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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 (who else??) Make a list of all softwares required and the directory structure for the tutorial
- Smruti Padhy, Marcus Slavenas, (who else?) Create Create 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.
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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.
- Eugene Roeder Create wiki pages with clear instructions
- 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.
- 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
- TODO
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- 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)
- Jay Alameda Aquire list of attendees
- 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
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- 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
- OCR
- Face
- Eyes
- Closeup
- Smruti Padhy speech2text
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
- TODO
- (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)
- 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)
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- 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
- 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
- Smruti Padhy, Marcus Slavenas, Jong Lee Provide step-by-step instructions/screenshots of modifying imagemagick and usage of polyglot GUI. Provide a default username/password
- Marcus Slavenas, Kenton McHenry Write a converter using FFmpeg
- 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
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- 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 name feedback-sea13.doc height 250 - Feed back form - updated
View file name feedback-XSEDEDemo.docx height 250 - Announcement of next user workshop
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