166 lines
8.2 KiB
Python
Executable File
166 lines
8.2 KiB
Python
Executable File
#!/usr/bin/python
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"""
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Deprecated
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This is a script to submit rabit job using hadoop streaming.
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It will submit the rabit process as mappers of MapReduce.
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This script is deprecated, it is highly recommended to use rabit_yarn.py instead
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"""
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import argparse
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import sys
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import os
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import time
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import subprocess
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import warnings
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import rabit_tracker as tracker
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WRAPPER_PATH = os.path.dirname(__file__) + '/../wrapper'
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#!!! Set path to hadoop and hadoop streaming jar here
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hadoop_binary = 'hadoop'
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hadoop_streaming_jar = None
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# code
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hadoop_home = os.getenv('HADOOP_HOME')
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if hadoop_home != None:
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if hadoop_binary == None:
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hadoop_binary = hadoop_home + '/bin/hadoop'
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assert os.path.exists(hadoop_binary), "HADOOP_HOME does not contain the hadoop binary"
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if hadoop_streaming_jar == None:
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hadoop_streaming_jar = hadoop_home + '/lib/hadoop-streaming.jar'
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assert os.path.exists(hadoop_streaming_jar), "HADOOP_HOME does not contain the hadoop streaming jar"
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if hadoop_binary == None or hadoop_streaming_jar == None:
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warnings.warn('Warning: Cannot auto-detect path to hadoop or hadoop-streaming jar\n'\
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'\tneed to set them via arguments -hs and -hb\n'\
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'\tTo enable auto-detection, you can set enviroment variable HADOOP_HOME'\
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', or modify rabit_hadoop.py line 16', stacklevel = 2)
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parser = argparse.ArgumentParser(description='Rabit script to submit rabit jobs using Hadoop Streaming.'\
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'It is Highly recommended to use rabit_yarn.py instead')
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parser.add_argument('-n', '--nworker', required=True, type=int,
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help = 'number of worker proccess to be launched')
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parser.add_argument('-hip', '--host_ip', default='auto', type=str,
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help = 'host IP address if cannot be automatically guessed, specify the IP of submission machine')
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parser.add_argument('-i', '--input', required=True,
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help = 'input path in HDFS')
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parser.add_argument('-o', '--output', required=True,
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help = 'output path in HDFS')
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parser.add_argument('-v', '--verbose', default=0, choices=[0, 1], type=int,
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help = 'print more messages into the console')
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parser.add_argument('-ac', '--auto_file_cache', default=1, choices=[0, 1], type=int,
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help = 'whether automatically cache the files in the command to hadoop localfile, this is on by default')
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parser.add_argument('-f', '--files', default = [], action='append',
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help = 'the cached file list in mapreduce,'\
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' the submission script will automatically cache all the files which appears in command'\
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' This will also cause rewritten of all the file names in the command to current path,'\
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' for example `../../kmeans ../kmeans.conf` will be rewritten to `./kmeans kmeans.conf`'\
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' because the two files are cached to running folder.'\
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' You may need this option to cache additional files.'\
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' You can also use it to manually cache files when auto_file_cache is off')
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parser.add_argument('--jobname', default='auto', help = 'customize jobname in tracker')
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parser.add_argument('--timeout', default=600000000, type=int,
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help = 'timeout (in million seconds) of each mapper job, automatically set to a very long time,'\
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'normally you do not need to set this ')
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parser.add_argument('--vcores', default = -1, type=int,
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help = 'number of vcpores to request in each mapper, set it if each rabit job is multi-threaded')
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parser.add_argument('-mem', '--memory_mb', default=-1, type=int,
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help = 'maximum memory used by the process. Guide: set it large (near mapred.cluster.max.map.memory.mb)'\
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'if you are running multi-threading rabit,'\
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'so that each node can occupy all the mapper slots in a machine for maximum performance')
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if hadoop_binary == None:
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parser.add_argument('-hb', '--hadoop_binary', required = True,
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help="path to hadoop binary file")
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else:
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parser.add_argument('-hb', '--hadoop_binary', default = hadoop_binary,
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help="path to hadoop binary file")
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if hadoop_streaming_jar == None:
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parser.add_argument('-hs', '--hadoop_streaming_jar', required = True,
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help='path to hadoop streamimg jar file')
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else:
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parser.add_argument('-hs', '--hadoop_streaming_jar', default = hadoop_streaming_jar,
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help='path to hadoop streamimg jar file')
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parser.add_argument('command', nargs='+',
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help = 'command for rabit program')
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args = parser.parse_args()
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if args.jobname == 'auto':
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args.jobname = ('Rabit[nworker=%d]:' % args.nworker) + args.command[0].split('/')[-1];
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# detech hadoop version
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(out, err) = subprocess.Popen('%s version' % args.hadoop_binary, shell = True, stdout=subprocess.PIPE).communicate()
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out = out.split('\n')[0].split()
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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 use_yarn:
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warnings.warn('It is highly recommended to use rabit_yarn.py to submit jobs to yarn instead', stacklevel = 2)
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print 'Current Hadoop Version is %s' % out[1]
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def hadoop_streaming(nworker, worker_args, worker_envs, use_yarn):
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worker_envs['CLASSPATH'] = '`$HADOOP_HOME/bin/hadoop classpath --glob` '
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worker_envs['LD_LIBRARY_PATH'] = '{LD_LIBRARY_PATH}:$HADOOP_HDFS_HOME/lib/native:$JAVA_HOME/jre/lib/amd64/server'
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fset = set()
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if args.auto_file_cache:
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for i in range(len(args.command)):
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f = args.command[i]
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if os.path.exists(f):
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fset.add(f)
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if i == 0:
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args.command[i] = './' + args.command[i].split('/')[-1]
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else:
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args.command[i] = args.command[i].split('/')[-1]
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if args.command[0].endswith('.py'):
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flst = [WRAPPER_PATH + '/rabit.py',
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WRAPPER_PATH + '/librabit_wrapper.so',
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WRAPPER_PATH + '/librabit_wrapper_mock.so']
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for f in flst:
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if os.path.exists(f):
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fset.add(f)
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kmap = {}
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kmap['env'] = 'mapred.child.env'
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# setup keymaps
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if use_yarn:
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kmap['nworker'] = 'mapreduce.job.maps'
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kmap['jobname'] = 'mapreduce.job.name'
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kmap['nthread'] = 'mapreduce.map.cpu.vcores'
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kmap['timeout'] = 'mapreduce.task.timeout'
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kmap['memory_mb'] = 'mapreduce.map.memory.mb'
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else:
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kmap['nworker'] = 'mapred.map.tasks'
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kmap['jobname'] = 'mapred.job.name'
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kmap['nthread'] = None
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kmap['timeout'] = 'mapred.task.timeout'
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kmap['memory_mb'] = 'mapred.job.map.memory.mb'
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cmd = '%s jar %s' % (args.hadoop_binary, args.hadoop_streaming_jar)
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cmd += ' -D%s=%d' % (kmap['nworker'], nworker)
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cmd += ' -D%s=%s' % (kmap['jobname'], args.jobname)
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envstr = ','.join('%s=%s' % (k, str(v)) for k, v in worker_envs.items())
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cmd += ' -D%s=\"%s\"' % (kmap['env'], envstr)
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if args.vcores != -1:
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if kmap['nthread'] is None:
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warnings.warn('nthread can only be set in Yarn(Hadoop version greater than 2.0),'\
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'it is recommended to use Yarn to submit rabit jobs', stacklevel = 2)
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else:
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cmd += ' -D%s=%d' % (kmap['nthread'], args.vcores)
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cmd += ' -D%s=%d' % (kmap['timeout'], args.timeout)
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if args.memory_mb != -1:
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cmd += ' -D%s=%d' % (kmap['timeout'], args.timeout)
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cmd += ' -input %s -output %s' % (args.input, args.output)
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cmd += ' -mapper \"%s\" -reducer \"/bin/cat\" ' % (' '.join(args.command + worker_args))
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if args.files != None:
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for flst in args.files:
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for f in flst.split('#'):
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fset.add(f)
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for f in fset:
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cmd += ' -file %s' % f
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print cmd
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subprocess.check_call(cmd, shell = True)
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fun_submit = lambda nworker, worker_args, worker_envs: hadoop_streaming(nworker, worker_args, worker_envs, int(hadoop_version[0]) >= 2)
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tracker.submit(args.nworker, [], fun_submit = fun_submit, verbose = args.verbose, hostIP = args.host_ip)
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