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
Public implementation of the Convolutional Motif Kernel Network (CMKN) architecture

CMKN Implementation of the convolutional motif kernel network (CMKN) introduced in Ditz et al., "Convolutional Motif Kernel Network", 2021. Testing Yo

1 Nov 17, 2021
Implementation of association rules mining algorithms (Apriori|FPGrowth) using python.

Association Rules Mining Using Python Implementation of association rules mining algorithms (Apriori|FPGrowth) using python. As a part of hw1 code in

Pre 2 Nov 10, 2021
Code for the paper "Training GANs with Stronger Augmentations via Contrastive Discriminator" (ICLR 2021)

Training GANs with Stronger Augmentations via Contrastive Discriminator (ICLR 2021) This repository contains the code for reproducing the paper: Train

Jongheon Jeong 174 Dec 29, 2022
Code for You Only Cut Once: Boosting Data Augmentation with a Single Cut

You Only Cut Once (YOCO) YOCO is a simple method/strategy of performing augmenta

88 Dec 28, 2022
GemNet model in PyTorch, as proposed in "GemNet: Universal Directional Graph Neural Networks for Molecules" (NeurIPS 2021)

GemNet: Universal Directional Graph Neural Networks for Molecules Reference implementation in PyTorch of the geometric message passing neural network

Data Analytics and Machine Learning Group 124 Dec 30, 2022
Reference code for the paper "Cross-Camera Convolutional Color Constancy" (ICCV 2021)

Cross-Camera Convolutional Color Constancy, ICCV 2021 (Oral) Mahmoud Afifi1,2, Jonathan T. Barron2, Chloe LeGendre2, Yun-Ta Tsai2, and Francois Bleibe

Mahmoud Afifi 76 Jan 07, 2023
Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation.

Training Script for Reuse-VOS This code implementation of CVPR 2021 paper : Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Vi

HYOJINPARK 22 Jan 01, 2023
CTF challenges from redpwnCTF 2021

redpwnCTF 2021 Challenges This repository contains challenges from redpwnCTF 2021 in the rCDS format; challenge information is in the challenge.yaml f

redpwn 27 Dec 07, 2022
Self-supervised Multi-modal Hybrid Fusion Network for Brain Tumor Segmentation

JBHI-Pytorch This repository contains a reference implementation of the algorithms described in our paper "Self-supervised Multi-modal Hybrid Fusion N

FeiyiFANG 5 Dec 13, 2021
Selective Wavelet Attention Learning for Single Image Deraining

SWAL Code for Paper "Selective Wavelet Attention Learning for Single Image Deraining" Prerequisites Python 3 PyTorch Models We provide the models trai

Bobo 9 Jun 17, 2022
Python3 / PyTorch implementation of the following paper: Fine-grained Semantics-aware Representation Enhancement for Self-supervisedMonocular Depth Estimation. ICCV 2021 (oral)

FSRE-Depth This is a Python3 / PyTorch implementation of FSRE-Depth, as described in the following paper: Fine-grained Semantics-aware Representation

77 Dec 28, 2022
Pytorch implementation of our method for regularizing nerual radiance fields for few-shot neural volume rendering.

InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering Pytorch implementation of our method for regularizing nerual radiance fields f

106 Jan 06, 2023
Learning Visual Words for Weakly-Supervised Semantic Segmentation

[IJCAI 2021] Learning Visual Words for Weakly-Supervised Semantic Segmentation Implementation of IJCAI 2021 paper Learning Visual Words for Weakly-Sup

Lixiang Ru 24 Oct 05, 2022
The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of Color

The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of Color Overview Code and dataset for The World of an Octopus: H

1 Nov 13, 2021
Accommodating supervised learning algorithms for the historical prices of the world's favorite cryptocurrency and boosting it through LightGBM.

Accommodating supervised learning algorithms for the historical prices of the world's favorite cryptocurrency and boosting it through LightGBM.

1 Nov 27, 2021
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation

AtlasNet [Project Page] [Paper] [Talk] AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation Thibault Groueix, Matthew Fisher, Vladimir

577 Dec 17, 2022
Object tracking and object detection is applied to track golf puts in real time and display stats/games.

Putting_Game Object tracking and object detection is applied to track golf puts in real time and display stats/games. Works best with the Perfect Prac

Max 1 Dec 29, 2021
Pytorch implementation for the Temporal and Object Quantification Networks (TOQ-Nets).

TOQ-Nets-PyTorch-Release Pytorch implementation for the Temporal and Object Quantification Networks (TOQ-Nets). Temporal and Object Quantification Net

Zhezheng Luo 9 Jun 30, 2022
Locally Constrained Self-Attentive Sequential Recommendation

LOCKER This is the pytorch implementation of this paper: Locally Constrained Self-Attentive Sequential Recommendation. Zhankui He, Handong Zhao, Zhe L

Zhankui (Aaron) He 8 Jul 30, 2022