Python API
The MLflow Python API is organized into the following modules. The most common functions are
exposed in the mlflow module, so we recommend starting there.
- mlflow
- MLflow Tracing APIs
- MLflow Logged Model APIs
- mlflow.anthropic
- mlflow.artifacts
- mlflow.autogen
- mlflow.bedrock
- mlflow.catboost
- mlflow.client
- mlflow.config
- mlflow.crewai
- mlflow.data
- mlflow.deployments
- mlflow.diviner
- mlflow.dspy
- mlflow.entities
- mlflow.environment_variables
- mlflow.gateway
- mlflow.gemini
- mlflow.groq
- mlflow.h2o
- mlflow.johnsnowlabs
- mlflow.keras
- mlflow.langchain
- mlflow.lightgbm
- mlflow.litellm
- mlflow.llama_index
- mlflow.metrics
- mlflow.mistral
- mlflow.models
- mlflow.onnx
- mlflow.openai
- mlflow.paddle
- mlflow.pmdarima
- mlflow.projects
- mlflow.promptflow
- mlflow.prophet
- mlflow.pydantic_ai
- mlflow.pyfunc
- mlflow.pyspark.ml
- mlflow.pytorch
- mlflow.sagemaker
- mlflow.sentence_transformers
- mlflow.server
- mlflow.shap
- mlflow.sklearn
- mlflow.smolagents
- mlflow.spacy
- mlflow.spark
- mlflow.statsmodels
- mlflow.system_metrics
- mlflow.tensorflow
- mlflow.tracing
- mlflow.transformers
- mlflow.types
- mlflow.utils
- mlflow.xgboost
See also the index of all functions and classes.
Log Levels
MLflow Python APIs log information during execution using the Python Logging API. You can configure the log level for MLflow logs using the following code snippet. Learn more about Python log levels at the Python language logging guide.
import logging
logger = logging.getLogger("mlflow")
# Set log level to debugging
logger.setLevel(logging.DEBUG)