Tutorial¶

In this document we will describe how to create a simple task, that checks that a code in Python returns “Hello World!”.

Note

Demonstration tasks are made available for download on the INGInious-demo-tasks repository. They can also be downloaded and installed automatically via the inginious-install script. You can also download courses examples on the marketplace page which allows to easily import courses files. The list of these open source courses is also available on the INGInious-courses repository

Creating the task description¶

Using the webapp¶

If you are using the webapp, this procedure can be done using the graphical interface:

Go to the Course administration/Tasks page, enter helloworld as a new task id and click on Create new task.
In the Basic settings tab, set the task name to Hello World! and put some context and author name. Container setup can be left with default parameters.
In the Subproblems tab, add a new code-type problem with problem id question1.
Set some problem name and context, and set language to python.
Save changes and go to Task files tab.

Manually¶

This is only possible if the administrator has given access to the course directory to the course administrator.

The task description is a YAML file describing everything that INGInious needs to know to verify the input of the student. Here is a simple task description. Put this file with the name task.yaml in a newly created helloworld folder in your course directory.

author: "The INGInious authors"
accessible: true
name: "Hello World!"
context: "In this task, you will have to write a python script that displays 'Hello World!'."
problems:
    question1:
        name: "Let's print it"
        header: "def func():"
        type: "code"
        language: "python"
limits:
    time: 10
    memory: 50
    output: 1000
environment: default

Most of the fields are self-explanatory. Some remarks:

The field problems is a dictionary of problems. Each problem must have an unique id, for example here question1.
Problem question1 have its type field that equals to code, which means the student must enter some code to answer the question. Other types exists, such as multiple-choice.
The field limits are the limits that the task cannot exceed. The time is in seconds, and memory and output are in MB.
The environment field is intended to change the environment where the tasks run. The available environments are those you downloaded during installation or those you created by creating a grading container. Please see create_container.

More documentation is available here: Task description files.

Creating the run file¶

In your task folder, you will put every file needed to test the input of the student. This folder content can be shown in the webapp in the Task files tab of the Edit task page.

Create a template file template.py, where we will put the code of the student.
```
def func():
    @    @question1@@

    func()
```
The syntax is very simple: put a first @ on the line where you want to put the code of the student. Then indent the line and write a second @. Now write the problem id of the problem you want to take the input from (question1) then write another @, write a possible suffix (not used here), and then finish the line with a last @.
Create the run file. This file will be the script that is launched when the task is started. Here we will create a bash script, that parses the template and verifies its content.
```
# This line parses the template and put the result in studentcode.py
parse_template("template.py", "student/studentcode.py")

# Verify the output of the code... (we ignore stderr and retval here)
output, _, _ = run_student_simple(python student/studentcode.py)

if output == "Hello World!":
    # The student succeeded
    set_global_result("success")
    set_global_feedback("You solved this difficult task!")
else:
    # The student succeeded
    set_global_result("failed")
    set_global_feedback("Your output is " + output)
```
Here we use four commands provided by INGInious, parse_template, run_simple, set_global_result and set_global_feedback. The code is self-explanatory; just notice the usage of run_student_simple (a version of run_student) that ask INGInious (precisely the Docker agent) to start a new student container and run inside the command python studentcode.py.

Please note that the run_student_simple command is fully configurable: you can change the environment on which you run the task, define new timeouts, memory limits, … See run_student for more details.
If not using the webapp, don’t forget to give the run file the execution rights:
```
$ chmod +x helloworld/run
```

More documentation is available here: Task description files.