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  • We will provide a VM with everything pre-installed in it through Nebula. 
    •  Rob Kooper will talk to Doug for this if we can spawn 50 VMs on Nebula for the tutorial session. (DONE) We will get  50 VMs on Nebula.

    •  Smruti Padhy Make a list of all softwares required and the directory structure for the tutorial
    •  Smruti PadhyCreate a VM with everything installed in it and take a snapshot which will then be deployed within Nebula. Approx. time required - 2 days
    •  Rui Liu Write a script for deployment of 50 VMs from the VM snapshot that we created. 
    •  Luigi Marini Testing BD service with 50 concurrent users to perform conversion/extractions tasks
    •  Luigi Marini Maximum size of file that can be uploaded to Brown Dog needs to be controlled. This is require to ensure no one uploads any large files.
    •  Not sure of Jetstream yet. 
    • Provide clear instructions as how to access VMs in Nebula with proper credentials.
      •  Smruti Padhy, Marcus Slavenas, Jong Lee Create powerpoint slides with clear Instructions of how to access the VMs (e.g., through ssh), from different OSes (Linux, Mac, Windows).
      •  Rui Liu Need training accounts on Nebula. Provide SSH key-pairs to each participant.
    • (Before tutorial - wiki pages with clear instructions) Installs Python/R/MATLAB/cURL to use BD Service along with the library required in case any one interested in using the BD services in future.

  • Backup - Provide VM through USB sticks in case of network interruption
    In addition to the installation required for VMs in Nebula, following are extra steps required for Backups VMs
    • TODO
      •  Smruti Padhy Convert the VM created (using Openstack image) to VirtualBox format (*vdi) and test the configurations.
      •  Smruti Padhy Order 50+ flash drive for back up that will contain the VMs (Received)
      •  Luigi Marini Test local installation of Fence with local authentication instead of Crowd. This is for backup to be provided in the preinstalled VM.
      •  Smruti Padhy bdfiddle installation
      •  Copy the VM and virtualbox to the flash drives

...

  • Demonstration of use of BD Fiddle (20 mins)
    • Sign up for Brown Dog Service
    • Obtain a key/token using curl on VM
      • Local VM and Nebula VM need to have curl available
    • Use token and bd fiddle interface to obtain to see BD in action. 
    • Copy paste the python code snippet and use it the application to be explained next. 
    •  Figure Kenton McHenry Figure out a good way to accept requests for new accounts for BD service (note this is different than Nebula account)
    •  Smruti Padhy, Marcus Slavenas, Jong Lee  Create a document for the demo with step-by-step screenshots for all above steps.
    •  Eugene Roeder Fix the CORS error for file url option (I think it is a known issue). Please add the JIRA issue number here.
    •  Christopher Navarro, Eugene Roeder Fix the delay experienced when file is uploaded from local directory to the bdfiddle ui

...

  • Create applications using BD services (50 mins)

    • Conversion Example (15 mins20mins):  To convert a collection of images/ps/odp/audio/video files to png/pdf/ppt/mp3.  This will demonstrates that if you have a directory with files in old file formats, just use BD to get it all converted without requiring to install any software. (Emphasizes on conversion)
      • Make sure imagemagick and ffmpeg converters are running before the demo
      • Obtain the BD token/key - ask participant to refer to previous bdfiddle step or use the python library
      • Ask the participant to check for the available output formats for specific input formats
      • Ask the participant to use python library to use BD service
      • TODO:
        •  Inna Zharnitsky Provide a Python script for this and let participants use python library to use the  BD service
        •  Smruti PadhyProvide a Step-by-step instructions with screenshot to do this
        •  (Optional) Provide R script for this problem.

    • Extraction/Indexing/Retrieval Example (20 mins): 

      Given a collection of images with text embedded in it, and audio files, search images/audio files based on its content. (

      Emphasizes on extraction on unstructured data, indexing and content-based retrieval)

      • Make sure OCR, face, and speech2text extractors are running before starting the demo
      • One can upload images from local directory to obtain images or use external web service.  
      • Let the participant use the python library of BD to obtain key/token and submit extraction request to BD-API gateway
      • Once technical metadata is obtained from BD, write the tags and technical metadata to a local file /python dictionary.
      • Search the file based on the tags/technical metadata by linear search on the index file

      • TODO
        •  Make sure the metadata to be posted as json-ld for the extractors 
        •  Create an example dataset with images and audios to which we can make interesting query
        •  (Optional) Provide a code snippet of using externel service to obtain images. e.g. Flicker API.
            •  This will only be provided as an example and will not be used for the rest of the code.
        •  Provide the link to the current BD REST API and create a document/wiki page showing step-by-step screenshots of obtaining a key/token using python library.
        •  Marcus SlavenasWrite a Python script that will serve as a stub for the BD client
          • The participant will fill in the code to use python library to call BD REST API and submit their requests. 
          •  The python script  should write the tags and technical metadata to a local file. (Probably can use python library's index method that writes it as feature vectors.)
        •   Marcus SlavenasWrite a Python script to make interesting search/query to the index file. Again probably use the python library's find method or just read the local file.     
        •  (Optional) provide R script for this problem

    •  Conversion & Extraction Example (15 10 min):  Given an audio image file of different format, convert it to a different format that speech2text extractor accepts and obtains the text for the audioface detection and OCR. 
      • TODO
        •  Write a Python script that does the conversion and then sends the converted file to the BD service.
        •  Create a step-by-step instructions document with screenshots.
        •  (Optional) Provide a R script for this problem

    • (Optional) Combination of Conversion & Extraction Example: Obtaining Ameriflux data and converting into *.clim format (similar to csv format but tab separated) for SNIPET model.  Calculate average air temperature and its standard deviation. (This will emphasize both conversion and analysis)

...

  • Part 1: Teach to write an extractor (35 mins)
    • Start with the bd-template extractor, which is the word count extractor.
    • Ask participant to modify the extractor, which would use 'grep' to find a specific pattern within the file.
    • Ask to change the name of the extractor from ncsa.wordcount to ncsa.grep.
    • Include yes/no in the metadata if the pattern is found or not found.
    • Briefly describe Json-ld support. Provide intuition behind the idea json-ld with a simple example. No need to go into details of RDF. 
    • TODO
      •  Smruti Padhy, Marcus Slavenas, Jong Lee  Provide Step-by-step instructions/screenshots of updating the extractor and the output as seen at the Clowder GUI.  Also provide link to json-ld for further readings. Provide minimum software requirements for the development such as Clowder, Rabbimq, MongoDB, pyclowder, python libraries, etc.
      •  Inna Zharnitsky Write an extractor that does grep along with the wordcount for demonstration purpose and include json-ld 
      •  Sandeep Puthanveetil Satheesan Write an extractor that accepts csv file with say 3 columns (probably with values from weather or bacterial growth model (see Problem 2.2 below)) , calculate the average of a specific column
        •  Provide step-by-step screenshots for writing such an extractor.

  • Part 2: Teach to write a converter (35 mins)
    • Start with the bd-template for converter- imagemagick
    • Ask the participant to modify the converter input/output formats in the comment section. And see the result using the polyglot web UI for post and get
    • Another example - FFmpeg converter for audio and video
    • TODO

  • Part 3: Teach to upload a converter or an extractor to the locally installed Tools Catalog. (20mins)
    •  Inna Zharnitsky Step-by-step procedure to upload an extractor or a converter, an input file and an output file without a docker file.

  • Part 4 (Optional - For advanced user): Dockerize the tool

...

  • TODO : Jay Alameda Design Tutorial feedback forms (SEA workshop form for Eclipse PTP included as a samlple):
  • TODO : Jay Alameda Print the feedback forms 
    View file
    namefeedback-sea13.doc
    height250
  • Feed back form - updated
    View file
    namefeedback-XSEDEDemo.docx
    height250
  • Announcement of next user workshop

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