41 lines
1.1 KiB
Python
41 lines
1.1 KiB
Python
"""
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MLflow Integration Utilities for LLM Workflows
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This module provides MLflow integration for:
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- Kubeflow Pipelines experiment tracking
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- Model Registry with KServe deployment metadata
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- Inference metrics logging from NATS handlers
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- Experiment comparison and analysis
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Configuration:
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Set MLFLOW_TRACKING_URI environment variable or use defaults:
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- In-cluster: http://mlflow.mlflow.svc.cluster.local:80
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- External: https://mlflow.lab.daviestechlabs.io
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Usage:
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from mlflow_utils import get_mlflow_client, MLflowTracker
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from mlflow_utils.kfp_components import log_experiment_component
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from mlflow_utils.model_registry import register_model_for_kserve
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from mlflow_utils.inference_tracker import InferenceMetricsTracker
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"""
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from .client import (
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MLflowConfig,
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ensure_experiment,
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get_mlflow_client,
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get_tracking_uri,
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)
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from .inference_tracker import InferenceMetricsTracker
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from .tracker import MLflowTracker
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__all__ = [
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"get_mlflow_client",
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"get_tracking_uri",
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"ensure_experiment",
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"MLflowConfig",
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"MLflowTracker",
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"InferenceMetricsTracker",
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]
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__version__ = "1.0.0"
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