Utilities to bridge Canvas-generated course rosters with GitLab's API.

Overview

gitlab-canvas-utils

A collection of scripts originally written for CSE 13S. Oversees everything from GitLab course group creation, student repository creation, all the way to cloning repos and adding users to a shared resources repository.

Installation

To install the included scripts, run:

./install --all

To install the scripts and man pages for development, run:

./install --symlink

To uninstall the scripts, run:

$ ./uninstall.sh

Utilities

There are currently 7 scripts/utilities:

  1. addtorepos - adds students to a set of specified repositories as reporters
  2. checkout - checks out cloned student repositories to commit IDs submitted for a specific assignment.
  3. clone - clones student repositories.
  4. createrepos - creates course GitLab course and student repos.
  5. pushfiles - adds files to cloned student repositories, pushing the changes.
  6. rmfiles - removes files from cloned student repositories, pushing the changes.
  7. roster - scrapes Canvas for a CSV of the student roster.

Read the supplied man pages for more information on each of these utilities.

Creating GitLab course, student repos, and adding students to resources repository
$ roster | createrepos | addtoresources
Cloning all student repos and checking them out to submitted commit IDs
$ roster | clone | checkout --asgn=5

Paths

To get (arguably) the full experience of these utilities, you should add the installed scripts directory to your $PATH and the installed man page directory to your $MANPATH.

To add the scripts directory:

$ export PATH=$PATH:$HOME/.config/gcu/scripts

To add the man directory (the double colon is intentional):

$ export MANPATH=::$MANPATH:$HOME/.config/gcu/man

You may want to add these exports to your shell configuration files.

Course Configuration

After running the installation script, a configuration file will need to be modifed for the specific course that these utilities will be used for. To modify the configuration file, run:

vi $HOME/.config/gcu/config.toml

A template configuration file will be supplied during installation if one does not already exist. The configuration file should have this basic structure:

canvas_url = "https://canvas.ucsc.edu"
canvas_course_id = 42878
canvas_token = "<your token here>"
course = "cse13s"
quarter = "spring"
year = "2021"
gitlab_server = "https://git.ucsc.edu"
gitlab_token = "<your token here>"
gitlab_role = "developer"
template_repo = "https://git.ucsc.edu/euchou/cse13s-template.git"
  • canvas_url: the Canvas server that your course is hosted on.
  • canvas_course_id: the Canvas course ID for your course. The one in the template is for the Spring 2021 offering of CSE 13S. You can find any course ID directly from the course page's url on Canvas.
  • canvas_token: your Canvas access token as a string. To generate a Canvas token, head to your account settings on Canvas. There will be a button to create a new access token under the section titled Approved Integrations. Note that you must have at least TA-level privilege under the course you want to use these scripts with.
  • course, quarter, and year should reflect, as one can imagine, the course, quarter, and year in which the course is held.
  • gitlab_server: the GitLab server that you want to create the course group and student repos on.
  • gitlab_token: your GitLab token as a string. Your token should have API-level privilege.
  • gitlab_role: the default role of students for their individual or shared repositories.
  • template_repo: the template repository to import and use as a base for student repositories. Note that this template repository will need to be publically visible.

Contributing

If you are interested in contributing to these scripts, send an email to [email protected]. Questions are welcomed as well.

Owner
Eugene Chou
Eugene Chou
NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions

NeoDTI NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions (Bioinformatics).

62 Nov 26, 2022
MT-GAN-PyTorch - PyTorch Implementation of Learning to Transfer: Unsupervised Domain Translation via Meta-Learning

MT-GAN-PyTorch PyTorch Implementation of AAAI-2020 Paper "Learning to Transfer: Unsupervised Domain Translation via Meta-Learning" Dependency: Python

29 Oct 19, 2022
This is a collection of our NAS and Vision Transformer work.

AutoML - Neural Architecture Search This is a collection of our AutoML-NAS work iRPE (NEW): Rethinking and Improving Relative Position Encoding for Vi

Microsoft 832 Jan 08, 2023
The source codes for ACL 2021 paper 'BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data'

BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data This repository provides the implementation details for

124 Dec 27, 2022
Make a Turtlebot3 follow a figure 8 trajectory and create a robot arm and make it follow a trajectory

HW2 - ME 495 Overview Part 1: Makes the robot move in a figure 8 shape. The robot starts moving when launched on a real turtlebot3 and can be paused a

Devesh Bhura 0 Oct 21, 2022
MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks

MEAL-V2 This is the official pytorch implementation of our paper: "MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tric

Zhiqiang Shen 653 Dec 19, 2022
State of the Art Neural Networks for Generative Deep Learning

pyradox-generative State of the Art Neural Networks for Generative Deep Learning Table of Contents pyradox-generative Table of Contents Installation U

Ritvik Rastogi 8 Sep 29, 2022
Network Compression via Central Filter

Network Compression via Central Filter Environments The code has been tested in the following environments: Python 3.8 PyTorch 1.8.1 cuda 10.2 torchsu

2 May 12, 2022
[AAAI22] Reliable Propagation-Correction Modulation for Video Object Segmentation

Reliable Propagation-Correction Modulation for Video Object Segmentation (AAAI22) Preview version paper of this work is available at: https://arxiv.or

Xiaohao Xu 70 Dec 04, 2022
Code for binary and multiclass model change active learning, with spectral truncation implementation.

Model Change Active Learning Paper (To Appear) Python code for doing active learning in graph-based semi-supervised learning (GBSSL) paradigm. Impleme

Kevin Miller 1 Jul 24, 2022
Code for 2021 NeurIPS --- Towards Multi-Grained Explainability for Graph Neural Networks

ReFine: Multi-Grained Explainability for GNNs This is the official code for Towards Multi-Grained Explainability for Graph Neural Networks (NeurIPS 20

Shirley (Ying-Xin) Wu 47 Dec 16, 2022
Multi-Horizon-Forecasting-for-Limit-Order-Books

Multi-Horizon-Forecasting-for-Limit-Order-Books This jupyter notebook is used to demonstrate our work, Multi-Horizon Forecasting for Limit Order Books

Zihao Zhang 116 Dec 23, 2022
IDA file loader for UF2, created for the DEFCON 29 hardware badge

UF2 Loader for IDA The DEFCON 29 badge uses the UF2 bootloader, which conveniently allows you to dump and flash the firmware over USB as a mass storag

Kevin Colley 6 Feb 08, 2022
Pytorch implementation of Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors

Make-A-Scene - PyTorch Pytorch implementation (inofficial) of Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors (https://arxiv.org/

Casual GAN Papers 259 Dec 28, 2022
This solves the autonomous driving issue which is supported by deep learning technology. Given a video, it splits into images and predicts the angle of turning for each frame.

Self Driving Car An autonomous car (also known as a driverless car, self-driving car, and robotic car) is a vehicle that is capable of sensing its env

Sagor Saha 4 Sep 04, 2021
TensorFlow for Raspberry Pi

TensorFlow on Raspberry Pi It's officially supported! As of TensorFlow 1.9, Python wheels for TensorFlow are being officially supported. As such, this

Sam Abrahams 2.2k Dec 16, 2022
Official implementation of "Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation" (RSS 2022)

Intro Official implementation of "Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation" Robotics:Science and

Yunho Kim 21 Dec 07, 2022
SymmetryNet: Learning to Predict Reflectional and Rotational Symmetries of 3D Shapes from Single-View RGB-D Images

SymmetryNet SymmetryNet: Learning to Predict Reflectional and Rotational Symmetries of 3D Shapes from Single-View RGB-D Images ACM Transactions on Gra

26 Dec 05, 2022
Music Source Separation; Train & Eval & Inference piplines and pretrained models we used for 2021 ISMIR MDX Challenge.

Introduction 1. Usage (For MSS) 1.1 Prepare running environment 1.2 Use pretrained model 1.3 Train new MSS models from scratch 1.3.1 How to train 1.3.

Leo 100 Dec 25, 2022
PyTorch for Semantic Segmentation

PyTorch for Semantic Segmentation This repository contains some models for semantic segmentation and the pipeline of training and testing models, impl

Zijun Deng 1.7k Jan 06, 2023