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Homework 1 Instructions Sagie Benaim and Tomer Galanti March 13, 2019 The homework assignment is to be done in groups of up to 3 students. Submission deadline is 4th of April 2019. For the current assignment there is no need for GPU usage and so we’ll not use the google cloud VM. Download the zip file provided (assignment.zip) and unzip it 1 . 1 Setup Follow the following setup instructions: 1.1 Jupyter As for the Numpy tutorial, we’ll use Jupyter. Jupyter is a widely-used tool in the machine-learning ecosystem which allows us to create interactive notebooks containing live code, equations and text. If you didn’t already do so, download it here: https://jupyter.org/install. 1.2 Conda Next, install Conda, a popular package manager for Python: Download from https://conda.io/miniconda.html (for Python 3) and follow instruction on: https://conda.io/docs/user-guide/install/index.html. 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. After installing conda, create your environment called hw1 env, by typing from the hw1 folder: conda env c r e a t e −f environment . yml This will install all necessary third party packages in the environment hw1 env. Next, activate the environment by typing: 1 This homework assignment is an adaptation of the one from the cs236605 course: https: //vistalab-technion.github.io/cs236605/ 1 conda a c t i v a t e hw1 env Activating an environment simply means that the path to it’s python binaries (and packages) is placed at the beginning of your $PATH shell variable. Therefore, running programs installed into the conda env (e.g. python) will run the version from the env since it appears in the $PATH before any other installed version. After you finish the homework, deactivate the environment by typing: conda d e a c t i v a t e You can read more about conda here: https://conda.io/projects/conda/ en/latest/user-guide/tasks/manage-environments.html 1.3 Pycharm For writing the actual code, it is recommended to use PyCharm, though you can use any other IDE or software you wish. Using your university email, you can download a professional version for free here: https://www.jetbrains. com/student/. 2 Assignment 2.1 Folder Structure After unzipping assignment.zip, you’ll have an assignment folder which consists of the following: 1. hw1 folder: Python package containing the assignment code. All your solutions will be implemented here. 2. tests folder: A package containing tests that run all the assignment notebooks and fail if there are errors. 3. PartN XYZ.ipynb where N is a number and XYZ is some name: A set of jupyter notebooks that contain the instructions that will guide you through the assignment. You won’t need to edit these except if you wish to play around and to write your name at the beginning. 4. main.py: A script providing some utilities via a CLI. Mainly, you’ll run it to create your submission after completing the assignment. 5. environment.yml: A file for conda, as described above. 2.2 Working with the Assingment After activating conda (see above), the from the assignment folder, type: jupyter lab 2 Follow the notebooks instructions statring from notebook 0. You are only required to change the code in the folder hw1. 1. The notebooks contain material you need to know, instructions about what to do and also code blocks that will test and visualize your implementations. 2. 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. 3. 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. 4. 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. Notes: 1. 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. 2. 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. 3. Always make sure the active conda env is hw1 env. If you get strange errors or broken 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 base environment. 2.3 Submitting your work To generate your submission, run: python main . py p r e p a r e −s u b m i s s i o n −−i d ID1 −−i d ID2 −−i d ID3 where ID1, ID2 and ID3 are your id numbers. 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 p r e p a r e −s u b m i s s i o n −−s k i p −run −−i d . . . 3 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 allow-errors flag, like so: python main . py p r e p a r e −s u b m i s s i o n −−a l l o w −e r r o r s −−i d . . . Finally zip the output and submit it on Moodle. 4
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