...
- Participant will use his/her own laptop for this part
- We will provide a VM with everything pre-installed in it through Nebula.
- TODO: Rob will talk to Doug for this if we can spawn 50 VMs on Nebula for the tutorial session
- TODO: Order 50+ flash drive for back up that will contain the VMs
- TODO: Create a VM with everything installed in it and take a snapshot which will then be deployed within Nebula. Approx. time required - 2 days
- TODO: Not sure of Jetstream yet.
- Provide clear instructions as how to access VMs in Nebula with proper credentials.
- TODO: Clear Instruction of how to access the VMs (e.g., through ssh), for different OSes.
- (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.
- TODO: Create wiki pages with clear instructions
- We will provide a VM with everything pre-installed in it through Nebula.
- Demonstration of use of BD Fiddle
- Sign up for Brown Dog Service
- Obtain a key/token using curl or Postman
- 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.
- TODO: Create a document for the demo with step-by-step screenshots
- Create an applications using BD services
Three applications:- Problem 1 : Given a collection of images with text embedded in it
(or scanned handwritten documents image), try to search images based on its content.- One can upload images from local directory to obtain images . /use flicker API (may be just a sample snippet)or use external web service.
- TODO: Create an example dataset with images
- TODO: Provide a code snippet of using externel service to obtain images. e.g. Flicker API.
- This will only provided as an example and will not used for the rest of the code.
- Write a Python script to upload all images to BD (provide this code)
- Let the participant use the python library of BD to obtain key/token and write request to BD-API
- Make sure OCR ( or Census Extractor) is running
- Once technical metadata is obtained from BD, index it tags and technical metadata in an locally running Elasticsearch (provide this code also)
- Search for the image using ES query
- Participant will require to install ES for this application.
- One can upload images from local directory to obtain images . /use flicker API (may be just a sample snippet)or use external web service.
- 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.
- Write a Python script to upload all files from a directory to the BD API
- Saves the results for each text file in a single file
- Create separate folders and move the file based on the value
Tried to see Yelp API, IMDB API to obtain reviews (???) or use Twitter API (??) to pull some reviews
- Problem 3: Use BD convert to convert a collection of images with old format to png or pdf. convert odp/odt to ppt/doc
CSV files (Ameriflux), use BD for some gap-filing on the files and return result.- Think of a R client for BD. (PeCAn??)
Think of a MATLAB clientWant to build a Web application on top of BD, (Similar to what Marcus build??)- Use BD convert to convert a collection of images with old format to png or pdf. convert odp/odt to ppt/doc
- Problem 1 : Given a collection of images with text embedded in it
...