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| = Big Data course on Beocat =
| | This course is now available here: http://people.beocat.ksu.edu/~dan/education/bigdata/ |
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| The Pittsburgh Supercomputing Center hosts 2-day remote Big Data workshops
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| several times each year. The information provided here will allow individual
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| users to go through the videos at their own pace and perform the exercises
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| on our local Beocat supercomputer. Each exercise will have data and results
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| tailored to each individual to allow instructors to measure the progress of
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| students assigned to take this course interactively.
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| Use the Agenda website below to access the slides starting with the Welcome slides
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| that don't have an associated video. The '>' sign at the start of lines below
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| represents the command line prompt on Beocat, and '>>>' represents the prompt
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| you'll get when you start pyspark or python.
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| Agenda: https://www.psc.edu/images/xsedetraining/BigData/Big_Data_Welcome.pdf
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| Videos: https://www.youtube.com/watch?v=NpapUmGHXyw&list=PLdkRteUOw2X-YKqommnuGWqNfEEUG6P2E
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| == Welcome ==
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| ssh into Beocat from your computer and copy the workshop data to your
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| home directory.
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| > cp -rp ~daveturner/workshops/bigdata_workshop .
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| > cd bigdata_workshop
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| PDF versions of the slides are available for each section
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| as are directories containing the data for each set of exercises.
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| You can copy the PDF files to your local computer for viewing or click
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| on the web link for each section.
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| Follow along with the Welcome slides from the Agenda website link or PDF file
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| Big_Data_Welcome.pdf as you listen to the video. Much of this information is specific to
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| the Bridges supercomputer at PSC so just scan over these slides.
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| Welcome slides: https://www.psc.edu/images/xsedetraining/BigData/Big_Data_Welcome.pdf
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| == Intro to Big Data ==
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| Video: https://www.youtube.com/watch?v=NpapUmGHXyw&list=PLdkRteUOw2X-YKqommnuGWqNfEEUG6P2E&index=1
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| Slides: https://www.psc.edu/images/xsedetraining/BigData/A_Brief_History_of_Big_Data.pdf
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| Watch the video 'Intro to Big Data - Big Data Video 1' and follow along with the slides
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| == Hadoop ==
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| Video: https://www.youtube.com/watch?v=WpxBFQr-ccw&list=PLdkRteUOw2X-YKqommnuGWqNfEEUG6P2E&index=2
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| Slides: https://www.psc.edu/images/xsedetraining/BigData/Hadoop2019.pdf
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| Watch the video 'Hadoop - Big Data Video 2'
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| We do not have Hadoop on Beocat so the commands they cover will not work locally
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| == Intro to Spark and Spark sections combined ==
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| Video: https://www.youtube.com/watch?v=iONkwqP2fEk&list=PLdkRteUOw2X-YKqommnuGWqNfEEUG6P2E&index=3
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| Slides: https://www.psc.edu/images/xsedetraining/BigData/Intro_To_Spark.pdf
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| Watch the video 'Spark - Big Data Video 3'
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| The link below shows how to load the Spark and Python modules on Beocat,
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| set up the Python virtual environment, and run Spark code interactively
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| or through the Slurm scheduler.
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| https://support.beocat.ksu.edu/BeocatDocs/index.php/Installed_software#Spark
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| Pause the video and do the exercises 1-5 around the 43 minute mark.
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| Try these yourself before they cover the answers.
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| You can do demos and exercises interactively by requesting an Elf core
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| or you can submit the job using a script
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| (see ~/bigdata_workshop/Shakespeare/sb.shakespeare as an example).
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| Request 1 core on an Elf node for interactive use then load the modules
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| > srun -J srun -N 1 -n 1 -t 24:00:00 --mem=10G -C elves --pty bash
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|
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| > module purge
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| > module load Spark
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| > module load Python
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|
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| > source ~/.virtualenvs/spark-test/bin/activate
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|
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| > pyspark
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| >>>
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| Email your solutions to exercises 1-5 to Dan along with a description of
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| how well you did on your own. Also include your solutions to
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| homework assignments 1-3 around the 103 minute mark if you want to impress him.
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| Dave's answers are in ~/bigdata_workshop/Shakespeare/shakespeare.py.
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| == Machine Learning: Recommender System for Spark ==
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| Video: https://www.youtube.com/watch?v=2rvW13YSNmM&list=PLdkRteUOw2X-YKqommnuGWqNfEEUG6P2E&index=4
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| Slides: https://www.psc.edu/images/xsedetraining/BigData/A_Recommender_System.pdf
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| Watch the video 'Machine Learning Recommender System With Spark - Big Data Video 4'
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| If you want to run demos and exercises interactively,
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| request 1 core on an Elf node for interactive use then load the modules
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| and activate your Python virtual environment.
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| > srun -J srun -N 1 -n 1 -t 24:00:00 --mem=10G -C elves --pty bash
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|
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| > module purge
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| > module load Spark
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| > module load Python
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|
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| > source ~/.virtualenvs/spark-test/bin/activate
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| Do the 3 exercises at 1:06 in the video and email Dan your answers and
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| a summary of how you did on your own.
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| Demos and exercises can be run on the node you're on using pyspark-submit
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| > pyspark-submit recommender.py
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| You can also start pyspark and use it interactively
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| > pyspark
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| >>>
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| The recommender.py script can be run using the job script sb.recommender
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| > sbatch sb.recommender
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| == Deep Learning with TensorFlow ==
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| Video: https://www.youtube.com/watch?v=bC1mzhoabRE&list=PLdkRteUOw2X-YKqommnuGWqNfEEUG6P2E&index=5
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| Slides: https://www.psc.edu/images/xsedetraining/BigData/Deep_Learning.pdf
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| Watch the video 'Tensorflow - Big Data Video 5'
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| Beocat has versions of TensorFlow that work on both CPUs and GPUs.
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| Use <B>module spider TensorFlow</B> to see a list of available versions.
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| You can do the demos on Beocat if you want. There is a warning that the
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| mnist data will be deprecated in the future.
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| > module purge
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| > module load TensorFlow
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|
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| > source ~/.virtualenvs/spark-test/bin/activate
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|
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| > python
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| >>>
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| == Bridges ==
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| Video: https://www.youtube.com/watch?v=gfqlNW-zILo&list=PLdkRteUOw2X-YKqommnuGWqNfEEUG6P2E&index=6
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| Slides: https://www.psc.edu/images/xsedetraining/BigData/Goodbye.pdf
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| Watch the video 'A Big Data Platform - Big Data Video 6'. This will provide an overview of
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| the Bridges system at Pittsburgh Supercomputing Center.
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