This document will help you get started with the course homework assignments. Please read it carefully as it contains crucial information.
The course homework assignments are mandatory and a large part of the grade. They entail writing code in python using popular third-party machine-learning libraries and also theoretical questions.
The assignments are implemented in part on a platform called Jupyter notebooks. Jupyter is a widely-used tool in the machine-learning ecosystem which allows us to create interactive notebooks containing live code, equations and text. We’ll use jupyter notebooks to guide you through the assignments, explain concepts, test your solutions and visualize their outputs.
To install and manage all the necessary packages and dependencies for the
assignments, we use conda, a popular package-manager for
python. The homework assignments come with an
environment.yml file which
defines what third-party libraries we depend on. Conda will use this file to
create a virtual environment for you. This virtual environment includes python
and all other packages and tools we specified, separated from any preexisting
python installation you may have. Detailed installation instructions are below.
We will not support any other installation method other than the one
For working on the code itself, we recommend using PyCharm, however you can use any other editor or IDE if you prefer. You can obtain the professional version of PyCharm for free by using your Technion student email (see here).
Each assignment’s root directory contains the following files and folders:
cs236781: Python package containing course utilities and helper functions. You do not need to edit anything here.
Nis the assignment number: Python package containing the assignment code. All your solutions will be implemented here, including answers to questions.
Nis a number and
XYZis some name: A set of jupyter notebooks that contain the instructions that will guide you through the assignment. You do not need to edit these. However, you should write your name(s) at the beginning of
main.py: A script providing some utilities via a CLI. You’ll run it to create your submission after completing the assignment.
environment.yml: A file for
conda, specifying the third-party packages it should install into the virtual environment it creates. Note that every assignment could define a different environment.
For example, on linux you should do:
curl -fsSLO https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh bash Miniconda3-latest-Linux-x86_64.sh # Accept EULA # Install in default directory # Select yes for running conda init
On macOS it’s similar but with a different script URL
curl -fsSLO https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh bash Miniconda3-latest-MacOSX-x86_64.sh # Rest is the same
On Windows, download the installer and follow the instructions on the conda website. See also the Windows-specific notes below before you proceed.
Note that you can skip this step if you already have either
condaor the full Anaconda distribution installed.
- Recommended: configure
condato use strict channel priority (will speed up solving the environment) and to not automatically activate the default env (will force you to activate a specific env):
conda config --set channel_priority strict conda config --set auto_activate_base False
- Install mamba (a drop-in replacement
condawhich is much faster in solving the dependency requirements):
conda install -n base -c conda-forge mamba mamba init
mambato create a virtual environment for the assignment. From the assignment’s root directory, run
mamba env update -f environment.yml -n cs236781-hw
This will install all the necessary packages into a new virtual environment named
From here on you can use either
mamba(they have exactly the same CLI), but for this course generally
mambawill be much faster. You can even
Note for users with M1 macs (Apple Silicon): You’ll need to install the Intel versions of the packages (they’ll run fine via rosetta2). To do this, run
export CONDA_SUBDIR=osx-64before running the above
Activate the new environment by running e.g.
conda activate cs236781-hw
Note: Activating an environment simply means that the path to its python binaries (and packages) is placed at the beginning of your
$PATHshell variable. Therefore, running programs installed into the conda env (e.g.
python) will run the version from the env since it appears in the
$PATHbefore any other installed version.
To check what conda environments you have and which is active, run
conda env list
or, you can run
which pythonand you should see the python binary is in a subfolder of e.g.
You can find more useful info about conda environments here.
You should to do steps 1 (installing
conda) once, not for each assignment.
However, the third-party package dependencies (in the
environment.ymlfile) might slightly change from one assignment to the next, or in case an update is published. To make sure you have the correct versions, always install the environment again (step 4 above) from the assignment root directory every time a new assignment is published and then activate the environment with the assignment number.
Always make sure the correct environment is active! Each time you close and re-open your terminal, the environment will need to be activated again.
If you use PyCharm or any other IDE, you should configure the interpreter path of the IDE to the path of the
pythonexecutable within the conda env folder. For example, point the interpreter path to
~/miniconda3/envs/cs236781-hw/bin/python. This is under
Settings -> Project -> Project Interpreter.
You’ll need to install the conda env within your user folder on the course server. The installation procedure is exactly the same, just follow the instructions for linux.
Notes for Windows Users
On Windows, you can run these commands from the Anaconda Prompt program that is installed with miniconda. If you also add the
condainstallation to the Windows
PATHvariable, you can run these commands from the regular windows command prompt.
Also on Windows, you may need to install Microsoft’s Build Tools for Visual Studio before the conda environment. Make sure “C++ Build Tools” is selected during installation. This only needs to be done once.
Working on the assignment
Make sure that the active conda environment is
cs236781-hw (see above), and
This will start a jupyter lab
server and open your browser at the local server’s url. You can now start working.
Open the first notebook (
Part0) and follow the instructions.
Note that if you are familiar with and prefer the regular
jupyter notebook you
can use that instead of
Implementing your solution and answering questions
- The assignment is comprised of a set of notebooks and accompanying code packages.
- You only need to edit files in the code package corresponding to the
assignment number, e.g.
- The notebooks contain material you need to know, instructions about what to do and also code blocks that will test and visualize your implementations.
- Within the notebooks, anything you need to do is marked with a TODO beside it. It will explain what to implement and in which file.
- Within the assignment code package, all locations where you need to write code
are marked with a special marker (
YOUR CODE). Additionally, implementation guidelines, technical details and hints are in some cases provided in a comment above.
- Sometimes there are open questions to answer. Your answers should also be written within the assignment package, not within the notebook itself. The notebook will specify where to write each answer.
You should think of the code blocks in the notebooks as tests. They test your solutions and they will fail if something is wrong. As such, if you implement everything and the notebook runs without error, you can be confident about your solution.
You may edit any part of the code, not just the sections marked with
YOUR CODE. However, note that there is always a solution which requires editing only within these markers.
When we check your submission, we’ll run the original notebook files of the assignment, together with your submitted code (from the
hwN) package. Therefore, any changes you do to the notebook files (such as changing the tests) will not affect the results of our grading. If you rely on notebook modifications to pass the tests, the tests will fail when we grade your work and you will lose marks.
Please don’t put other files in the assignment directory. If you do, they will be added to your submission which is automatically generated from the contents of the assignment folder.
Always make sure the active conda env is
cs236781-hw. If you get strange errors or failing import statements, this is probably the reason. Note that if you close your terminal session you will need to re-activate since conda will use it’s default
Submitting the assignment
What you’ll submit:
- All notebooks, after running them clean from start to end, with all outputs present.
- An html file containing the merged content of all notebooks.
- The code package (
hwN), with all your solutions present.
You don’t need to do this manually; we provide you with a helper CLI program to run all the notebooks and combine them into a single file for submission.
Generating your submission file
To generate your submission, run (obviously with different id’s):
python main.py prepare-submission --id 123456789 --id 987654321
The above command will:
- Execute all the notebooks cleanly, from start to end, regenerating all outputs.
- Merge the notebook contents into a single html file.
- Create a zip file with all of the above and also with your code.
If there are errors when running your notebooks, it means there’s a problem with your solution or that you forgot to implement something.
Additionally, you can use the
--skip-run flag to skip running your notebooks
(and just merge them) in case you already ran everything and you’re sure that
all outputs are present:
python main.py prepare-submission --skip-run --id 123456789 --id 987654321
Note however that if some of the outputs are missing from your submission you’ll lose marks.
Note: The submission script must also be run from within the same
conda env as
the assignment. Don’t forget to activate the env before running the submission
Submitting a partial solution
If you are unable to solve the entire assignment and wish to submit a partial
solution you can create a submission with errors by adding an
flag, like so:
python main.py prepare-submission --allow-errors --id 123456789 --id 987654321
Uploading the solution
.zip file you generate should be uploaded using the assignments tab in the
Grades will also be reported there.
Only a submission generated by the course script is considered valid. Any other submissions, e.g. submitting only the notebooks or the code files will not be graded.