mlflow.mleap

The mlflow.mleap module provides an API for saving Spark MLLib models using the MLeap persistence mechanism.

exception mlflow.mleap.MLeapSerializationException(message, error_code=1, **kwargs)[source]

Bases: mlflow.exceptions.MlflowException

Exception thrown when a model or DataFrame cannot be serialized in MLeap format.

mlflow.mleap.add_to_model(mlflow_model, path, spark_model, sample_input)[source]

Warning

mlflow.mleap.add_to_model is deprecated since 2.6.0. This method will be removed in a future release. Use mlflow.onnx instead.

Note

This method requires all argument be specified by keyword.

Add the MLeap flavor to an existing MLflow model.

Parameters

mlflow_model – mlflow.models.Model to which this flavor is being added.
path – Path of the model to which this flavor is being added.
spark_model – Spark PipelineModel to be saved. This model must be MLeap-compatible and cannot contain any custom transformers.
sample_input – Sample PySpark DataFrame input that the model can evaluate. This is required by MLeap for data schema inference.

mlflow.mleap.log_model(spark_model, sample_input, artifact_path, registered_model_name=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes, tuple] = None, metadata=None)[source]

Warning

mlflow.mleap.log_model is deprecated since 2.6.0. This method will be removed in a future release. Use mlflow.onnx instead.

Note

This method requires all argument be specified by keyword.

Log a Spark MLLib model in MLeap format as an MLflow artifact for the current run. The logged model will have the MLeap flavor.

Note

You cannot load the MLeap model flavor in Python; you must download it using the Java API method downloadArtifacts(String runId) and load the model using the method MLeapLoader.loadPipeline(String modelRootPath).

Parameters

spark_model – Spark PipelineModel to be saved. This model must be MLeap-compatible and cannot contain any custom transformers.
sample_input – Sample PySpark DataFrame input that the model can evaluate. This is required by MLeap for data schema inference.
artifact_path – Run-relative artifact path.
registered_model_name – If given, create a model version under registered_model_name, also creating a registered model if one with the given name does not exist.
signature –
ModelSignature describes model input and output Schema. The model signature can be inferred from datasets with valid model input (e.g. the training dataset with target column omitted) and valid model output (e.g. model predictions generated on the training dataset), for example:
```
from mlflow.models import infer_signature

train = df.drop_column("target_label")
predictions = ...  # compute model predictions
signature = infer_signature(train, predictions)
```
input_example – {{ input_example }}
metadata – {{ metadata }}

Returns

A ModelInfo instance that contains the metadata of the logged model.

Example

import mlflow
import mlflow.mleap
import pyspark
from pyspark.ml import Pipeline
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.feature import HashingTF, Tokenizer

# training DataFrame
training = spark.createDataFrame(
    [
        (0, "a b c d e spark", 1.0),
        (1, "b d", 0.0),
        (2, "spark f g h", 1.0),
        (3, "hadoop mapreduce", 0.0),
    ],
    ["id", "text", "label"],
)
# testing DataFrame
test_df = spark.createDataFrame(
    [(4, "spark i j k"), (5, "l m n"), (6, "spark hadoop spark"), (7, "apache hadoop")],
    ["id", "text"],
)
# Create an MLlib pipeline
tokenizer = Tokenizer(inputCol="text", outputCol="words")
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
lr = LogisticRegression(maxIter=10, regParam=0.001)
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
model = pipeline.fit(training)
# log parameters
mlflow.log_param("max_iter", 10)
mlflow.log_param("reg_param", 0.001)
# log the Spark MLlib model in MLeap format
mlflow.mleap.log_model(
    spark_model=model, sample_input=test_df, artifact_path="mleap-model"
)

mlflow.mleap.save_model(spark_model, sample_input, path, mlflow_model=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes, tuple] = None, metadata=None)[source]

Warning

mlflow.mleap.save_model is deprecated since 2.6.0. This method will be removed in a future release. Use mlflow.onnx instead.

Note

This method requires all argument be specified by keyword.

Save a Spark MLlib PipelineModel in MLeap format at a local path. The saved model will have the MLeap flavor.

Note

You cannot load the MLeap model flavor in Python; you must download it using the Java API method downloadArtifacts(String runId) and load the model using the method MLeapLoader.loadPipeline(String modelRootPath).

Parameters

spark_model – Spark PipelineModel to be saved. This model must be MLeap-compatible and cannot contain any custom transformers.
sample_input – Sample PySpark DataFrame input that the model can evaluate. This is required by MLeap for data schema inference.
path – Local path where the model is to be saved.
mlflow_model – mlflow.models.Model to which this flavor is being added.
signature –
ModelSignature describes model input and output Schema. The model signature can be inferred from datasets with valid model input (e.g. the training dataset with target column omitted) and valid model output (e.g. model predictions generated on the training dataset), for example:
```
from mlflow.models import infer_signature

train = df.drop_column("target_label")
predictions = ...  # compute model predictions
signature = infer_signature(train, predictions)
```
input_example – {{ input_example }}
metadata – {{ metadata }}