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Welcome to BulkHours documentation page

🛠️The aim of the package:

• 🔗Create a real-time interactivity between 🧑‍🎓students and 👨‍🏫evaluators (through Jupyter notebooks),
• 📈Serve as as data provider for practical case studies to support courses (data science, physics and IT for now),
• 🤖Automatic evaluation of students,
• 👨‍💻Tools to develop in C/C++/CUDA within a jupyter notebook environement (with a python kernel),
• 🧠Simple Interfaces with new machine learning trends packages🤗.

With great feed-backs 🚀🏆🎯 from users, there is an on-going effort to industrialize the package. 📧 contact

Bulkhours_eu.mp4

🎒Usage in few examples

🔗1.Sigmoid: evaluation automation [🧑‍🎓version]

• Create a exercice with real-time monitoring,
• Vizualize the exercice of a student,
• Build up an automatic evaluation of the exercice.
  

👨‍🏫As an evaluator:

🧑‍🎓As a student:

📋2.Classroom/course administration [🧑‍🎓version]

• Edit the classrooms composition,
• Edit the notebooks and global parameters,
• How to follow up the students activity.
  

👨‍🏫As an evaluator:

🧑‍🎓As a student:

🌍3.Global warming: dataset studies [🧑‍🎓version]

• How to explore data in the package,
• How to load data from the package,
• How to add a new dataset.
  

👨‍🏫As an evaluator:

🧑‍🎓As a student:

🧮4.C++/CUDA review [🧑‍🎓version]

• Create a exercice with real-time monitoring,
• Vizualize the exercice of a student,
• Build up an automatic evaluation of the exercice.
  

👨‍🏫As an evaluator:

🧑‍🎓As a student:

🧠5.Machine learning examples [🧑‍🎓version]

• Check Chatgpt answers to an exercice,
• Use Chatgpt to generate an exercice,
• Use an existing network to tag a image,
• Train a lunar landing system.
  

👨‍🏫As an evaluator:

🧑‍🎓As a student:

🦠6.Data science examples [🧑‍🎓version]

• Inequalities vizualization per country,
• Predict covid cases,
• Aurora borealis predictions,
• Plot the Moore's law.
  

👨‍🏫As an evaluator:

🧑‍🎓As a student:

🧑‍🎓Documentation of the main methods

📱bulkhours.init_env(email=MY_EMAIL)

You have to identify yourself to be able to share your cell with others ▶️📚.

🔗%%evaluation_cell_id -i MY_EXERCICE_ID

Calling this jupyter magic command is important to identify and link cells from different notebooks ▶️📚.

🧮%%compile_and_exec -c {nvcc,g++,gcc}

This magic method will compile and run the content of the current cell ▶️📚.

📈bulkhours.get_data(MY_DATASET)

This is a standard way to get bulkhours data▶️📚.

👨‍🏫Documentation of the main evaluators' methods

❓def student_evaluation_function()

It will be used to automaticaly estimate the exercice. If not defined, no automatic evaluation should ne available▶️📚.

💡def student_explanation_function()

When it is defined in a cell, this code will be executed after the publication of the correction to give explanation to students. It not present, nothing happen▶️📚.

💻bulkhours.is_equal(DATA_TO_TEST)

This method compares the students results data with reference data (by default, the teacher's data)▶️📚.

📋bulkhours.admin.dashboard(MY_DATASET)

Main menu to configure the course parameters▶️📚.

📰bulkhours.admin.summary()

This will show a resumé of all the students situation▶️📚.

✏️bulkhours.admin.evaluate(MY_EXERCICE_ID, STUDENT_EMAIL)

If you want to show the specific work of a particular student▶️📚.