wehub-resource-sync
50 lines
1.8 KiB
Markdown
50 lines
1.8 KiB
Markdown
### 1. Install MLflow
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Detect this project's Python package manager and add `mlflow` as a dependency
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if it is not already declared:
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- `uv` (look for `uv.lock` or `[tool.uv]` in `pyproject.toml`) -> `uv add mlflow`
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- `poetry` (look for `poetry.lock`) -> `poetry add mlflow`
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- `pip` / plain `requirements.txt` -> append `mlflow` and `pip install mlflow`
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Skip this step if `mlflow` is already a declared dependency.
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{{ server_setup }}### 2. Configure tracking URI
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Configure MLflow to log to `{{ tracking_uri }}`. Pick whichever of these fits the
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project's conventions:
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- Set `MLFLOW_TRACKING_URI={{ tracking_uri }}` in the project's env file (`.env`,
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`.env.example`, etc.).
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- Call `mlflow.set_tracking_uri("{{ tracking_uri }}")` once during application
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startup, before any `mlflow.*` calls.
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Don't do both. If the project already sets a tracking URI, leave it alone and
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note the existing value in the final summary.
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### 3. Instrument with `mlflow.autolog`
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Consult the `instrumenting-with-mlflow-tracing` skill in `{{ skills_dir }}/` for
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the supported libraries and per-integration setup. That skill is the source
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of truth for what `mlflow.autolog()` covers.
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For most applications, `mlflow.autolog()` is the recommended entry point:
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```python
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import mlflow
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mlflow.set_tracking_uri("{{ tracking_uri }}")
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mlflow.autolog()
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```
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Wire this into the application's entry point(s):
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- Find the main entry (e.g. `main.py`, `app.py`, `__main__.py`, FastAPI
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lifespan / `Depends`, Django app config `ready` hook, Lambda handler init).
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- Call `mlflow.autolog()` once, before any LLM clients are created.
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- Do not add it to library modules or tests.
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For library-specific instrumentation (LangChain, LangGraph, OpenAI, Anthropic,
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LlamaIndex, DSPy, etc.), many libraries have a dedicated
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`mlflow.<library>.autolog()` flavor. The skill above lists them.
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