The xgboost model flavor enables logging of XGBoost models con MLflow format modo the mlflow

juin 1 2023
Lims2022

xgboost.save_model() and mlflow.xgboost.log_model() methods con python and mlflow_save_model and mlflow_log_model in R respectively. These methods also add the python_function flavor onesto the MLflow Models that they produce, allowing the models onesto be interpreted as generic Python functions for inference strada mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame incentivo. You can also use the mlflow.xgboost.load_model() method sicuro load MLflow Models with the xgboost model flavor sopra native XGBoost format.

LightGBM ( lightgbm )

The lightgbm model flavor enables logging of LightGBM models per MLflow format coraggio the mlflow.lightgbm.save_model() and mlflow.lightgbm.log_model() methods. These methods also add the python_function flavor to the MLflow Models that they produce, allowing the models esatto be interpreted as generic Python functions for inference cammino mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame molla. You can also use the mlflow.lightgbm.load_model() method onesto load MLflow Models with the lightgbm model flavor per native LightGBM format.

CatBoost ( catboost )

The catboost model flavor enables logging of CatBoost models in MLflow format via the mlflow.catboost.save_model() and mlflow.catboost.log_model() methods. These methods also add the python_function flavor onesto the MLflow Models that they produce, allowing the models puro be interpreted as generic Python functions for inference cammino mlflow.pyfunc.load_model() . You can also use the mlflow.catboost.load_model() method preciso load MLflow Models with the catboost model flavor per native CatBoost format.

Spacy( spaCy )

The spaCy model flavor enables logging of spaCy models sopra MLflow format modo the mlflow.spacy.save_model() and mlflow.spacy.log_model() methods https://datingranking.net/it/muslima-review/. Additionally, these methods add the python_function flavor onesto the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference cammino mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame incentivo. You can also use the mlflow.spacy.load_model() method preciso load MLflow Models with the spacy model flavor per native spaCy format.

Fastai( fastai )

The fastai model flavor enables logging of fastai Learner models in MLflow format modo the mlflow.fastai.save_model() and mlflow.fastai.log_model() methods. Additionally, these methods add the python_function flavor puro the MLflow Models that they produce, allowing the models preciso be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame input. You can also use the mlflow.fastai.load_model() method esatto load MLflow Models with the fastai model flavor durante native fastai format.

Statsmodels ( statsmodels )

The statsmodels model flavor enables logging of Statsmodels models mediante MLflow format coraggio the mlflow.statsmodels.save_model() and mlflow.statsmodels.log_model() methods. These methods also add the python_function flavor esatto the MLflow Models that they produce, allowing the models sicuro be interpreted as generic Python functions for inference cammino mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame input. You can also use the mlflow.statsmodels.load_model() method sicuro load MLflow Models with the statsmodels model flavor mediante native statsmodels format.

As for now, automatic logging is restricted sicuro parameters, metrics and models generated by verso call to fit on verso statsmodels model.

Prophet ( prophet )

The prophet model flavor enables logging of Prophet models in MLflow format coraggio the mlflow.prophet.save_model() and mlflow.prophet.log_model() methods. These methods also add the python_function flavor sicuro the MLflow Models that they produce, allowing the models esatto be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame input. You can also use the mlflow.prophet.load_model() method preciso load MLflow Models with the prophet model flavor in native prophet format.

Model Customization

While MLflow’s built-in model persistence utilities are convenient for packaging models from various popular ML libraries sopra MLflow Model format, they do not cover every use case. For example, you may want preciso use a model from an ML library that is not explicitly supported by MLflow’s built-durante flavors. Alternatively, you may want to package custom inference code and data puro create an MLflow Model. Fortunately, MLflow provides two solutions that can be used sicuro accomplish these tasks: Custom Python Models and Custom Flavors .