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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 Order 50+ flash drive for back up that will contain the VMs
- Smruti Padhy, Marcus Slavenas, (who else?) Create a VM with everything installed in it and take a snapshot which will then be deployed within Nebula. Approx. time required - 2 days
- Convert the VM created (using Openstack image) to virtualBox format (*vdi) and test the configurations.
- Make Smruti Padhy (who else??) Make a list of all softwares required and the directory structure for the tutorial
- Write a script for deployment of 50 VMs from the VM snapshot that we created.
- Local installation of fence with local authentication. This is for backup to be provided in the preinstalled VM.
- 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.
- Clear Instruction of how to access the VMs (e.g., through ssh), from different OSes.
- 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.
- Create wiki pages with clear instructions
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- Demonstration of use of BD Fiddle (20 mins)
- Sign up for Brown Dog Service
- Obtain a key/token using curl or Postman or use of IPython notebook
- 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.
- Training account for BD service (note this is different than Nebula account)
- 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.
- 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 mins): 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
- TODO:
- Provide a Python script for this and let participants use python library to use the BD service
- Provide a Step-by-step instructions with screenshot to do this
Extraction/Indexing/Retrieval Example (20 mins):
Emphasizes on extraction on unstructured data, indexing and content-based retrieval)
- Make sure OCR, Faceface, 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
- Create an example dataset with images and audios with 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.
- Write 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 call to and submit their requests.
- The python script should write the tags and technical metadata to a local file. (Probably can use
- Problem 2 : Given a collection of text files from a survey or reviews for a book/movie, use sentiment analysis extractor to calculate the sentiment value for each file and group similar values together. (Emphasizes on extraction on unstructured data and useful analysis )
- python library's index method that writes it as feature vectors.)
- Write 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.
- A collection of text files with reviews
- Obtain an examples dataset from the web.
- Let the participant use the python library of BD to obtain key/token and submit request to BD-API gateway
- 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.
- Write a Python script that will serve as a stub for the BD client
- The participant will fill in the code to BD REST API call to submit their requests.
- Make sure the Sentiment Analysis extractor is running
- Saves the results for each text file in a single file with corresponding values
- Provide code for this in stub script
- Create separate folders and move the file based on the sentiment value
- Provide a code that will do the above action in the stub
(Optional) Index text files along with the sentiment values and use ES visualization tool to search for documents with sentiment value less than some number. - Problem 4: Given a collection of *.xlsx files, obtain some results based on some columns value. (Emphasizes on extraction and analysis on scientific data)
- Conversion Example (15 mins): 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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- Tutorial feedback form
- Announcement of next user workshop
OPTIONAL EXAMPLES
- Problem 2 : Given a collection of text files from a survey or reviews for a book/movie, use sentiment analysis extractor to calculate the sentiment value for each file and group similar values together. (Emphasizes on extraction on unstructured data and useful analysis )
- A collection of text files with reviews
- Obtain an examples dataset from the web.
- Let the participant use the python library of BD to obtain key/token and submit request to BD-API gateway
- 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.
- Write a Python script that will serve as a stub for the BD client
- The participant will fill in the code to BD REST API call to submit their requests.
- Make sure the Sentiment Analysis extractor is running
- Saves the results for each text file in a single file with corresponding values
- Provide code for this in stub script
- Create separate folders and move the file based on the sentiment value
- Provide a code that will do the above action in the stub
- (Optional) Index text files along with the sentiment values and use ES visualization tool to search for documents with sentiment value less than some number.
- A collection of text files with reviews