Unraveling Multimodality with Large Language Models.pdf
An example Hadoop Install
1. Apache Hadoop Install Example
● Using Ubuntu 12.04
● Java 1.6
● Hadoop 1.2.0
● Static DNS
● 3 Machine Cluster
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2. Install Step 1
● Install Ubuntu Linux 12.04 on each machine
● Assign a hostname and static IP address to each machine
● Names used here
– hc1nn ( hadoop cluster 1 name node )
– hc1r1m1 ( hadoop cluster 1 rack 1 machine 1 )
– hc1r1m2 ( hadoop cluster 1 rack 1 machine 2 )
● Install ssh daemon on each server
● Install vsftpd ( ftp ) deamon on each server
● Update /etc/host with all hostnames on each server
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3. Install Step 2
● Generate ssh keys for each server under hadoop user
● Copy keys to all server's hadoop account
● Install java 1.6 ( we used openjdk )
● Obtain the Hadoop software from
– hadoop.apache.org
– Unpack Hadoop software to /usr/local
● Now consider cluster architecture
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4. Install Step 3
● Start will three single installs
– For Hadoop
● Then cluster the
– Hadoop machines
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5. Install Step 4
● Ensure auto shh
– From name node (hc1nn) to both data nodes
– From each machine to itself
● Create symbolic link
– Named hadoop
– Pointing to /usr/local/hadoop-1.2.0
● Set up Bash .bashrc on each machine hadoop user set
– HADOOP_HOME
– JAVA_HOME
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6. Install Step 5
● Create Hadoop tmp dir on all servers
sudo mkdir -p /app/hadoop/tmp
sudo chown hadoop:hadoop /app/hadoop/tmp
sudo chmod 750 /app/hadoop/tmp
● Set Up conf/core-site.xml
– ( on all servers )
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7. Install Step 5
<property>
<name>hadoop.tmp.dir</name>
<value>/app/hadoop/tmp</value>
<description>A base for other temporary directories.</description>
</property>
<property>
<name>fs.default.name</name>
<value>hdfs://localhost:54310</value>
<description>The name of the default file system. A URI whose
scheme and authority determine the FileSystem implementation. The
uri's scheme determines the config property (fs.SCHEME.impl) naming
the FileSystem implementation class. The uri's authority is used to
determine the host, port, etc. for a filesystem.</description>
</property>
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8. Install Step 6
● Set Up conf/mapred-site.xml
– ( on all servers )
<property>
<name>mapred.job.tracker</name>
<value>localhost:54311</value>
<description>The host and port that the MapReduce job tracker runs
at. If "local", then jobs are run in-process as a single map
and reduce task.
</description>
</property>
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9. Install Step 7
● Set Up conf/hdfs-site.xml
– ( on all servers )
<property>
<name>dfs.replication</name>
<value>1</value>
<description>Default block replication.
The actual number of replications can be specified when the file is created.
The default is used if replication is not specified in create time.
</description>
</property>
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10. Install Step 8
● Format the Hadoop file system ( on all servers )
– hadoop namenode -format
– Dont do this on a running HDFS you will lose all data !!
● Now start Hadoop ( on all servers )
– $HADOOP_HOME/bin/start-all.sh
● Check Hadoop is running with
– sudo netstat -plten | grep java
– you should see ports like 54310 and 54311 being used.
● All Good ? Stop Hadoop on all servers
– $HADOOP_HOME/bin/stop-all.sh
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11. Install Step 9
● Now set up the cluster – do on all servers
● Set $HADOOP_HOME/conf/masters file to contain
– hc1nn
● Set $HADOOP_HOME/conf/slaves file to contain
– hc1r1m1
– hc1r1m2
– hc1nn
● We will be using the name node as a data node as well
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12. Install Step 10
on all machines
● Change conf/core-site.xml on all machines
– fs.default.name = hdfs://hc1nn:54310
● Change conf/mapred-site.xml
– mapred.job.tracker = hc1nn:54311
● Change conf/hdfs-site.xml
– dfs.replication = 3
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13. Install Step 11
● Now reformat the HDFS on hc1nn
– hadoop namenode -format
● On name node start HDFS
– $HADOOP_HOME/bin/start-dfs.sh
● On name node start Map Reduce
– $HADOOP_HOME/bin/start-mapred.sh
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14. Install Step 12
● Run a test Map Reduce job
– I have data in /tmp/gutenberg
● Load Data into HDFS
hadoop dfs -copyFromLocal /tmp/gutenberg /usr/hadoop/gutenberg
● List Data in HDFS
hadoop dfs -ls /usr/hadoop/gutenberg
Found 18 items
-rw-r--r-- 3 hadoop supergroup 674389 2013-07-30 19:31 /usr/hadoop/gutenberg/pg20417.txt
-rw-r--r-- 3 hadoop supergroup 674389 2013-07-30 19:31 /usr/hadoop/gutenberg/pg20417.txt1
...............
-rw-r--r-- 3 hadoop supergroup 834980 2013-07-30 19:31 /usr/hadoop/gutenberg/pg5000.txt4
-rw-r--r-- 3 hadoop supergroup 834980 2013-07-30 19:31 /usr/hadoop/gutenberg/pg5000.txt5
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15. Install Step 13
● Run the Map Reduce job
cd $HADOOP_HOME
hadoop jar hadoop*examples*.jar wordcount /usr/hduser/gutenberg /usr/hduser/gutenberg-output
● Check the output
13/07/30 19:34:13 INFO input.FileInputFormat: Total input paths to process : 18
13/07/30 19:34:13 INFO util.NativeCodeLoader: Loaded the native-hadoop library
13/07/30 19:34:14 INFO mapred.JobClient: Running job: job_201307301931_0001
13/07/30 19:34:15 INFO mapred.JobClient: map 0% reduce 0%
13/07/30 19:34:26 INFO mapred.JobClient: map 11% reduce 0%
13/07/30 19:34:34 INFO mapred.JobClient: map 16% reduce 0%
13/07/30 19:34:35 INFO mapred.JobClient: map 22% reduce 0%
13/07/30 19:34:42 INFO mapred.JobClient: map 33% reduce 0%
13/07/30 19:34:43 INFO mapred.JobClient: map 33% reduce 7%
13/07/30 19:34:48 INFO mapred.JobClient: map 44% reduce 7%
13/07/30 19:34:52 INFO mapred.JobClient: map 44% reduce 14%
13/07/30 19:34:54 INFO mapred.JobClient: map 55% reduce 14%
13/07/30 19:35:01 INFO mapred.JobClient: map 66% reduce 14%
13/07/30 19:35:02 INFO mapred.JobClient: map 66% reduce 18%
13/07/30 19:35:06 INFO mapred.JobClient: map 72% reduce 18%
13/07/30 19:35:07 INFO mapred.JobClient: map 77% reduce 18%
13/07/30 19:35:08 INFO mapred.JobClient: map 77% reduce 25%
13/07/30 19:35:12 INFO mapred.JobClient: map 88% reduce 25%
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16. Install Step 13
13/07/30 19:35:17 INFO mapred.JobClient: map 88% reduce 29%
13/07/30 19:35:18 INFO mapred.JobClient: map 100% reduce 29%
13/07/30 19:35:23 INFO mapred.JobClient: map 100% reduce 33%
13/07/30 19:35:27 INFO mapred.JobClient: map 100% reduce 100%
13/07/30 19:35:28 INFO mapred.JobClient: Job complete: job_201307301931_0001
13/07/30 19:35:28 INFO mapred.JobClient: Counters: 29
13/07/30 19:35:28 INFO mapred.JobClient: Job Counters
13/07/30 19:35:28 INFO mapred.JobClient: Launched reduce tasks=1
13/07/30 19:35:28 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=119572
13/07/30 19:35:28 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0
13/07/30 19:35:28 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0
13/07/30 19:35:28 INFO mapred.JobClient: Launched map tasks=18
13/07/30 19:35:28 INFO mapred.JobClient: Data-local map tasks=18
13/07/30 19:35:28 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=61226
13/07/30 19:35:28 INFO mapred.JobClient: File Output Format Counters
13/07/30 19:35:28 INFO mapred.JobClient: Bytes Written=725257
13/07/30 19:35:28 INFO mapred.JobClient: FileSystemCounters
13/07/30 19:35:28 INFO mapred.JobClient: FILE_BYTES_READ=6977160
13/07/30 19:35:28 INFO mapred.JobClient: HDFS_BYTES_READ=17600721
13/07/30 19:35:28 INFO mapred.JobClient: FILE_BYTES_WRITTEN=14994585
13/07/30 19:35:28 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=725257
13/07/30 19:35:28 INFO mapred.JobClient: File Input Format Counters
13/07/30 19:35:28 INFO mapred.JobClient: Bytes Read=17598630
13/07/30 19:35:28 INFO mapred.JobClient: Map-Reduce Framework
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17. Install Step 14
● Check the job output
hadoop dfs -ls /usr/hadoop/gutenberg-output
Found 3 items
-rw-r--r-- 3 hadoop supergroup 0 2013-07-30 19:35 /usr/hadoop/gutenberg-output/_SUCCESS
drwxr-xr-x - hadoop supergroup 0 2013-07-30 19:34 /usr/hadoop/gutenberg-output/_logs
-rw-r--r-- 3 hadoop supergroup 725257 2013-07-30 19:35 /usr/hadoop/gutenberg-output/part-r-00000
● Now get results out of HDFS
hadoop dfs -cat /usr/hadoop/gutenberg-output/part-r-00000 > /tmp/hrun/cluster_run.txt
head -10 /tmp/hrun/cluster_run.txt
"(Lo)cra" 6
"1490 6
"1498," 6
"35" 6
"40," 6
"A 12
"AS-IS". 6
"A_ 6
"Absoluti 6
"Alack! 6
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18. Install Step 15
● Congratulations – you now have
– A working HDFS cluster
– With three data nodes
– One name node
– Tested via a Map Reduce job
● Detailed install instructions available from our site shop
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