MLflow
MLflow osFoundry community catalog में एक app है। Machine learning + LLM lifecycle के लिए open-source platform — experiments track करें (params, metrics, artifacts), registry में model versions log करें, serving endpoint पर deploy करें, हर LLM prompt + response token cost + latency के साथ log करें। ML + GenAI teams में standard tool; LLM tracing UI prompt observability के लिए Langfuse / Helicone के साथ रैंक करता है। SQLite backend default; artifacts /data/artifacts पर।
विवरण
- Workspace: osfoundry
- Category: AI
- मूल्य: Free
- Access: Community
Features
- 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
Documentation
Documentation को upstream project द्वारा अंग्रेज़ी में मेंटेन किया जाता है।
# 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.
osFoundry में MLflow का उपयोग कैसे करें
एक क्लिक में MLflow को अपने workspace में install करें, फिर अपने stack के लिए prompts, tools, या configuration को customise करने के लिए इसे osStudio में fork करें। आपके workspace का कोई भी सदस्य वहीं से आगे बढ़ा सकता है जहाँ आपने छोड़ा था।
Community से अन्य apps
- CRM — Contacts, deals, और pipeline tracking के साथ customer relationship management।
- Kanban Board — एक Trello-style kanban and project board के साथ cards, boards, calendar and table views, and per-board properties. Powered by Focalboard (standalone personal server). Embedded SQLite एक persistent volume पर.
- Helpdesk — SLA tracking के साथ ticket triage और customer support inbox।
- Page Builder — Visual drag-and-drop page builder के साथ sections, themes, SEO, and publishing
- Website Builder — Multi-page website builder के साथ CMS collections, global navigation, footer, themes, and publishing
- Storefront — Product catalog, cart, और checkout के साथ e-commerce storefront।