make wrapper ok

Makefile

@@ -32,8 +32,8 @@ sync_tcp.o: src/sync/sync_tcp.cpp
 sync_empty.o: src/sync/sync_empty.cpp 
 main.o: src/xgboost_main.cpp src/utils/*.h src/*.h src/learner/*.hpp src/learner/*.h 
 xgboost-mpi:  updater.o gbm.o io.o main.o sync_mpi.o 
-xgboost:  updater.o gbm.o io.o main.o sync_empty.o
+xgboost:  updater.o gbm.o io.o main.o sync_tcp.o
-wrapper/libxgboostwrapper.so: wrapper/xgboost_wrapper.cpp src/utils/*.h src/*.h src/learner/*.hpp src/learner/*.h  updater.o gbm.o io.o sync_empty.o
+wrapper/libxgboostwrapper.so: wrapper/xgboost_wrapper.cpp src/utils/*.h src/*.h src/learner/*.hpp src/learner/*.h  updater.o gbm.o io.o sync_tcp.o
 
 $(BIN) : 
 	$(CXX) $(CFLAGS) $(LDFLAGS) -o $@ $(filter %.cpp %.o %.c, $^)

demo/binary_classification/runexp.sh

@@ -4,12 +4,12 @@ python mapfeat.py
 # split train and test
 python mknfold.py agaricus.txt 1
 # training and output the models
-mpirun ../../xgboost mushroom.conf
+../../xgboost mushroom.conf
 # output prediction task=pred 
-mpirun ../../xgboost mushroom.conf task=pred model_in=0002.model
+../../xgboost mushroom.conf task=pred model_in=0002.model
 # print the boosters of 00002.model in dump.raw.txt
-mpirun ../../xgboost mushroom.conf task=dump model_in=0002.model name_dump=dump.raw.txt 
+../../xgboost mushroom.conf task=dump model_in=0002.model name_dump=dump.raw.txt 
 # use the feature map in printing for better visualization
-mpirun ../../xgboost mushroom.conf task=dump model_in=0002.model fmap=featmap.txt name_dump=dump.nice.txt
+../../xgboost mushroom.conf task=dump model_in=0002.model fmap=featmap.txt name_dump=dump.nice.txt
 cat dump.nice.txt
 

demo/guide-python/runall.sh

@@ -4,5 +4,5 @@ python custom_objective.py
 python boost_from_prediction.py
 python generalized_linear_model.py
 python cross_validation.py
-python predict_leaf_index.py
+python predict_leaf_indices.py
 rm -rf *~ *.model *.buffer 

multi-node/README.md

@@ -4,17 +4,21 @@ This folder contains information about experimental version of distributed xgboo
 
 Build
 =====
-* You will need to have MPI
 * In the root folder, run ```make mpi```, this will give you xgboost-mpi
+  - You will need to have MPI to build xgboost-mpi
+* Alternatively, you can run ```make```, this will give you xgboost, which uses a beta buildin allreduce
+  - You do not need MPI to build this, you can modify [submit_job_tcp.py](submit_job_tcp.py) to use any job scheduler you like to submit the job
 
 Design Choice
 =====
-* Does distributed xgboost reply on MPI?
+* Does distributed xgboost must reply on MPI library?
-  - Yes, but the dependency is isolated in [sync module](../src/sync/sync.h)
+  - No, XGBoost replies on MPI protocol that provide Broadcast and AllReduce,
+  - The dependency is isolated in [sync module](../src/sync/sync.h)
   - All other parts of code uses interface defined in sync.h
-  - sync_mpi.cpp is a implementation of sync interface using standard MPI library
+  - [sync_mpi.cpp](../src/sync/sync_mpi.cpp) is a implementation of sync interface using standard MPI library, to use xgboost-mpi, you need an MPI library
-  - Specificially, xgboost reply on MPI protocol that provide Broadcast and AllReduce,
+  - If there are platform/framework that implements these protocol, xgboost should naturally extends to these platform
-     if there are platform/framework that implements these protocol, xgboost should naturally extends to these platform
+  - As an example, [sync_tcp.cpp](../src/sync/sync_tcp.cpp) is an implementation of interface using TCP, and is linked with xgboost by default
+
 * How is the data distributed?
   - There are two solvers in distributed xgboost
   - Column-based solver split data by column, each node work on subset of columns, 
@@ -26,10 +30,11 @@ Design Choice
 
 Usage
 ====
-* The current code run in MPI enviroment, you will need to have a network filesystem,
+* You will need a network filesystem, or copy data to local file system before running the code
-    or copy data to local file system before running the code
+* xgboost-mpi run in MPI enviroment, 
+* xgboost can be used together with [submit_job_tcp.py](submit_job_tcp.py) on other types of job schedulers
 * ***Note*** The distributed version is still multi-threading optimized.
-    You should run one xgboost-mpi per node that takes most available CPU,
+    You should run one process per node that takes most available CPU,
     this will reduce the communication overhead and improve the performance.
    - One way to do that is limit mpi slot in each machine to be 1, or reserve nthread processors for each process.
 * Examples:

multi-node/col-split/README.md

@@ -1,6 +1,11 @@
 Distributed XGBoost: Column Split Version
 ====
-* run ```bash mushroom-row.sh <n-mpi-process>```
+* run ```bash mushroom-col.sh <n-mpi-process>```
+* run ```bash mushroom-col-tcp.sh <n-process>```
+  - mushroom-col-tcp.sh starts xgboost job using xgboost's buildin allreduce 
+* run ```bash mushroom-col-python.sh <n-process>```
+  - mushroom-col-python.sh starts xgboost python job using xgboost's buildin all reduce
+  - see mushroom-col.py
 
 How to Use
 ====

multi-node/col-split/mushroom-col-python.sh

@@ -0,0 +1,22 @@
+#!/bin/bash
+if [[ $# -ne 1 ]]
+then
+    echo "Usage: nprocess"
+    exit -1
+fi
+
+#
+# This script is same as mushroom-col except that we will be using xgboost python module
+# 
+# xgboost used built in tcp-based allreduce module, and can be run on more enviroment, so long as we know how to start job by modifying ../submit_job_tcp.py
+#
+rm -rf train.col* *.model
+k=$1
+
+# split the lib svm file into k subfiles
+python splitsvm.py ../../demo/data/agaricus.txt.train train $k
+
+# run xgboost mpi
+../submit_job_tcp.py $k python mushroom-col.py
+
+cat dump.nice.$k.txt

multi-node/col-split/mushroom-col.py

@@ -0,0 +1,29 @@
+import os
+import sys
+sys.path.append(os.path.dirname(__file__)+'/../wrapper')
+import xgboost as xgb
+# this is example script of running distributed xgboost using python
+
+# call this additional function to intialize the xgboost sync module
+# in distributed mode
+xgb.sync_init(sys.argv)
+rank = xgb.sync_get_rank()
+# read in dataset
+dtrain = xgb.DMatrix('train.col%d' % rank)
+param = {'max_depth':3, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
+param['dsplit'] = 'col'
+nround = 3
+
+if rank == 0:
+    dtest = xgb.DMatrix('../../demo/data/agaricus.txt.test')
+    model = xgb.train(param, dtrain, nround, [(dtrain, 'train') , (dtest, 'test')])
+else:
+    # if it is a slave node, do not run evaluation
+    model = xgb.train(param, dtrain, nround)
+
+if rank == 0:
+    model.save_model('%04d.model' % nround)
+    # dump model with feature map
+    model.dump_model('dump.nice.%d.txt' % xgb.sync_get_world_size(),'../../demo/data/featmap.txt')
+# shutdown the synchronization module
+xgb.sync_finalize()

multi-node/submit_job_tcp.py

@@ -11,6 +11,10 @@ import subprocess
 sys.path.append(os.path.dirname(__file__)+'/../src/sync/')
 import tcp_master as master
 
+#
+#  Note: this submit script is only used for example purpose
+#  It does not have to be mpirun, it can be any job submission script that starts the job, qsub, hadoop streaming etc.
+#  
 def mpi_submit(nslave, args):
     """
       customized submit script, that submit nslave jobs, each must contain args as parameter

src/io/io.cpp

@@ -13,6 +13,11 @@
 namespace xgboost {
 namespace io {
 DataMatrix* LoadDataMatrix(const char *fname, bool silent, bool savebuffer) {
+  if (!strcmp(fname, "stdin")) {
+    DMatrixSimple *dmat = new DMatrixSimple();
+    dmat->LoadText(fname, silent);
+    return dmat;
+  }
   std::string tmp_fname;
   const char *fname_ext = NULL;
   if (strchr(fname, ';') != NULL) {

src/io/simple_dmatrix-inl.hpp

@@ -84,7 +84,12 @@ class DMatrixSimple : public DataMatrix {
   inline void LoadText(const char* fname, bool silent = false) {
     using namespace std;
     this->Clear();
-    FILE* file = utils::FopenCheck(fname, "r");
+    FILE* file;
+    if (!strcmp(fname, "stdin")) {
+      file = stdin;
+    } else {
+      file = utils::FopenCheck(fname, "r");      
+    }
     float label; bool init = true;
     char tmp[1024];
     std::vector<RowBatch::Entry> feats;
@@ -112,7 +117,9 @@ class DMatrixSimple : public DataMatrix {
                     static_cast<unsigned long>(info.num_col()),
                     static_cast<unsigned long>(row_data_.size()), fname);
     }
-    fclose(file);
+    if (file != stdin) {
+      fclose(file);
+    }
     // try to load in additional file
     std::string name = fname;
     std::string gname = name + ".group";

src/sync/sync_tcp.cpp

@@ -352,7 +352,7 @@ class SyncManager {
       buffer_.resize(std::min(reduce_buffer_size, n));
       // make sure align to type_nbytes
       buffer_size = buffer_.size() * sizeof(uint64_t) / type_nbytes * type_nbytes;
-      utils::Assert(type_nbytes < buffer_size, "too large type_nbytes=%lu, buffer_size", type_nbytes, buffer_size);
+      utils::Assert(type_nbytes <= buffer_size, "too large type_nbytes=%lu, buffer_size=%lu", type_nbytes, buffer_size);
       // set buffer head
       buffer_head = reinterpret_cast<char*>(BeginPtr(buffer_));
     }
@@ -487,6 +487,8 @@ void AllReduce<uint32_t>(uint32_t *sendrecvbuf, int count, ReduceOp op) {
   typedef uint32_t DType;
   switch(op) {
     case kBitwiseOR: manager.AllReduce(sendrecvbuf, sizeof(DType), count, ReduceBitOR<DType>); return;
+    case kSum: manager.AllReduce(sendrecvbuf, sizeof(DType), count, ReduceSum<DType>); return;
+    case kMax: manager.AllReduce(sendrecvbuf, sizeof(DType), count, ReduceMax<DType>); return;
     default: utils::Error("reduce op not supported");
   }
 }

test/Makefile

@@ -1,5 +1,5 @@
 export CC  = gcc
-export CXX = clang++
+export CXX = g++
 export MPICXX = mpicxx
 export LDFLAGS= -pthread -lm 
 export CFLAGS = -Wall -O3 -msse2  -Wno-unknown-pragmas -fPIC -I../src

wrapper/xgboost.py

@@ -33,7 +33,10 @@ xglib.XGBoosterCreate.restype = ctypes.c_void_p
 xglib.XGBoosterPredict.restype = ctypes.POINTER(ctypes.c_float)
 xglib.XGBoosterEvalOneIter.restype = ctypes.c_char_p
 xglib.XGBoosterDumpModel.restype = ctypes.POINTER(ctypes.c_char_p)
-
+# sync function
+xglib.XGSyncGetRank.restype = ctypes.c_int
+xglib.XGSyncGetWorldSize.restype = ctypes.c_int
+# initialize communication module
 
 def ctypes2numpy(cptr, length, dtype):
     """convert a ctypes pointer array to numpy array """
@@ -553,3 +556,18 @@ def cv(params, dtrain, num_boost_round = 10, nfold=3, metrics=[], \
         sys.stderr.write(res+'\n')
         results.append(res)
     return results
+
+# synchronization module
+def sync_init(args = sys.argv):
+    arr = (ctypes.c_char_p * len(args))()
+    arr[:] = args
+    xglib.XGSyncInit(len(args), arr)
+    
+def sync_finalize():
+    xglib.XGSyncFinalize()
+
+def sync_get_rank():
+    return int(xglib.XGSyncGetRank())
+
+def sync_get_world_size():
+    return int(xglib.XGSyncGetWorldSize())

wrapper/xgboost_wrapper.cpp

@@ -80,6 +80,23 @@ class Booster: public learner::BoostLearner {
 using namespace xgboost::wrapper;
 
 extern "C"{
+  void XGSyncInit(int argc, char *argv[]) {
+    sync::Init(argc, argv);
+    if (sync::IsDistributed()) {
+      std::string pname = xgboost::sync::GetProcessorName();
+      utils::Printf("distributed job start %s:%d\n", pname.c_str(), xgboost::sync::GetRank());
+    }
+  }
+  void XGSyncFinalize(void) {
+    sync::Finalize();
+  }
+  int XGSyncGetRank(void) {
+    int rank = xgboost::sync::GetRank();
+    return rank;
+  }
+  int XGSyncGetWorldSize(void) {
+    return sync::GetWorldSize();
+  }
   void* XGDMatrixCreateFromFile(const char *fname, int silent) {
     return LoadDataMatrix(fname, silent != 0, false);
   }

wrapper/xgboost_wrapper.h

@@ -17,6 +17,28 @@ typedef unsigned long bst_ulong;
 #ifdef __cplusplus
 extern "C" {
 #endif
+  /*!
+   * \brief initialize sync module, this is needed if used in distributed model
+   *        normally, argv need to contain master_uri and master_port
+   *        if start using submit_job_tcp script, then pass args to this will do
+   * \param argc number of arguments
+   * \param argv the arguments to be passed in sync module
+   */
+  XGB_DLL void XGSyncInit(int argc, char *argv[]);
+  /*!
+   * \brief finalize sync module, call this when everything is done
+   */
+  XGB_DLL void XGSyncFinalize(void);
+  /*!
+   * \brief get the rank 
+   * \return return the rank of 
+   */
+  XGB_DLL int XGSyncGetRank(void);
+  /*!
+   * \brief get the world size from sync
+   * \return return the number of distributed job ran in the group
+   */
+  XGB_DLL int XGSyncGetWorldSize(void);
   /*!
    * \brief load a data matrix 
    * \return a loaded data matrix