mlflow.spark

The mlflow.spark module provides an API for logging and loading Spark MLlib models. This module exports Spark MLlib models with the following flavors:

Spark MLlib (native) format: Allows models to be loaded as Spark Transformers for scoring in a Spark session. Models with this flavor can be loaded as PySpark PipelineModel objects in Python. This is the main flavor and is always produced.
mlflow.pyfunc: Supports deployment outside of Spark by instantiating a SparkContext and reading input data as a Spark DataFrame prior to scoring. Also supports deployment in Spark as a Spark UDF. Models with this flavor can be loaded as Python functions for performing inference. This flavor is always produced.
mlflow.mleap: Enables high-performance deployment outside of Spark by leveraging MLeap’s custom dataframe and pipeline representations. Models with this flavor cannot be loaded back as Python objects. Rather, they must be deserialized in Java using the mlflow/java package. This flavor is produced only if you specify MLeap-compatible arguments.

mlflow.spark.autolog(disable=False, silent=False)[source]

Note

Autologging is known to be compatible with the following package versions: 3.1.2 <= pyspark <= 3.5.1. Autologging may not succeed when used with package versions outside of this range.

Enables (or disables) and configures logging of Spark datasource paths, versions (if applicable), and formats when they are read. This method is not threadsafe and assumes a SparkSession already exists with the mlflow-spark JAR attached. It should be called on the Spark driver, not on the executors (i.e. do not call this method within a function parallelized by Spark). This API requires Spark 3.0 or above.

Datasource information is cached in memory and logged to all subsequent MLflow runs, including the active MLflow run (if one exists when the data is read). Note that autologging of Spark ML (MLlib) models is not currently supported via this API. Datasource autologging is best-effort, meaning that if Spark is under heavy load or MLflow logging fails for any reason (e.g., if the MLflow server is unavailable), logging may be dropped.

For any unexpected issues with autologging, check Spark driver and executor logs in addition to stderr & stdout generated from your MLflow code - datasource information is pulled from Spark, so logs relevant to debugging may show up amongst the Spark logs.

Example

import mlflow.spark
import os
import shutil
from pyspark.sql import SparkSession

# Create and persist some dummy data
# Note: On environments like Databricks with pre-created SparkSessions,
# ensure the org.mlflow:mlflow-spark:2.22.0 is attached as a library to
# your cluster
spark = (
    SparkSession.builder.config("spark.jars.packages", "org.mlflow:mlflow-spark:2.22.0")
    .master("local[*]")
    .getOrCreate()
)
df = spark.createDataFrame(
    [(4, "spark i j k"), (5, "l m n"), (6, "spark hadoop spark"), (7, "apache hadoop")],
    ["id", "text"],
)
import tempfile

tempdir = tempfile.mkdtemp()
df.write.csv(os.path.join(tempdir, "my-data-path"), header=True)
# Enable Spark datasource autologging.
mlflow.spark.autolog()
loaded_df = spark.read.csv(
    os.path.join(tempdir, "my-data-path"), header=True, inferSchema=True
)
# Call toPandas() to trigger a read of the Spark datasource. Datasource info
# (path and format) is logged to the current active run, or the
# next-created MLflow run if no run is currently active
with mlflow.start_run() as active_run:
    pandas_df = loaded_df.toPandas()

Parameters

disable – If True, disables the Spark datasource autologging integration. If False, enables the Spark datasource autologging integration.
silent – If True, suppress all event logs and warnings from MLflow during Spark datasource autologging. If False, show all events and warnings during Spark datasource autologging.

mlflow.spark.get_default_conda_env(is_spark_connect_model=False)[source]

Returns: The default Conda environment for MLflow Models produced by calls to save_model() and log_model(). This Conda environment contains the current version of PySpark that is installed on the caller’s system. dev versions of PySpark are replaced with stable versions in the resulting Conda environment (e.g., if you are running PySpark version 2.4.5.dev0, invoking this method produces a Conda environment with a dependency on PySpark version 2.4.5).

mlflow.spark.get_default_pip_requirements(is_spark_connect_model=False)[source]

Returns: A list of default pip requirements for MLflow Models produced by this flavor. Calls to save_model() and log_model() produce a pip environment that, at minimum, contains these requirements.

mlflow.spark.load_model(model_uri, dfs_tmpdir=None, dst_path=None)[source]

Load the Spark MLlib model from the path.

Parameters

model_uri –
The location, in URI format, of the MLflow model, for example:
- /Users/me/path/to/local/model
- relative/path/to/local/model
- s3://my_bucket/path/to/model
- runs:/<mlflow_run_id>/run-relative/path/to/model
- models:/<model_name>/<model_version>
- models:/<model_name>/<stage>
For more information about supported URI schemes, see Referencing Artifacts.
dfs_tmpdir – Temporary directory path on Distributed (Hadoop) File System (DFS) or local filesystem if running in local mode. The model is loaded from this destination. Defaults to /tmp/mlflow.
dst_path – The local filesystem path to which to download the model artifact. This directory must already exist. If unspecified, a local output path will be created.

Returns

pyspark.ml.pipeline.PipelineModel

Example

from mlflow import spark

model = mlflow.spark.load_model("spark-model")
# Prepare test documents, which are unlabeled (id, text) tuples.
test = spark.createDataFrame(
    [(4, "spark i j k"), (5, "l m n"), (6, "spark hadoop spark"), (7, "apache hadoop")],
    ["id", "text"],
)
# Make predictions on test documents
prediction = model.transform(test)

mlflow.spark.log_model(spark_model, artifact_path, conda_env=None, code_paths=None, dfs_tmpdir=None, sample_input=None, 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, await_registration_for=300, pip_requirements=None, extra_pip_requirements=None, metadata=None)[source]

Log a Spark MLlib model as an MLflow artifact for the current run. This uses the MLlib persistence format and produces an MLflow Model with the Spark flavor.

Note: If no run is active, it will instantiate a run to obtain a run_id.

Parameters

spark_model – Spark model to be saved - MLflow can only save descendants of pyspark.ml.Model or pyspark.ml.Transformer which implement MLReadable and MLWritable.
artifact_path – Run relative artifact path.
conda_env –
Either a dictionary representation of a Conda environment or the path to a conda environment yaml file. If provided, this describes the environment this model should be run in. At a minimum, it should specify the dependencies contained in get_default_conda_env(). If None, a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements(). pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml. The following is an example dictionary representation of a conda environment:
```
{
    "name": "mlflow-env",
    "channels": ["conda-forge"],
    "dependencies": [
        "python=3.8.15",
        {
            "pip": [
                "pyspark==x.y.z"
            ],
        },
    ],
}
```
code_paths –
A list of local filesystem paths to Python file dependencies (or directories containing file dependencies). These files are prepended to the system path when the model is loaded. Files declared as dependencies for a given model should have relative imports declared from a common root path if multiple files are defined with import dependencies between them to avoid import errors when loading the model.

You can leave code_paths argument unset but set infer_code_paths to True to let MLflow infer the model code paths. See infer_code_paths argument doc for details.

For a detailed explanation of code_paths functionality, recommended usage patterns and limitations, see the code_paths usage guide.
dfs_tmpdir – Temporary directory path on Distributed (Hadoop) File System (DFS) or local filesystem if running in local mode. The model is written in this destination and then copied into the model’s artifact directory. This is necessary as Spark ML models read from and write to DFS if running on a cluster. If this operation completes successfully, all temporary files created on the DFS are removed. Defaults to /tmp/mlflow. For models defined in pyspark.ml.connect module, this param is ignored.
sample_input – A sample input used to add the MLeap flavor to the model. This must be a PySpark DataFrame that the model can evaluate. If sample_input is None, the MLeap flavor is not added.
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 –

A Model Signature object that describes the input and output Schema of the model. The model signature can be inferred using infer_signature function of mlflow.models.signature. Note if your Spark model contains Spark ML vector type input or output column, you should create SparkMLVector vector type for the column, infer_signature function can also infer SparkMLVector vector type correctly from Spark Dataframe input / output. When loading a Spark ML model with SparkMLVector vector type input as MLflow pyfunc model, it accepts Array[double] type input. MLflow internally converts the array into Spark ML vector and then invoke Spark model for inference. Similarly, if the model has vector type output, MLflow internally converts Spark ML vector output data into Array[double] type inference result.

Example:

from mlflow.models import infer_signature
from pyspark.sql.functions import col
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.functions import array_to_vector
import pandas as pd
import mlflow

train_df = spark.createDataFrame(
    [([3.0, 4.0], 0), ([5.0, 6.0], 1)], schema="features array<double>, label long"
).select(array_to_vector("features").alias("features"), col("label"))
lor = LogisticRegression(maxIter=2)
lor.setPredictionCol("").setProbabilityCol("prediction")
lor_model = lor.fit(train_df)

test_df = train_df.select("features")
prediction_df = lor_model.transform(train_df).select("prediction")

signature = infer_signature(test_df, prediction_df)

with mlflow.start_run() as run:
    model_info = mlflow.spark.log_model(
        lor_model,
        "model",
        signature=signature,
    )

# The following signature is outputed:
# inputs:
#   ['features': SparkML vector (required)]
# outputs:
#   ['prediction': SparkML vector (required)]
print(model_info.signature)

loaded = mlflow.pyfunc.load_model(model_info.model_uri)

test_dataset = pd.DataFrame({"features": [[1.0, 2.0]]})

# `loaded.predict` accepts `Array[double]` type input column,
# and generates `Array[double]` type output column.
print(loaded.predict(test_dataset))

input_example – one or several instances of valid model input. The input example is used as a hint of what data to feed the model. It will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format, or a numpy array where the example will be serialized to json by converting it to a list. Bytes are base64-encoded. When the signature parameter is None, the input example is used to infer a model signature.
await_registration_for – Number of seconds to wait for the model version to finish being created and is in READY status. By default, the function waits for five minutes. Specify 0 or None to skip waiting.
pip_requirements – Either an iterable of pip requirement strings (e.g. ["pyspark", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes the environment this model should be run in. If None, a default list of requirements is inferred by mlflow.models.infer_pip_requirements() from the current software environment. If the requirement inference fails, it falls back to using get_default_pip_requirements(). Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
extra_pip_requirements –
Either an iterable of pip requirement strings (e.g. ["pandas", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes additional pip requirements that are appended to a default set of pip requirements generated automatically based on the user’s current software environment. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
Warning

The following arguments can’t be specified at the same time:
- conda_env
- pip_requirements
- extra_pip_requirements
This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements.
metadata – Custom metadata dictionary passed to the model and stored in the MLmodel file.

Returns

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

Example

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

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"],
)
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)
mlflow.spark.log_model(model, "spark-model")

mlflow.spark.save_model(spark_model, path, mlflow_model=None, conda_env=None, code_paths=None, dfs_tmpdir=None, sample_input=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, pip_requirements=None, extra_pip_requirements=None, metadata=None)[source]

Save a Spark MLlib Model to a local path.

By default, this function saves models using the Spark MLlib persistence mechanism. Additionally, if a sample input is specified using the sample_input parameter, the model is also serialized in MLeap format and the MLeap flavor is added.

Parameters

spark_model – Spark model to be saved - MLflow can only save descendants of pyspark.ml.Model or pyspark.ml.Transformer which implement MLReadable and MLWritable.
path – Local path where the model is to be saved.
mlflow_model – MLflow model config this flavor is being added to.
conda_env –
Either a dictionary representation of a Conda environment or the path to a conda environment yaml file. If provided, this describes the environment this model should be run in. At a minimum, it should specify the dependencies contained in get_default_conda_env(). If None, a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements(). pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml. The following is an example dictionary representation of a conda environment:
```
{
    "name": "mlflow-env",
    "channels": ["conda-forge"],
    "dependencies": [
        "python=3.8.15",
        {
            "pip": [
                "pyspark==x.y.z"
            ],
        },
    ],
}
```
code_paths –
A list of local filesystem paths to Python file dependencies (or directories containing file dependencies). These files are prepended to the system path when the model is loaded. Files declared as dependencies for a given model should have relative imports declared from a common root path if multiple files are defined with import dependencies between them to avoid import errors when loading the model.

You can leave code_paths argument unset but set infer_code_paths to True to let MLflow infer the model code paths. See infer_code_paths argument doc for details.

For a detailed explanation of code_paths functionality, recommended usage patterns and limitations, see the code_paths usage guide.
dfs_tmpdir – Temporary directory path on Distributed (Hadoop) File System (DFS) or local filesystem if running in local mode. The model is be written in this destination and then copied to the requested local path. This is necessary as Spark ML models read from and write to DFS if running on a cluster. All temporary files created on the DFS are removed if this operation completes successfully. Defaults to /tmp/mlflow.
sample_input – A sample input that is used to add the MLeap flavor to the model. This must be a PySpark DataFrame that the model can evaluate. If sample_input is None, the MLeap flavor is not added.
signature – See the document of argument signature in mlflow.spark.log_model().
input_example – one or several instances of valid model input. The input example is used as a hint of what data to feed the model. It will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format, or a numpy array where the example will be serialized to json by converting it to a list. Bytes are base64-encoded. When the signature parameter is None, the input example is used to infer a model signature.
pip_requirements – Either an iterable of pip requirement strings (e.g. ["pyspark", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes the environment this model should be run in. If None, a default list of requirements is inferred by mlflow.models.infer_pip_requirements() from the current software environment. If the requirement inference fails, it falls back to using get_default_pip_requirements(). Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
extra_pip_requirements –
Either an iterable of pip requirement strings (e.g. ["pandas", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes additional pip requirements that are appended to a default set of pip requirements generated automatically based on the user’s current software environment. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
Warning

The following arguments can’t be specified at the same time:
- conda_env
- pip_requirements
- extra_pip_requirements
This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements.
metadata – Custom metadata dictionary passed to the model and stored in the MLmodel file.

Example

from mlflow import spark
from pyspark.ml.pipeline import PipelineModel

# your pyspark.ml.pipeline.PipelineModel type
model = ...
mlflow.spark.save_model(model, "spark-model")