ImportError: No module named numpy on spark workers

Question

Launching pyspark in client mode. bin/pyspark --master yarn-client --num-executors 60 The import numpy on the shell goes fine but it fails in the kmeans. Somehow the executors do not have numpy installed is my feeling. I didnt find any good solution anywhere to let workers know about numpy. I tried setting PYSPARK_PYTHON but that didnt work either.

import numpy
features = numpy.load(open("combined_features.npz"))
features = features['arr_0']
features.shape
features_rdd = sc.parallelize(features, 5000)
from pyspark.mllib.clustering import KMeans, KMeansModel

from numpy import array
from math import sqrt
clusters = KMeans.train(features_rdd, 2, maxIterations=10, runs=10, initializationMode="random")

Stack trace

 org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/hadoop/3/scratch/local/usercache/ajkale/appcache/application_1451301880705_525011/container_1451301880705_525011_01_000011/pyspark.zip/pyspark/worker.py", line 98, in main
    command = pickleSer._read_with_length(infile)
  File "/hadoop/3/scratch/local/usercache/ajkale/appcache/application_1451301880705_525011/container_1451301880705_525011_01_000011/pyspark.zip/pyspark/serializers.py", line 164, in _read_with_length
    return self.loads(obj)
  File "/hadoop/3/scratch/local/usercache/ajkale/appcache/application_1451301880705_525011/container_1451301880705_525011_01_000011/pyspark.zip/pyspark/serializers.py", line 422, in loads
    return pickle.loads(obj)
  File "/hadoop/3/scratch/local/usercache/ajkale/appcache/application_1451301880705_525011/container_1451301880705_525011_01_000011/pyspark.zip/pyspark/mllib/__init__.py", line 25, in <module>

ImportError: No module named numpy

        at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:166)
        at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:207)
        at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:125)
        at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:70)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
        at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
        at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:262)
        at org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:99)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
        at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
        at org.apache.spark.scheduler.Task.run(Task.scala:88)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
        at java.lang.Thread.run(Thread.java:745)
        enter code here

Answer 1

To use Spark in Yarn client mode, you'll need to install any dependencies to the machines on which Yarn starts the executors. That's the only surefire way to make this work.

Using Spark with Yarn cluster mode is a different story. You can distribute python dependencies with spark-submit.

spark-submit --master yarn-cluster my_script.py --py-files my_dependency.zip

However, the situation with numpy is complicated by the same thing that makes it so fast: the fact that does the heavy lifting in C. Because of the way that it is installed, you won't be able to distribute numpy in this fashion.