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tqchen 2015-03-21 23:12:55 -07:00
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multi-node/README.md
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@ -2,7 +2,7 @@ Distributed XGBoost
======
This folder contains information of Distributed XGBoost (Distributed GBDT).
* The distributed version is built on Rabit:[Reliable Allreduce and Broadcast Library](https://github.com/tqchen/rabit)
* The distributed version is built on Rabit:[Reliable Allreduce and Broadcast Library](https://github.com/dmlc/rabit)
- Rabit is a portable library that provides fault-tolerance for Allreduce calls for distributed machine learning
- This makes xgboost portable and fault-tolerant against node failures
* You can run Distributed XGBoost on platforms including Hadoop(see [hadoop folder](hadoop)) and MPI
@ -23,7 +23,7 @@ Notes
* The multi-threading nature of xgboost is inheritated in distributed mode
- This means xgboost efficiently use all the threads in one machine, and communicates only between machines
- Remember to run on xgboost process per machine and this will give you maximum speedup
* For more information about rabit and how it works, see the [Rabit's Tutorial](https://github.com/tqchen/rabit/tree/master/guide)
* For more information about rabit and how it works, see the [Rabit's Tutorial](https://github.com/dmlc/rabit/tree/master/guide)
Solvers
=====

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multi-node/hadoop/README.md
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Distributed XGBoost: Hadoop Yarn Version
====
* The script in this fold shows an example of how to run distributed xgboost on hadoop platform with YARN
* It relies on [Rabit Library](https://github.com/tqchen/rabit) (Reliable Allreduce and Broadcast Interface) and Yarn. Rabit provides an interface to aggregate gradient values and split statistics, that allow xgboost to run reliably on hadoop. You do not need to care how to update model in each iteration, just use the script ```rabit_yarn.py```. For those who want to know how it exactly works, plz refer to the main page of [Rabit](https://github.com/tqchen/rabit).
* It relies on [Rabit Library](https://github.com/dmlc/rabit) (Reliable Allreduce and Broadcast Interface) and Yarn. Rabit provides an interface to aggregate gradient values and split statistics, that allow xgboost to run reliably on hadoop. You do not need to care how to update model in each iteration, just use the script ```rabit_yarn.py```. For those who want to know how it exactly works, plz refer to the main page of [Rabit](https://github.com/dmlc/rabit).
* Quick start: run ```bash run_mushroom.sh <n_hadoop_workers> <n_thread_per_worker> <path_in_HDFS>```
- This is the hadoop version of binary classification example in the demo folder.
- More info of the usage of xgboost can be refered to [wiki page](https://github.com/tqchen/xgboost/wiki)
- More info of the usage of xgboost can be refered to [wiki page](https://github.com/dmlc/xgboost/wiki)
Before you run the script
====