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
Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity

Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity Indic TTS Samples can be found at https://peter-yh-wu.github.io/cross-

Peter Wu 1 Nov 12, 2022
Demonstrates iterative FGSM on Apple's NeuralHash model.

apple-neuralhash-attack Demonstrates iterative FGSM on Apple's NeuralHash model. TL;DR: It is possible to apply noise to CSAM images and make them loo

Lim Swee Kiat 11 Jun 23, 2022
A high-performance distributed deep learning system targeting large-scale and automated distributed training.

HETU Documentation | Examples Hetu is a high-performance distributed deep learning system targeting trillions of parameters DL model training, develop

DAIR Lab 150 Dec 21, 2022
Imposter-detector-2022 - HackED 2022 Team 3IQ - 2022 Imposter Detector

HackED 2022 Team 3IQ - 2022 Imposter Detector By Aneeljyot Alagh, Curtis Kan, Jo

Joshua Ji 3 Aug 20, 2022
BoxInst: High-Performance Instance Segmentation with Box Annotations

Introduction This repository is the code that needs to be submitted for OpenMMLab Algorithm Ecological Challenge, the paper is BoxInst: High-Performan

88 Dec 21, 2022
基于PaddleClas实现垃圾分类,并转换为inference格式用PaddleHub服务端部署

百度网盘链接及提取码: 链接:https://pan.baidu.com/s/1HKpgakNx1hNlOuZJuW6T1w 提取码:wylx 一个垃圾分类项目带你玩转飞桨多个产品(1) 基于PaddleClas实现垃圾分类,导出inference模型并利用PaddleHub Serving进行服务

thomas-yanxin 22 Jul 12, 2022
Playing around with FastAPI and streamlit to create a YoloV5 object detector

FastAPI-Streamlit-based-YoloV5-detector Playing around with FastAPI and streamlit to create a YoloV5 object detector It turns out that a User Interfac

2 Jan 20, 2022
Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration

This repo is for the paper: Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration The DAC environment is based on the Dynam

Carola Doerr 1 Aug 19, 2022
This repo implements several applications of the proposed generalized Bures-Wasserstein (GBW) geometry on symmetric positive definite matrices.

GBW This repo implements several applications of the proposed generalized Bures-Wasserstein (GBW) geometry on symmetric positive definite matrices. Ap

Andi Han 0 Oct 22, 2021
A library for researching neural networks compression and acceleration methods.

A library for researching neural networks compression and acceleration methods.

Intel Labs 100 Dec 29, 2022
HTSeq is a Python library to facilitate processing and analysis of data from high-throughput sequencing (HTS) experiments.

HTSeq DEVS: https://github.com/htseq/htseq DOCS: https://htseq.readthedocs.io A Python library to facilitate programmatic analysis of data from high-t

HTSeq 57 Dec 20, 2022
The implementation of 'Image synthesis via semantic composition'.

Image synthesis via semantic synthesis [Project Page] by Yi Wang, Lu Qi, Ying-Cong Chen, Xiangyu Zhang, Jiaya Jia. Introduction This repository gives

DV Lab 71 Jan 06, 2023
Twins: Revisiting the Design of Spatial Attention in Vision Transformers

Twins: Revisiting the Design of Spatial Attention in Vision Transformers Very recently, a variety of vision transformer architectures for dense predic

482 Dec 18, 2022
Pytorch code for "Text-Independent Speaker Verification Using 3D Convolutional Neural Networks".

:speaker: Deep Learning & 3D Convolutional Neural Networks for Speaker Verification

Amirsina Torfi 114 Dec 18, 2022
Demonstration of transfer of knowledge and generalization with distillation

Distilling-the-Knowledge-in-a-Neural-Network This is an implementation of a part of the paper "Distilling the Knowledge in a Neural Network" (https://

26 Nov 25, 2022
Code for "OctField: Hierarchical Implicit Functions for 3D Modeling (NeurIPS 2021)"

OctField(Jittor): Hierarchical Implicit Functions for 3D Modeling Introduction This repository is code release for OctField: Hierarchical Implicit Fun

55 Dec 08, 2022
CompilerGym is a library of easy to use and performant reinforcement learning environments for compiler tasks

CompilerGym is a library of easy to use and performant reinforcement learning environments for compiler tasks

Facebook Research 721 Jan 03, 2023
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning

Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning This is the code for implementing the MADDPG algorithm presented in

97 Dec 21, 2022
Code for 2021 NeurIPS --- Towards Multi-Grained Explainability for Graph Neural Networks

ReFine: Multi-Grained Explainability for GNNs We are trying hard to update the code, but it may take a while to complete due to our tight schedule rec

Shirley (Ying-Xin) Wu 47 Dec 16, 2022
EgGateWayGetShell py脚本

EgGateWayGetShell_py 免责声明 由于传播、利用此文所提供的信息而造成的任何直接或者间接的后果及损失,均由使用者本人负责,作者不为此承担任何责任。 使用 python3 eg.py urls.txt 目标 title:锐捷网络-EWEB网管系统 port:4430 漏洞成因 ?p

榆木 61 Nov 09, 2022