MLflow
MLflow 是 osFoundry 社区目录中的应用。面向机器学习与 LLM 全生命周期的开源平台——跟踪实验（参数、指标、产物），将模型版本注册到模型仓库，部署到推理端点，并记录每一次 LLM 提示词与响应连同 token 成本和延迟。是机器学习与生成式 AI 团队的标准工具；其 LLM 追踪 UI 在提示词可观测性方面可与 Langfuse / Helicone 并列。默认 SQLite 后端，产物写入 /data/artifacts。
详情
- 工作区: osfoundry
- 分类: AI
- 价格: Free
- 访问权限: Community
功能
- Experiment tracking — params, metrics, artifacts versioned per run, full diff/compare UI
- LLM tracing — auto-capture prompts + responses + token cost from LangChain / LlamaIndex / OpenAI / Anthropic
- Model registry with versions + stages (Staging / Production / Archived) + transition history
- SQLite backend bundled — zero-config self-hosting for solo + small team use
- Standard Python / R / Java / REST APIs — works from any ML or LLM framework
- 20 GB volume for artifacts (models, plots, datasets) — expandable
文档
文档由上游项目以英文维护。
# MLflow
## Track your first run
Set your client to point at the public URL:
```python
import mlflow
mlflow.set_tracking_uri('https://<your-public-url>')
mlflow.set_experiment('my-first-experiment')
with mlflow.start_run():
mlflow.log_param('learning_rate', 0.01)
mlflow.log_metric('accuracy', 0.92)
mlflow.log_artifact('model.pkl')
```
Open the web UI — the run appears under 'my-first-experiment'.
## LLM tracing
The Tracing UI (added in MLflow 2.14+) auto-captures every prompt + response + tool call + token count from LangChain, LlamaIndex, OpenAI SDK, Anthropic SDK, and DSPy:
```python
import mlflow
mlflow.openai.autolog() # or langchain.autolog() / llama_index.autolog() / ...
```
Every call shows up in the Trace tab with the full request/response, token cost, latency, errors. The 'Compare' view lets you diff prompt variants side-by-side.
## Model registry
**Models → Register Model** from any run that logged a model. Versioned, with stages (Staging / Production / Archived) and transition workflows.
## Serving
The registered models can be served via `mlflow models serve -m models:/my-model/Production` from your own infra. The tracking server itself doesn't serve inference — it's the catalog.
## Storage
SQLite at `/data/mlflow.db` for metadata; artifacts at `/data/artifacts/`. 20 GB volume. For team-scale use, switch the backend store to Postgres via `MLFLOW_BACKEND_STORE_URI` env.
如何在 osFoundry 中使用 MLflow
一键将 MLflow 安装到您的工作区，然后在 osStudio 中将其分支，针对您的技术栈自定义提示词、工具或配置。工作区中的任何人都可以接续您的工作继续推进。
社区中的其他应用
- 客户关系管理 — 客户关系管理工具，支持联系人、交易和销售管道跟踪。
- Kanban Board — Trello 风格的看板与项目板，含卡片、面板、日历与表格视图以及每面板属性。基于 Focalboard（独立个人服务器）构建。在持久卷上内嵌 SQLite。
- 服务台 — 工单分流与客户支持收件箱，附带 SLA 跟踪。
- Page Builder — 可视化拖放页面生成器，支持区块、主题、SEO 与发布
- Website Builder — 带 CMS 合集、全局导航、页脚、主题与发布的多页面网站生成器
- 店面 — 电商店面，包含商品目录、购物车和结账流程。