ctivated by installing the respective integration:Integration Materializer Handled Data Types Storage Format bentoml BentoMaterializer bentoml.Bento .bento deepchecks DeepchecksResultMateriailzer deepchecks.CheckResult , deepchecks.SuiteResult .json evidently EvidentlyProfileMaterializer evidently.Profile .json great_expectations GreatExpectationsMaterializer great_expectations.ExpectationSuite , great_expectations.CheckpointResult .json huggingface HFDatasetMaterializer datasets.Dataset , datasets.DatasetDict Directory huggingface HFPTModelMaterializer transformers.PreTrainedModel Directory huggingface HFTFModelMaterializer transformers.TFPreTrainedModel Directory huggingface HFTokenizerMaterializer transformers.PreTrainedTokenizerBase Directory lightgbm LightGBMBoosterMaterializer lgbm.Booster .txt lightgbm LightGBMDatasetMaterializer lgbm.Dataset .binary neural_prophet NeuralProphetMaterializer NeuralProphet .pt pillow PillowImageMaterializer Pillow.Image .PNG polars PolarsMaterializer pl.DataFrame , pl.Series .parquet pycaret PyCaretMaterializer Any sklearn , xgboost , lightgbm or catboost model .pkl pytorch PyTorchDataLoaderMaterializer torch.Dataset , torch.DataLoader .pt pytorch PyTorchModuleMaterializer torch.Module .pt scipy SparseMaterializer scipy.spmatrix .npz spark SparkDataFrameMaterializer pyspark.DataFrame .parquet spark SparkModelMaterializer pyspark.Transformer pyspark.Estimator tensorflow KerasMaterializer tf.keras.Model Directory tensorflow TensorflowDatasetMaterializer tf.Dataset Directory whylogs WhylogsMaterializer whylogs.DatasetProfileView .pb xgboost XgboostBoosterMaterializer xgb.Booster .json xgboost XgboostDMatrixMaterializer xgb.DMatrix .binary

If you are running pipelines with a Docker-based orchestrator, you need to specify the corresponding integration as required_integrations in the DockerSettings of your pipeline in order to have the integration materializer available inside your Docker container. See the pipeline configuration documentation for more information.