Mentor Pipeline

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Use these tools to create/update a mentor for Mentorpal.

Quick Start

Required Setup (session videos and timestamps)

TODO: needs to download session videos and timestamp files automatically but for now... Download session videos and timestamps and place them under ./data/mentors/{mentor_id}/build/recordings eg.

data/mentors/my_new_mentor/
├── build
│   ├── recordings
│   │   ├── session1
│   │   │   ├── p001-bio-long.csv
│   │   │   ├── p001-bio-long.mp4
│   │   │   ├── p002-some-utterances.csv
│   │   │   ├── p002-some-utterances.mp4
│   │   ├── session2
│   │   │   ├── p001-more-questions.csv
│   │   │   ├── p001-more-questions.mp4

Build and Test a Mentor

If raw video, audio and timestamp files for a mentor are stored in S3 (more on this below), we can use the following commands to build a classifier for the mentor. Note that videos are not required to generate a classifier to a new mentor.

Create/update the training data

make data/mentors/{mentor_id}

Train {mentor} classifier

make checkpoint/{mentor_id}

Run mentorpal cluster with {mentor} data and classifier

cd .. && make local-run-dev

Generate web videos (if you're ready to run the website and not just classifier)

make videos/mentors/{mentor_id}

Example endpoints for mentorpal cluster

• Mentor Homepage (Test web site) http://localhost:8080/mentorpanel/?mentor={mentor}
• Mentor API (Test classifier, e.g. with Postman) http://localhost:8080/mentor-pipeline/questions/?mentor={mentor}&query={question}

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Pipeline Overview

The classification data pipeline can be used to create all data needed for a usable mentor from raw recording files.

Output

After running the pipeline the following files will be generated:

• {mentor}/data/classifier_data.csv
• {mentor}/data/topics.csv
• {mentor}/data/utterance_data.csv
• videos/{mentor}/mobile/idle.mp4
• videos/{mentor}/web/idle.mp4
• videos/{mentor}/mobile/{mentor}_{video_id}.mp4
• videos/{mentor}/web/{mentor}_{video_id}.mp4

Supplementary Documentation

Generating Timestamp Files

After the interview is done, watch it fully and note down the start and end timestamps for each question. Timestamp files should be in a CSV file of the following format:

Notes	Answer/Utterance	Question	Response start	Response end
(string)	(char: A/U)	(string)	(timestamp HH:MM:SS)	(timestamp HH:MM:SS)

Development

Any changes made to this repo should be covered by tests. To run the existing tests:

make test

All pushed commits must also pass format and lint checks. To check all required tests before a commit:

make test-all

To fix formatting issues:

make format

Licensing

All source code files must include a USC open license header.

To check if files have a license header:

make test-license

To add license headers:

make license

Releases

Currently, this image is semantically versioned. When making changes that you want to test in another project, create a branch and PR and then you can release a test tag one of two ways:

To build/push a work-in-progress tag of mentor-pipeline for the current commit in your branch

• find the docker_tag_commit workflow for your commit in circleci
• approve the workflow
• this will create a tag like https://hub.docker.com/mentor-pipeline:${COMMIT_SHA}

To build/push a pre-release semver tag of mentor-pipeline for the current commit in your branch

• create a github release from your development branch with tag format /^\d+\.\d+\.\d+(-[a-z\d\-.]+)?$/ (e.g. 1.0.0-alpha.1)
• find the docker_tag_release workflow for your git tag in circleci
• approve the workflow
• this will create a tag like uscictdocker/mentor-pipeline:1.0.0-alpha.1

Once your changes are approved and merged to master, you should create a release tag in semver format as follows:

• create a github release from master with tag format /^\d+\.\d+\.\d$/ (e.g. 1.0.0)
• find the docker_tag_release workflow for your git tag in circleci
• approve the workflow
• this will create a tag like uscictdocker/mentor-pipeline:1.0.0