CODEWITHSUNDEEP
pythonpysparkmleap
Ollie
Ollie

Reputation: 664

mleap AttributeError: 'Pipeline' object has no attribute 'serializeToBundle'

I am having problems executing the example code from the mleap repository. I wish to run the code in a script instead of a jupyter notebook (which is how the example is run). My script is as follows:

##################################################################################
# start a local spark session
# https://spark.apache.org/docs/0.9.0/python-programming-guide.html
##################################################################################
from pyspark import SparkContext, SparkConf
conf = SparkConf()

#set app name
conf.set("spark.app.name", "train classifier")
#Run Spark locally with as many worker threads as logical cores on your machine (cores X threads).
conf.set("spark.master", "local[*]")
#number of cores to use for the driver process (only in cluster mode)
conf.set("spark.driver.cores", "1")
#Limit of total size of serialized results of all partitions for each Spark action (e.g. collect)
conf.set("spark.driver.maxResultSize", "1g")
#Amount of memory to use for the driver process
conf.set("spark.driver.memory", "1g")
#Amount of memory to use per executor process (e.g. 2g, 8g).
conf.set("spark.executor.memory", "2g")

#pass configuration to the spark context object along with code dependencies
sc = SparkContext(conf=conf)
from pyspark.sql.session import SparkSession
spark = SparkSession(sc)
##################################################################################


import mleap.pyspark

# # Imports MLeap serialization functionality for PySpark
from mleap.pyspark.spark_support import SimpleSparkSerializer

# Import standard PySpark Transformers and packages
from pyspark.ml.feature import VectorAssembler, StandardScaler, OneHotEncoder, StringIndexer
from pyspark.ml import Pipeline, PipelineModel
from pyspark.sql import Row

# Create a test data frame
l = [('Alice', 1), ('Bob', 2)]
rdd = sc.parallelize(l)
Person = Row('name', 'age')
person = rdd.map(lambda r: Person(*r))
df2 = spark.createDataFrame(person)
df2.collect()

# Build a very simple pipeline using two transformers
string_indexer = StringIndexer(inputCol='name', outputCol='name_string_index')

feature_assembler = VectorAssembler(
    inputCols=[string_indexer.getOutputCol()], outputCol="features")

feature_pipeline = [string_indexer, feature_assembler]

featurePipeline = Pipeline(stages=feature_pipeline)

featurePipeline.fit(df2)

featurePipeline.serializeToBundle("jar:file:/tmp/pyspark.example.zip")

On executing spark-submit script.py I get the following error:

17/09/18 13:26:43 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Traceback (most recent call last):
  File "/Users/opringle/Documents/Repos/finn/Magellan/src/no_spark_predict.py", line 58, in <module>
    featurePipeline.serializeToBundle("jar:file:/tmp/pyspark.example.zip")
AttributeError: 'Pipeline' object has no attribute 'serializeToBundle'

Any help would be much appreciated! I have installed mleap from pypy.

Upvotes: 0

Views: 2023

Answers (3)

cappaberra
cappaberra

Reputation: 16

See Here

It seems MLeap isn't ready for Spark 2.3 yet. If you happen to be running Spark 2.3, try downgrading to 2.2 and retry. Hopefully, that helps!

Upvotes: 0

Ollie
Ollie

Reputation: 664

I have solved the issue by attaching the following jar file when running:

spark-submit --packages ml.combust.mleap:mleap-spark_2.11:0.8.1  script.py

Upvotes: 0

MaFF
MaFF

Reputation: 10086

It seems you didn't follow the steps correctly, here http://mleap-docs.combust.ml/getting-started/py-spark.html it says that

Note: the import of mleap.pyspark needs to happen before any other PySpark libraries are imported.

Hence try importing your SparkContext after mleap

Upvotes: -1

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