Standardized deployment programme
UltraRAG offers two reliable deployment options:
- Docker Containerized Deployment::
- Pre-installed with all dependencies
- Supports GPU acceleration (requires NVIDIA Docker runtime)
- Built-in health check mechanism
- Conda environment deployment::
- Provides precise version control of Python packages
- Suitable for local development and debugging
- Supports model hot updating
Detailed deployment process
Docker Solution:
- Make sure the host installs Docker 20.10+ and NVIDIA drivers
- fulfillment
docker-compose up --build -d - interviews
http://localhost:8843
Conda Program:
- Creating the environment:
conda create -n ultrarag python=3.10 - Activate the environment:
conda activate ultrarag - Install the dependencies:
pip install -r requirements.txt
Operation and Maintenance Recommendations
Production environments are recommended to be clustered and deployed with Docker Swarm or Kubernetes, and the system is preconfigured with the Prometheus monitoring interface (default port 9090).
This answer comes from the articleUltraRAG: A One-Stop RAG System Solution to Simplify Data Construction and Model Fine-TuningThe
May not be reproduced without permission:AI productivity tools " How to Rapidly Deploy an Enterprise RAG System and Ensure Environmental Consistency?
