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.artifacts
• mlflow.catboost
• mlflow.client
• mlflow.config
• mlflow.data
• mlflow.deployments
• mlflow.diviner
• mlflow.entities
• mlflow.environment_variables
• mlflow.fastai
• mlflow.gateway
• mlflow.gluon
• mlflow.h2o
• mlflow.johnsnowlabs
• mlflow.keras
• mlflow.langchain
• mlflow.lightgbm
• mlflow.llama_index
• mlflow.metrics
• mlflow.mleap
• mlflow.models
• mlflow.onnx
• mlflow.paddle
• mlflow.pmdarima
• mlflow.projects
• mlflow.promptflow
• mlflow.prophet
• mlflow.pyfunc
• mlflow.pyspark.ml
• mlflow.pytorch
• mlflow.recipes
• mlflow.sagemaker
• mlflow.sentence_transformers
• mlflow.server
• mlflow.shap
• mlflow.sklearn
• mlflow.spacy
• mlflow.spark
• mlflow.statsmodels
• mlflow.system_metrics
• mlflow.tensorflow
• mlflow.tracing
• mlflow.transformers
• mlflow.types
• mlflow.utils
• mlflow.xgboost
• mlflow.openai

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)