Distributed XGBoost: Hadoop Version
- Hadoop version: run
bash run_binary_classification.sh <n_hadoop_workers> <n_thread_per_worker> <path_in_HDFS>- This is the hadoop version of binary classification example in the demo folder.
How to Use
- Check whether environment variable $HADOOP_HOME exists (e.g. run
echo $HADOOP_HOME). If not, plz set up hadoop-streaming.jar path in rabit_hadoop.py.
Notes
- The code has been tested on MapReduce 1 (MRv1) and YARN, it recommended run on MapReduce 2 (MRv2, YARN).
- The code is multi-threaded, so you want to run one xgboost per node/worker, which means you want to set <n_thread_per_worker> to be number of cores you have on each machine.
- You will need YARN to set specify number of cores of each worker
- The hadoop version save the final model into HDFS