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quantmind-deploy

Deploys, updates, and troubleshoots the QuantMind quantitative trading platform via Docker Compose.

1.5k|337|Updated Apr 15, 2026
Search Tags:#devops#deployment#troubleshooting#postgresql#docker#quantitative trading#gpu training
One-click install
npx skills add https://github.com/qusong0627/QuantMind --skill quantmind-deploy-qusong0627
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quantmind-deploy
Source: https://github.com/qusong0627/QuantMind/tree/main/skills/quantmind-deploy
Command: npx skills add https://github.com/qusong0627/QuantMind --skill quantmind-deploy-qusong0627

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Deploying the QuantMind quantitative trading platform involves 11+ Docker services, database initialization, and environment-specific pitfalls; this Skill gives an AI assistant a complete runbook covering pre-deployment checks, one-click deployment, health verification, troubleshooting, updates, and cloud GPU training node setup. ## Core Features & Use Cases - Guided Deployment: Step-by-step one-click, quick, and manual Docker Compose deployment with environment requirements, mirror source selection, and interactive prompt preparation. - Post-Deployment Verification: Ordered health checks for containers, PostgreSQL, Redis, backend API, login authentication, and frontend access, with the users-table check flagged as the most common failure point. - Troubleshooting & Updates: Diagnostic trees for deployment failures plus update.sh workflows covering code sync, image rebuilds, idempotent SQL patches, and version verification. - Use Case: A user tells the AI "help me deploy QuantMind" or "deployment failed, help me troubleshoot"; the AI follows the runbook to run quick-deploy.sh, verify the users table exists, test the login endpoint, and diagnose failures. ## Quick Start Ask the AI assistant to deploy QuantMind on your server or to diagnose why your QuantMind deployment failed.

Frequently Asked Questions about quantmind-deploy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I deploy QuantMind with Docker?▼

Run the one-click script: curl the quick-deploy.sh from the Gitee repository and execute it with sudo. It installs Docker, clones the code to /opt/quantmind, builds images, initializes the database via db_init.sql, and runs health checks across six stages.

How do I troubleshoot a failed QuantMind deployment?▼

Check containers with docker compose ps, verify PostgreSQL with pg_isready and Redis with redis-cli ping, then test the API health endpoint and login. The most common failure is a missing users table, meaning db_init.sql did not execute successfully.

What are the system requirements for QuantMind deployment?▼

QuantMind requires Ubuntu 22.04 or 24.04 on x86_64 architecture only, since the Qlib framework does not publish ARM builds. Minimum specs are 4 CPU cores, 16GB RAM, and 100GB disk; model training recommends 64GB or more memory.

Can QuantMind run on ARM servers or Apple Silicon?▼

No. QuantMind only supports x86_64/AMD64 because Microsoft Qlib publishes x86_64-only packages, so ARM (aarch64) machines cannot install Qlib and are rejected by the deployment script.

How do I update an existing QuantMind deployment?▼

Run sudo bash deploy/update.sh in /opt/quantmind. It pulls code, rebuilds the backend image, restarts services, applies idempotent SQL patches from data/upgrade_*.sql, and runs a health check without touching database data.

Can QuantMind train models on remote GPU servers?▼

Yes. QuantMind supports AutoDL remote GPU nodes configured in config/training_nodes.yaml using native_python execution mode over SSH. Training artifacts are rsynced back to the master node and registered automatically.

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