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Washington5533

guarftrain MCP Server

by Washington5533
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Python
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Training Guardian Agent · 训练守护智能体

CI Version Python

一行命令，训练脚本零行改动，获得完整守护能力。

One command. Zero changes to your training script. Full guardian capabilities.

guarftrain init && guarftrain watch -- python train.py --epochs 20

What's New in v0.2.0

Feature

Description

guarftrain CLI

pip install 后全局可用，替换旧 python run.py

guarftrain init

自动扫描训练脚本，生成 contract.yaml

guarftrain check

环境自检：Python/GPU/依赖/项目结构

Dashboard 远程配置

外部 Agent 通过 MCP 控制 Dashboard 图表/面板，用户操作受 dirty flag 保护

Agent 图表推荐

chart_selection 决策点：Agent 分析训练状态，推荐应关注的指标组

MCP 委托模式

外部 Claude Code 连接时内置 Agent 进入 provisional 模式，决策可被覆盖

增量图表更新

Dashboard 实时推送图表数据，不再全量重建

依赖瘦身

核心安装 ~2MB，torch/anthropic 按需安装

Related MCP server: mlflow-mcp-server

What does it do? · 它做什么？

Phase · 阶段

Capability · 能力

How · 方式

训练前 Pre-flight

GPU 显存预估 + batch 推荐

guarftrain preflight

训练中 During

GPU+Loss 监控告警 / 崩溃自动恢复 / LLM 决策

guarftrain watch

训练后 Post

摘要+AI 解读 / Checkpoint 分析 / 模型可视化 / 推理

guarftrain summarize

跨实验 Cross

自然语言查询 / 实验对比 / 数据导入

guarftrain query "best lr?"

外部接入 External

MCP 32 工具 + Dashboard 远程配置 + Agent 图表推荐

guarftrain start

Quick Start · 快速开始

Install · 安装

# 方式 1: pip 安装（推荐，轻量核心 ~2MB，torch 已有不重装）
pip install guarftrain

# 方式 2: 从源码安装
git clone https://github.com/user/guarftrain.git
cd guarftrain
pip install .

# 按需安装可选组件
pip install guarftrain[agent]       # AI 决策层 (anthropic)
pip install guarftrain[mcp]         # MCP 外部 Agent 接入
pip install guarftrain[dashboard]   # Web 控制面板
pip install guarftrain[full]        # 全部安装

Three steps to guard · 三步守护

# 1. 初始化项目（自动扫描训练脚本，生成配置）
cd /path/to/your-project
guarftrain init

# 2. 守护训练（纯规则，零外部依赖）
guarftrain watch -- python train.py --epochs 20

# 3. 或启用 AI + Dashboard + MCP
guarftrain watch --agent --with-dashboard --with-mcp -- python train.py --epochs 20

What does the training script need? · 训练脚本要满足什么？

Four contracts (just good training hygiene):

  1. --resume / --ckpt flags for checkpoint resumption

  2. cp_{epoch}/model.pth with epoch/model_state_dict/optimizer_state_dict

  3. Structured logging: epoch {n} loss {v} val_acc {v} lr {v}

  4. Importable: train:build_model / train:get_dataloaders

Missing any one? Only the corresponding capability is disabled — training still runs.

四项契约（写好训练脚本的基本功），缺任一项只关对应能力，不阻断训练。

Architecture · 架构

┌─ Guardian Process (sidecar) ────────────────────────────────────┐
│                                                                  │
│  CLI (run.py) ──→ 14 subcommands                                 │
│  ├─ watch ──→ Watchdog: Popen + crash recovery + CLI rewrite    │
│  │             └─ Monitor: log tail + GPU poll + anomaly detect  │
│  │                  └─ AgentAdvisor: LLM decide → intervene       │
│  ├─ serve ──→ MCP Server: 32 tools (22 read + 10 write)          │
│  ├─ start ──→ Dashboard + MCP one-click                        │
│  └─ experiments / query / compare ──→ Cross-experiment analysis  │
│                                                                  │
│  Decision Layers · 决策分层:                                      │
│  ┌─ Contract (hard boundary, human-defined)                     │
│  ├─ Agent (LLM, optional, within action space)                  │
│  ├─ Rules (deterministic, always-on fallback)                   │
│  ├─ MCP (external agent access, dual-mode delegation)           │
│  └─ Dashboard (remote config, dirty-flag user protection)       │
│                                                                  │
│  Training Process: python train.py (0 changes required)          │
└──────────────────────────────────────────────────────────────────┘

CLI Commands · 命令速查

Command

Description

init

Auto-detect project + generate contract.yaml

check

Environment readiness check (deps, GPU, config)

watch

Guard any training command

start

Dashboard + MCP one-click launch

serve

Standalone MCP server

contract check

Validate training script contract

preflight

GPU memory estimate + batch recommendation

analyze

Scan existing checkpoints

experiments

List all historical experiments

query

Natural language query ("best lr?")

compare

Compare two experiments

visualize

Model structure visualization (D3.js HTML)

infer

Run inference with checkpoint

gallery

Image filtering + selection

dashboard

Web control panel (standalone)

project

Project context management (init/show/scan/fill)

MCP Tools · MCP 工具

24 read-only (always available, no auth):

get_training_status · get_metrics_history · list_checkpoints · compare_checkpoints · get_anomaly_history · get_recovery_history · get_summary · get_agent_decision_log · get_contract_status · list_contract_proposals · list_experiments · query_experiment · compare_experiments · get_model_structure · get_guardian_mode · get_gallery_config · get_import_format · inspect_source · get_training_log · get_post_training_checklist · get_pending_decisions · get_dashboard_config · recommend_charts · list_dashboard_templates

11 write (token auth + training-phase gating):

trigger_recovery · restart_with_params · stop_training · approve_contract_proposal · reject_contract_proposal · run_visualization · set_gallery_config · run_inference · submit_import · resolve_decision · set_dashboard_config

→ Full API reference: MCP_API_REFERENCE.md

Configuration · 配置

Three layers, zero secrets in YAML:

DEFAULTS  <  guardian.yaml  <  GUARDIAN_* env vars  <  CLI flags
# configs/guardian.yaml — only override what you need
watchdog:
  max_retries: 3
monitor:
  poll_interval: 5
mcp:
  enable_write_tools: true
# Env override: GUARDIAN_ + section + __ + key
export GUARDIAN_WATCHDOG__MAX_RETRIES=5
export GUARDIAN_MCP_TOKEN=your-secret   # write tool auth

Project Status · 项目状态

Metric

Value

Version

0.2.0

Modules

16 (cp_1 ~ cp_16)

Production code

~10,500 lines

Tests

221 (CI on push)

MCP tools

35 (24 read + 11 write)

CLI commands

16

Test coverage

~13% (core paths: 100%)

Python

3.10+

Docs · 文档索引

Document

Content

ARCHITECTURE.md

Architecture & workflow (ZH)

DEPLOYMENT.md

User manual (ZH)

MCP.md

MCP integration guide (ZH)

MCP_API_REFERENCE.md

35-tool API reference (ZH)

MCP_QUICKSTART.md

5-minute MCP onboarding (ZH)

IMPLEMENTATION_REPORT.md

Per-module completion report (ZH)

checkpoint/INDEX.md

Module index cp_1~cp_16 (ZH)

checkpoint/cp_10.md

MCP layer design doc (ZH)

License

MIT

This server cannot be installed

A
license - permissive license
-
quality - not tested
B
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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