (inherited from AsyncEngineArgs)
    gpu_memory_utilization: float = 0.9,
    tensor_parallel_size: int = 1,
    pipeline_parallel_size: int = 1,
    max_model_len: Optional[int] = None,
    quantization: Optional[str] = None,
    dtype: str = "auto",
    # ... supports all AsyncEngineArgs parameters
)

# Properties
instance.served_model_name  # Model name (last path component)
instance.base_url           # http://host:port
instance.api_url            # http://host:port/v1
instance.log_file           # Log file path

VLLMCluster

cluster = VLLMCluster(log_dir="./vllm_logs")
cluster.add_instance(instance: VLLMInstance)
cluster.start_all()
cluster.stop_all()
cluster.health_check()
client = cluster.get_openai_client()

📝 Log Management

Log File Naming

Log files are named by model name + port + timestamp for easy identification:

./vllm_logs/
├── vllm_manager_20260227_101234.log          # Manager logs
├── vllm_Qwen2.5-1.5B-Instruct_8000_101235.log  # Qwen model
└── vllm_Llama-2-7b-chat_8001_101236.log        # Llama model

View Logs

from vllm_manager import LogAggregator

aggregator = LogAggregator(log_dir="./vllm_logs")

# Get all logs
logs = aggregator.get_all_logs(limit=100)
for log in logs:
    print(f"[{log.timestamp}] {log.instance}: {log.message}")

# Export to JSON
aggregator.export_json("logs.json")

❓ FAQ

Q: Why use vLLM Manager?

A: When you need to run multiple vLLM instances (different models, different GPUs), vLLM Manager provides unified cluster management and log collection.

Q: Which vLLM parameters are supported?

A: All AsyncEngineArgs parameters are supported, since VLLMInstance inherits from AsyncEngineArgs.

Q: How are log files named?

A: Format is vllm_{model_name}_{port}_{timestamp}.log, where model_name is the last component of the model path (e.g., Qwen2.5-1.5B-Instruct).

Q: How do I check which model each instance is running?

A: Use the instance.served_model_name property:

for instance in cluster.instances.values():
    print(f"{instance.name}: {instance.served_model_name}")

🤝 Contributing

Issues and Pull Requests are welcome!

# 1. Fork the repo
# 2. Create your branch (git checkout -b feature/AmazingFeature)
# 3. Commit your changes (git commit -m 'Add some AmazingFeature')
# 4. Push to the branch (git push origin feature/AmazingFeature)
# 5. Open a Pull Request

📄 License

MIT License - See LICENSE file for details.

📬 Contact

• Project URL: https://github.com/AiKiAi-stack/vllm_startup
• Issues: https://github.com/AiKiAi-stack/vllm_startup/issues
• Author: AiKiAi-stack

🙏 Acknowledgements

• vLLM - LLM inference engine
• OpenAI Python SDK - API client