Spark-knn-recommender is a fast, scalable recommendation engine built on top of PySpark, the Python API for Apache Spark. It can be deployed locally or on Amazon EMR.
- Python >= 2.7.3
- Spark >= 0.7.0
- Numpy
First, clone this repo onto your local machine:
$ git clone https://github.com/evancasey/spark-knn-recommender.gitSet your spark cluster configuration in config.py:
# change if you are running on Amazon EMR
CLUSTER_CONFIG = "local"
# fill this in with pySpark path
PYSPARK_HOME = "../spark/pyspark"
SPARKLER_HOME = "../spark/python/sparkler"In the root directory, run:
$ python setup.pyRun:
$ python train_and_test.py- create an Amazan Web Services Account
- # for Elastic MapReduce
- install the Amazon EMR CLI
Run the Spark/Shark bootstrap script:
$ ./elastic-mapreduce --create --alive --name "Spark/Shark Cluster" --bootstrap-action s3://elasticmapreduce/samples/spark/0.8.1/install-spark-shark.sh --bootstrap-name "Spark/Shark" --instance-type m1.xlarge --instance-count 3 --jobflow-role spark
Created job flow j-2Y0VECUPLFW94SSH into the master node of your cluster (replace the job ID below with your job ID):
./elastic-mapreduce -j j-2Y0VECUPLFW94 --sshCopyright (c) 2011-2016
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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