一个轻量级 ** 模式上下文协议(MCP)服务器**,它使AI助手能够与 ClearML 实验,模型,和项目交互. 在你的AI对话中直接获得全面的ML实验背景和分析.

□ * * * * 特点

• 实验发现**:发现和分析跨项目的ML实验
• 绩效分析**:比较模型衡量尺度和培训进展
• 实时计量**: 获得培训分级、验证曲线和趋同分析
• 聪明的搜索**: 按名称、标签、状态和自定义查询过滤任务
• 人工活性管理**: 获取模型文件、数据集和实验输出
• 跨平台**: 与所有主要AI助理和代码编辑员合作

□ □ □ 要求

• uv(安装指南)用于uvx指令
• ** ClearML 账户**,在 [[T1] 有有效的 API 证书

# # # 快速启动 # # # # # # QQ Quick start

· 先决条件

您需要一个配置的 ClearML 环境, 并在 [[T2] 中显示您的证书 :

从 ClearML 设置中获取您的证书 。

• 安装

□□□□□□□□□□同入.

克劳德桌面

添加到您的 Claude 桌面配置 :

ClearML MCP Server

A lightweight Model Context Protocol (MCP) server that enables AI assistants to interact with ClearML experiments, models, and projects. Get comprehensive ML experiment context and analysis directly in your AI conversations.

✨ Features

• 🔍 Experiment Discovery: Find and analyze ML experiments across projects
• 📊 Performance Analysis: Compare model metrics and training progress
• 📈 Real-time Metrics: Access training scalars, validation curves, and convergence analysis
• 🏷️ Smart Search: Filter tasks by name, tags, status, and custom queries
• 📦 Artifact Management: Retrieve model files, datasets, and experiment outputs
• 🌐 Cross-platform: Works with all major AI assistants and code editors

📋 Requirements

• uv (installation guide) for uvx command
• ClearML account with valid API credentials in ~/.clearml/clearml.conf

🚀 Quick Start

Prerequisites

You need a configured ClearML environment with your credentials in ~/.clearml/clearml.conf:

[api]
api_server = https://api.clear.ml
web_server = https://app.clear.ml
files_server = https://files.clear.ml
credentials {
    "access_key": "your-access-key",
    "secret_key": "your-secret-key"
}

Get your credentials from ClearML Settings.

Installation

# Install from PyPI
pip install clearml-mcp

# Or run directly with uvx (no installation needed)
uvx clearml-mcp

🔌 Integrations