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Pre-requisites

If you have a cluster already deployed you can skip this section. To deploy a 3 node chameleon cluster, use the following:

cm reset
pip uninstall cloudmesh_client
pip install -U cloudmesh_client
cm key add --ssh
cm refresh on
cm cluster define --count 3 \
    --image CC-Ubuntu14.04 --flavor m1.medium
cm hadoop define spark pig
cm hadoop sync
cm hadoop deploy
cm cluster cross_ssh

clone or download repository

git clone https://github.com/cloudmesh/cloudmesh.word2vec.git

update hosts file with ip of your primary node on your cluster

(Note: On the cluster, first node becomes master)

cd ansible-word2vec
vi hosts

execute script to deploy and run spark job

cd ansible-word2vec
./run.sh

run script will:

  • deploy code
  • run the crawler for collecting the training set
  • submit the job on spark

Execution log

Test results

single node

multinode node Chameleon

multinode node Jetstream

Cleanup

To cleanup installation from cluster

cd ansible-word2vec
ansible-playbook word2vec_cleanup.yaml

Appendix:

Troubleshooting

  1. If the installation run.sh script fails in middle due to some reason, execute the cleanup script before re-triggering run script again. The run script may fail due to variety of reasons like failed to shh, hadoop not available etc

  2. If the run script fails due to spark memory errors, you can modify the spark memory setting in ansible-word2vec/setupvariables.yml execute ansible-word2vec/word2vec_cleanup.yaml and ansible-word2vec/run.sh script.

  3. To test intermediate code changes, you can push the code to a git feature branch for example spark_test. Modify word2vec_setup.yaml git section version=master to point to spark_test branch and execute the ansible-word2vec/run.sh script.

  4. If hadoop goes into safe mode, goto the cluster namenode and execute

    /opt/hadoop$ bin/hadoop dfsadmin -safemode leave

This will remove the cluster from safe mode.

Manual steps for running the crawler and spark job

Setup Wiki Crawler
  • Configure various properties in config.properties. For crawler, the important parameters are A)data_location B)seed_list C)max_pages
  • configure the seed pages in wiki_crawl_seedlist.csv
  • create folder 'crawldb' under 'code' (or the location specified in the config file)
  • execute crawler
    python wikicrawl.py
Setup News Crawler
  • configure various properties in config.properties. For news crawler, the important parameters are A)data_location B)news_seed_list C)Google custom search API keys
  • configure the seed pages in news_crawl_seedlist.csv
  • get Google customer search API keys by following instructions in https://developers.google.com/custom-search/
  • create folder 'crawldb' under 'code' (or the location specified in config file)
  • execute crawler
    python newscrawl.py

Create Word2Vec Model

  • create folder 'model' under 'code' for saving the model
  • configure various properties in config.properties
  • start your spark cluster
  • configure the spark-master URL in create-word2vec-model.sh.
  • execute the create-word2vec-model
    bash create-word2vec-model.sh

Query Word2Vec Model

  • execute use_word2vec_model.py
  • the test file location can be defined in config.properties (synonym_test_file). The default value is stest.csv
  • The generated result file will be stored in stestresult.csv by default. It can be changed using config.properties
    bash use_word2vec_model.sh

Find Relations using Word2Vec Model

  • execute find_relations.py
  • the test file location can be defined in config.properties (relations_test_file). The default value is relationstest.csv
  • The generated result file will be stored in relationsresult.csv by default. It can be changed using config.properties
    bash find_relations.sh

Manual cleanup of deployment on cluster

login(ssh) to cluster and run the following

cd /opt
sudo unlink spark
sudo rm -rf spark-2.1.0-bin-hadoop2.6*
sudo ln -s /opt/spark-1.6.0-bin-hadoop2.6 spark
sudo rm -rf /tmp/word2vec*
sudo rm -rf /opt/word2vec*