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ENOVA

ENOVA is an open-source service for LLM deployment, monitoring, injection, and auto-scaling. With the increasing popularity of large language model (LLM) backend systems, deploying stable serverless LLM services on GPU clusters with auto-scaling has become essential. However, challenges arise due to the diversity and co-location of applications in GPU clusters, leading to low service quality and GPU utilization.

To address these issues, ENOVA deconstructs the LLM service execution process and incorporates a configuration recommendation module for automatic deployment on any GPU cluster and a performance detection module for auto-scaling. Additionally, ENOVA features a deployment execution engine for efficient GPU cluster scheduling.

With ENOVA, users can:

• Build and deploy LLM with only few command lines
• Recommend optimal computing resources and operating parameter configurations for LLM
• Quick experience LLM performance with ENOVA by request injection offered
• In-depth observation of LLM operating status and abnormal self-healing
• Achieve load balancing through autoscaling

Here are ENOVA's core technical points and values:

• Configuration Recommendation: ENOVA can automatically identify various LLMs (open-source or fine-tuned), and recommend the most suitable parameter configurations for deploying the model, such as GPU type, maximal batch size, replicas, weights etc.
• Performance Detection: ENOVA enables real-time monitoring of service quality and abnormal usage of computational resources.
• Deep Observability: By conducting in-depth observation of the entire chain of task execution of large models, we can provide the best guidance for maximizing model performance and optimizing the utilization of computing resources.
• Deployment & Execution: Achieve rapid deployment and model serving, aiming to achieve autoscaling objectives.

Based on the aforementioned capabilities of ENOVA, we can ensure that LLM services with ENOVA are:

• Stable: Achieve a high availability rate of over 99%, ensuring stable operation without downtime.
• Cost-effective: Increase resource utilization by over 50% and enhance comprehensive GPU memory utilization from 40% to 90%.
• Efficient: Boost deployment efficiency by over 10 times and run LLMs with lower latency and higher throughputs
• Strong Scalability: ENOVA can automatically cluster different task types, thus adapting to applications in many fields.

✈️ Getting Started

We can demonstrate the powerful capabilities of E NOVA in model deployment and performance monitoring by swiftly running an open-source AI model on your GPUs and conducting request injection tests.

Requirement

• OS: Linux
• Docker
• Python: >=3.10
• GPU: Nvidia GPUs with compute capability 7.0 or higher

Installation

1. Make sure you have Docker and Python Environment.

2. Use pip to install ENOVA:

# Create a new Python environment
conda create -n enova_env python=3.10
conda activate enova_env

# Install ENOVA
# Source: https://pypi.python.org/simple/
pip install enova_instrumentation_llmo
pip install enova

3. To verify the installation, run:

enova -h 

The expected output is:

Usage: enova [OPTIONS] COMMAND [ARGS]...

  ███████╗███╗   ██╗ ██████╗ ██╗   ██╗ █████╗
  ██╔════╝████╗  ██║██╔═══██╗██║   ██║██╔══██╗
  █████╗  ██╔██╗ ██║██║   ██║██║   ██║███████║
  ██╔══╝  ██║╚██╗██║██║   ██║╚██╗ ██╔╝██╔══██║
  ███████╗██║ ╚████║╚██████╔╝ ╚████╔╝ ██║  ██║
  ╚══════╝╚═╝  ╚═══╝ ╚═════╝   ╚═══╝  ╚═╝  ╚═╝

  ENOVA is an open-source llm deployment, monitoring, injection and auto-scaling service.
  It provides a set of commands to deploy stable serverless serving of LLM on GPU clusters with auto-scaling.

Options:
  -v, --version  Show the version and exit.
  -h, --help     Show this message and exit.

Commands:
  algo      Run the autoscaling service.
  app       Start ENOVA application server.
  serving     Deploy the target LLM and launch the LLM API service.
  injector  Run the autoscaling service.
  mon       Run the monitors of LLM server
  pilot     Start an all-in-one LLM server with deployment, monitoring,...
  webui     Build agent at this page based on the launched LLM API service.

Quickstart

1. Start an all-in-one LLM server with deployment, monitoring, injection and auto-scaling service:

enova pilot run --model mistralai/Mistral-7B-Instruct-v0.1

# openai
enova pilot run --model mistralai/Mistral-7B-Instruct-v0.1 --vllm_mode openai

Use proxy to download LLMs:

enova pilot run --model mistralai/Mistral-7B-Instruct-v0.1 --hf_proxy xxx

2. Check Deployed LLM service via ENOVA Application Server:

http://localhost:8182/instance

3. Test the Deployed LLM service with an prompt:

Use WebUI:

http://localhost:8501

Use Shell:

curl -X POST http://localhost:9199/generate \
-d '{
"prompt": "San Francisco is a",
"max_tokens": 1024,
"temperature": 0.9,
"top_p": 0.9
}'

# openai
curl http://localhost:9199/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "mistralai/Mistral-7B-Instruct-v0.1",
"prompt": "San Francisco is a",
"max_tokens": 128,
"temperature": 0
}'

4. Monitor the LLM Service Quality via ENOVA Application Server:

http://localhost:8182/instance

5. Stop all service

enova pilot stop --service all

🏠 LLM Deployment Service

In addition to offering an all-in-one solution for service deployment, monitoring, and autoscaling, ENOVA also provides support for single modules.

The LLM deployment service facilitates the deployment of LLMs and provides a stable API for accessing LLMs.

Run an LLM

enova serving run --model mistralai/Mistral-7B-Instruct-v0.1

enova serving run --model mistralai/Mistral-7B-Instruct-v0.1 --host 127.0.0.1 --port 9199

LLM WebUI Service

This service features a WebUI page for dialog interaction, where the serving host and port for the LLM server, and the host and port for the WebUI service, are configurable parameters.

enova webui run --serving_host 127.0.0.1 --serving_port 9199 --host 127.0.0.1 --port 8501

Autoscaling Module

The autoscaling service is automatically launched and managed by escaler module.

Request Injection Module

We implemented a request injection module using JMeter to simulate real user requests for evaluating LLM performance. The module allows simulation of request arrival probabilities using two modes: Poisson distribution and normal distribution. Further details on the injection operation are available at:

http://localhost:8182/instance

🏠 LLM Monitoring Service

Manage LLM Monitoring Service

The monitoring system is designed for monitoring and autoscaling, which contains real-time data collection, storage, and consumption. We can manage the LLM monitoring service via:

1. Start llm monitoring service

enova mon start

2. Check service status

enova mon status

3. Stop monitoring service

enova mon stop

Monitoring Metrics

Monitoring metrics are collected using the DCGM exporter, Prometheus exporters, and the OpenTelemetry collector. A brief description is provided in the following tables. For more details, please refer to the Grafana dashboard.

Metric Type	Metric Description
API Service	The number of requests sent to LLM services per second
API Service	The number of requests processed by LLM services per second
API Service	The number of requests successfully processed per second
API Service	The success rate of requests processed by LLM services per second
API Service	The number of requests being processed by LLM services
API Service	The average execution time per request processed by LLM services
API Service	The average request size of requests per second
API Service	The average response size of requests per second
LLM Performance	The average prompt throughput per second
LLM Performance	The average generation throughput per second
LLM Performance	The number of requests being processed by the deployed LLM
LLM Performance	The number of requests being pended by the deployed LLM
LLM Performance	The utilization ratio of memory allocated for KV cache
GPU Utilization	DCGM Metrics, like DCGM_FI_DEV_GPU_UTIL.

More detailed metrics can be viewed on our application server. In the process of deploying the all-in-one llm service with ENOVA, we also created the corresponding indicator dashboard in Grafana and supported viewing of more detailed trace data.

• URL：http://localhost:32827/dashboards
• Default user account：admin
• Password：grafana

GPU Metrics

Traces

📚 Reference

@inproceedings{tao2024ENOVA,
  title={ENOVA: Autoscaling towards Cost-effective and Stable Serverless LLM Serving},
  author={Tao Huang and Pengfei Chen and Kyoka Gong and Jocky Hawk and Zachary Bright and Wenxin Xie and Kecheng Huang and Zhi Ji},
  booktitle={arXiv preprint arXiv:},
  year={2024}
}

🤝 Join Our Community

We use Slack workspace for the collaboration on building ENOVA.

• Slack workspace
• Browse our website for more informations