# 模型平台

**Repository Path**: cqszkj/model-platform

## Basic Information

- **Project Name**: 模型平台
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-07-14
- **Last Updated**: 2026-07-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Model Platform

Standalone operations platform for preparing corpora, training GPU models, tracking progress, evaluating candidates, and exporting deployment-compatible artifacts.

The first release supports:

- Student Chinese/English AI-vs-human adversarial classifiers.
- The 11-family `style_analysis` attribution ensemble consumed by `aigc-gpu-detector`.
- Resumable browser uploads, dataset/model CRUD and downloads, a MySQL-backed GPU0 queue, live job events, evaluation gates, and automatic candidate registration.

The platform never writes into a production model directory. Every successful run atomically registers a versioned candidate archive with checksums, lineage, metrics, and its training manifest in the model library.

## Quick start

```bash
cp .env.example .env
docker compose -f compose.yml up --build -d
```

Open `http://localhost:18090` and sign in with the administrator credentials from `.env`.

## Server deployment

The production server uses a stable self-updating entrypoint. The standard update mode builds the
new release, interrupts queued or active training jobs during the final switch, verifies all four
services, external MySQL, and GPU0, and restores the previous application images if the new release
fails health checks.

```bash
bash /devdata/disk_ssd1/docker/model-platform-script/deploy.sh --interrupt-active
```

First-time server setup and exact verification commands are documented in
[`deploy/README.md`](deploy/README.md).

Detailed deployment, data layout, model contracts, and verification commands are documented under `docs/`.