MLflow
MLflow adalah aplikasi di katalog komunitas osFoundry. Platform open source untuk siklus hidup machine learning dan LLM — lacak eksperimen (parameter, metrik, artefak), catat versi model di registry, deploy ke endpoint serving, dan catat setiap prompt + respons LLM dengan biaya token dan latensi. Alat standar di tim ML dan GenAI; UI tracing LLM-nya sejajar dengan Langfuse / Helicone untuk observabilitas prompt. Backend SQLite default; artefak ke /data/artifacts.
Detail
- Workspace: osfoundry
- Kategori: AI
- Harga: Free
- Akses: Community
Fitur
- 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
Dokumentasi
Dokumentasi dipelihara dalam bahasa Inggris oleh proyek upstream.
# 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.
Cara menggunakan MLflow di osFoundry
Instal MLflow ke workspace Anda dalam satu klik, lalu fork di osStudio untuk menyesuaikan prompt, tool, atau konfigurasi sesuai stack Anda. Siapa pun di workspace Anda dapat melanjutkan dari titik Anda berhenti.
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