tqchen 75bf97b575 Squashed 'subtree/rabit/' changes from 091634b..59e63bc
59e63bc minor
6233050 ok
14477f9 add namenode
75a6d34 add libhdfs opts
e3c76bf minmum fix
8b3c435 chg
2035799 test code
7751b2b add debug
7690313 ok
bd346b4 ok
faba1dc add testload
6f7783e add testload
e5f0340 ok
3ed9ec8 chg
e552ac4 ask for more ram in am
b2505e3 only stop nm when sucess
bc696c9 add queue info
f3e867e add option queue
5dc843c refactor fileio
cd9c81b quick fix
1e23af2 add virtual destructor to iseekstream
f165ffb fix hdfs
8cc6508 allow demo to pass in env
fad4d69 ok
0fd6197 fix more
7423837 fix more
d25de54 add temporal solution, run_yarn_prog.py
e5a9e31 final attempt
ed3bee8 add command back
0774000 add hdfs to resource
9b66e7e fix hadoop
6812f14 ok
08e1c16 change hadoop prefix back to hadoop home
d6b6828 Update build.sh
146e069 bugfix: logical boundary for ring buffer
19cb685 ok
4cf3c13 Merge branch 'master' of ssh://github.com/tqchen/rabit
20daddb add tracker
c57dad8 add ringbased passing and batch schedule
295d8a1 update
994cb02 add sge
014c866 OK

git-subtree-dir: subtree/rabit
git-subtree-split: 59e63bc1354c9ff516d72d9a6468f6c431627202
2015-03-21 00:44:31 -07:00

rabit: Reliable Allreduce and Broadcast Interface

rabit is a light weight library that provides a fault tolerant interface of Allreduce and Broadcast. It is designed to support easy implementations of distributed machine learning programs, many of which fall naturally under the Allreduce abstraction. The goal of rabit is to support portable , scalable and reliable distributed machine learning programs.

Features

All these features comes from the facts about small rabbit:)

  • Portable: rabit is light weight and runs everywhere
    • Rabit is a library instead of a framework, a program only needs to link the library to run
    • Rabit only replies on a mechanism to start program, which was provided by most framework
    • You can run rabit programs on many platforms, including Yarn(Hadoop), MPI using the same code
  • Scalable and Flexible: rabit runs fast
    • Rabit program use Allreduce to communicate, and do not suffer the cost between iterations of MapReduce abstraction.
    • Programs can call rabit functions in any order, as opposed to frameworks where callbacks are offered and called by the framework, i.e. inversion of control principle.
    • Programs persist over all the iterations, unless they fail and recover.
  • Reliable: rabit dig burrows to avoid disasters
    • Rabit programs can recover the model and results using synchronous function calls.

Use Rabit

  • Type make in the root folder will compile the rabit library in lib folder
  • Add lib to the library path and include to the include path of compiler
  • Languages: You can use rabit in C++ and python
    • It is also possible to port the library to other languages

Contributing

Rabit is an open-source library, contributions are welcomed, including:

  • The rabit core library.
  • Customized tracker script for new platforms and interface of new languages.
  • Toolkits, benchmarks, resource (links to related repos).
  • Tutorial and examples about the library.
Description
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Readme 33 MiB
Languages
C++ 45.5%
Python 20.3%
Cuda 15.2%
R 6.8%
Scala 6.4%
Other 5.6%