yarn is part of hadoop script
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@ -197,13 +197,10 @@ Rabit is a portable library that can run on multiple platforms.
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* You can use [../tracker/rabit_hadoop.py](../tracker/rabit_hadoop.py) to run rabit programs on hadoop
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* This will start n rabit programs as mappers of MapReduce
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* Each program can read its portion of data from stdin
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* Yarn is highly recommended, since Yarn allows specifying number of cpus and memory of each mapper:
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* Yarn(Hadoop 2.0 or higher) is highly recommended, since Yarn allows specifying number of cpus and memory of each mapper:
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- This allows multi-threading programs in each node, which can be more efficient
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- An easy multi-threading solution could be to use OpenMP with rabit code
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#### Running Rabit on Yarn
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* To Be modified from [../tracker/rabit_hadoop.py](../tracker/rabit_hadoop.py)
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#### Running Rabit using MPI
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* You can submit rabit programs to an MPI cluster using [../tracker/rabit_mpi.py](../tracker/rabit_mpi.py).
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* If you linked your code against librabit_mpi.a, then you can directly use mpirun to submit the job
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@ -89,8 +89,7 @@ assert out[0] == 'Hadoop', 'cannot parse hadoop version string'
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hadoop_version = out[1].split('.')
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use_yarn = int(hadoop_version[0]) >= 2
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if not use_yarn:
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print 'Current Hadoop Version is %s' % out[1]
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print 'Current Hadoop Version is %s' % out[1]
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def hadoop_streaming(nworker, worker_args, use_yarn):
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fset = set()
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