ldssa.md

LDSSA Learning Units

Running and Submitting a Learning Unit

In the batch6-students repository that you just cloned there is a sample learning unit. It's used to give instructors guidelines to produce the learning units. We are also using it to ensure that you are able to run and submit a learning unit.

So go ahead and copy the sample directory sample/SLU00 - LU Tutorial from the batch6-students repository to your repository (named batch6-workspace).

The grader only requires you to have the contents in a directory starting with the learning unit's ID, but we highly advise to keep the same directory structure as the students repository. All learning units are organized as:

<specialization ID> - <specialization name>/<learning unit ID> - <learnin unit name>

Doing so will help you keep organized and ease copying data from the students repository to yours.

Working on the Learning Unit

All learning units come as a set of Jupyter Notebooks (and some links to presentations). Notebooks are documents that can contain text, images and live code that you can run interactively.

In this section we will launch the Jupyter Notebook application. The application is accessed through the web browser.

Once you have the application open feel free to explore the sample learning unit structure. It will give you a handle on what to expect and what rules the instructors follow (and the effort they put) when creating a learning unit.

So let's start the Jupyter Notebook app:

1. Activate your virtual environment

   source ~/.virtualenvs/slu00/bin/activate

2. Enter the Learning unit directory in your workspace directory (batch6-workspace).

   Note: It is VERY IMPORTANT that you ALWAYS work on the files on your batch6-workspace repository, and NEVER work on files that are in your batch6-students repository!

   cd ~/projects/batch6-workspace/sample/"SLU00 - LU Tutorial"

3. Installing the necessary packages

   pip install -r requirements.txt

4. Run the jupyter notebook If you are running WLS on Windows 10 run the following:

   jupyter notebook --NotebookApp.use_redirect_file=False

else:

```bash
jupyter notebook
```

When you run the jupyter notebook command, you should see something similar to this in your terminal: Your browser should pop up with Jupyter open, however, if this does not happen, you can simply copy the link you see on your terminal (the one that contains localhost) and past it in your browser's address bar:

Note: If you see these scarry looking error messages, don't worry, you can just ignore them.

The Exercise Notebook

Make sure you open and go through the Learning Notebook first.

Every learning unit contains an exercise notebook with exercises you will work on. So let's have a look at the sample Learning Unit.

1. On the Jupyter Notebook UI in the browser open the exercise notebook
2. Follow the instructions provided in the notebook

Besides the exercises and the cells for you to write solutions you will see other cells with a series of assert statements. This is how we (and you) will determine if a solution is correct. If all assert statements pass, meaning you dont get an AssertionError or any other kind of exception, the solution is correct.

Once you've solved all of the notebook we recommend the following this simple checklist to avoid unexpected surprises.

1. Save the notebook (again)
2. Run "Restart & Run All"
3. At this point the notebook should have run without any failing assertions

If you want to submit your notebook before it is all the way done to check intermediate progress, feel free to.

If you are able to go through the entire process and get a passing grade on the sample LU you'll have a good understanding of the same flow that you'll use for all LUs throughout the academy.

Commit and Push

Now you have worked on the sample learning unit and you have some uncommitted changes. It's time to commit the changes, which just means adding them to your batch6-workspace repository history, and pushing this history to you remote on GitHub.

• Using the terminal commit and push the changes

git add .
git commit -m 'Testing the sample notebook'
git push

Grading

1. Go to the Portal and select the learning unit
2. Select "Grade"
3. After grading is complete you should have 20/20
4. If everything passes locally but the grader doesn't give you the excepted output head to out troubleshooting

Learning Unit Workflow

You will need to follow this workflow whenever new learning materials are released.

Learning units will be announced in the academy's #announcements channel. At this point they are available in the batch6-students repository. A new Learning Unit is released every Monday.

The steps you followed during the initial setup are exactly what you are going to be doing for each new Learning Unit. Here's a quick recap:

0. If you haven't, activate your virtual environment

   source ~/.virtualenvs/slu00/bin/activate

1. Once a new Learning Unit is available, pull the changes from the batch6-students repo:

   • enter the ~/projects/batch6-students/ using the cd command, then use the git pull command:

   cd ~/projects/batch6-students/
   git pull

2. Copy the Learning Unit to your batch6-workspace repo

   cp -r ~/projects/batch6-students/"<specialization ID> - <specialization name>"/"<learning unit ID> - <learnin unit name>" ~/projects/batch6-workspace/"<specialization ID> - <specialization name>"

   For example, for the S01 - Bootcamp and Binary Classification and SLU01 - Pandas 101, it would look like this:

   cp -r ~/projects/batch6-students/"S01 - Bootcamp and Binary Classification"/"SLU01 - Pandas 101" ~/projects/batch6-workspace/"S01 - Bootcamp and Binary Classification"

3. Create a new virtual environment for the Learning Unit you'll be working on.

   • To do this you will run the following command:

   python3.8 -m venv ~/.virtualenvs/<learning unit ID>

   • and you would replace the <learning unit ID> with the learning unit ID, such that for SLU01, for example, the command would be:

   python3.8 -m venv ~/.virtualenvs/slu01

4. Activate your virtual environment

   source ~/.virtualenvs/slu01/bin/activate

5. Install the python packages from requirements.txt for the specific Learning Unit (you must do this for each Learning Unit, and there are multiple Learning Units in a Specialization)

   pip install -r ~/projects/batch6-workspace/"<specialization ID> - <specialization name>"/"<learning unit ID> - <learnin unit name>"/requirements.txt

   For example, for the S01 - Bootcamp and Binary Classification and SLU01 - Pandas 101, it would look like this:

   pip install -r ~/projects/batch6-workspace/"S01 - Bootcamp and Binary Classification"/requirements.txt

6. Change to the batch6-workspace dir

   cd ~/projects/batch6-workspace

7. Open Jupyter Notebook

   jupyter notebook

8. Work

9. Once all tests pass or once you're happy, save your work, close the browser tab with the Jupyter Notebook, close the terminal and open a new terminal

10. Then commit the changes and push

    cd ~/projects/batch6-workspace
    git add .
    git commit -m "Worked on SLU01 exercises"
    git push

11. Profit

Updates to Learning Units

As much as we try and have processes in place to prevent errors and bugs in the learning units some make it through to you. If the problem is not in the exercise notebook you can just pull the new version from the students repo and replace the file. The problem is if the correction is in the exercise notebook, you can't just replace the file your work is there and you'll lose it!

When a new version of the exercise notebook is released (and announced) two things will happen. If you submit an old version of the notebook it will be flagged as out of date and not graded. You will have to merge the work you've already done into the new version of the notebook.

At the moment our suggestion to merge the changes is:

1. Rename the old version
2. Copy the new exercise notebook over
3. Open both and copy paste your solutions to the new notebook

We understand it's not ideal and are working on improving this workflow using nbdime. If you are comfortable installing Python packages you can try it out, but we offer no support for this at the moment.

Add your Slack ID to the Portal

In your Profile in the Portal, besides your GitHub Handle, you should add your SlackID. You can find information on how to find it following this link

Help

During the academy you will surely run into problems and have doubts about the material. We provide you with some different channels to ask for help.

Learning Unit

If you feel something is not clear enough or there is a bug in the learning material please follow these steps. Remember, there is no such thing as a dumb question, and by asking questions publicly you will help others!

If you have more conceptual questions about the materials or how to approach a problem you can also reach out to the instructors on slack. You can find the main contact for the learning unit in the Portal this instructor can help you out or redirect you to someone that is available at the moment.

Portal

Are you getting different results locally than what you are getting in the Portal? If so we will first ask to do a bit of troubleshooting.

1. Ensure that you have saved the changes in the notebook
2. Ensure that you have committed and pushed the changes
3. Ensure that you are not using packages that are not present in the original requirements.txt file (changes to this file or your local environment have no effect)
4. In the learning unit page in the Portal you are able to download the exercise notebook with the results of the grader by clicking your grade, have a look to figure out what went wrong. If none of these steps helped go ahead and open a support ticket for the portal here.

Is the Portal down or acting out in some unexpected way? Then please open a support ticket for the portal here.