wang-bill / 6.1600-labs

Student lab assignments for MIT 6.1600

6.1600 Labs

Most 6.1600 labs require implementing several features of a photo-sharing system, the skeleton of which we provide for you in this repository.

The general structure of this system includes a client, which you control, and a server which you do not control---imagine that the client is an application on your phone that connects to a cloud provider that you do not trust. Your job in each of the labs will be to enforce certain security properties within this client-server relationship.

Code Overview

The system is structured as a client and a server that communicate via Remote Procedure Calls (RPCs) over HTTP. This code is split up into three main folders:

• client: Code that is exclusive to the client is stored here. This consists of only the client implementation itself, Client in client.py.
• server: Code that is exclusive to the server. This includes an interface defining the methods that a server should support as well as a reference server that follows the specification. Note, however, that there are no standards which a server your client interacts with will follow.
• common: Code that is shared between the client and server, such as types (including those that define the RPC interface) and cryptography functions.

In your submission, you may edit only the code in client/.

There is also an autograder for your code. This is contained in the ag/ folder.

Installation

The labs are all written in python and require no installation beyond cloning the git repo. When running commands with the Makefile, a python virtual environment called venv will automatically be created for you, and the packages listed in requirements.txt for the corresponding lab will be installed.

Dependencies

These labs depend on Python 3.10 or later. You can verify that your Python version is correct by running python3 --version, e.g.:

$ python3 --version
3.10.5

Testing

Functionality Tests

We have included some "doctests" to verify that our examples are correct. To run these tests for a lab, run make test, which will run Python doctests. When the lab is complete, they should all pass, but they are not used for grading.

Note that these tests are different from the tests used by the autograder, which will be used to grade your assignments.

Autograder

We will grade your lab using a set of tests contained in the ag/ directory. To run these tests, you can use make grade-lab0 for lab 0 and similar commands for the other labs.

Note that the autograder mocks out the HTTP interface (ag/common/mock_http.py and the link_client_server() calls you see in the doctests) to simplify testing. However, the client-server interactions should be exactly the same as if we were using an HTTP interface---please let us know if something seems wrong.

Manual Testing

We encourage you to run tests manually after making a change to verify that it does what you intend. To do this, you need two terminal windows---one for the server and one for the client.

• Server: run make run-server to start the server. It will listen on the URL that it displays (likely http://localhost:5000). If you want to use a server other than the reference server, you can change the default server in server/app.py to point to a different implementation.
• Client: you can run the client from an interactive python window, a script, or whatever else you like. Notice that the client takes a server_url parameter when creating a Client instance---this should be the URL on which the server said it was listening.

Assignments

You can find all the code required for each lab inside of its directory. For instance, the code for lab0 resides in lab0/.

You can find the tasks for the corresponding assignment by looking at the Markdown file associated with the lab number. The following files contain descriptions of the tasks for each lab:

• lab0

Make sure to keep your solutions private, as per the course collaboration policy. In particular, note that if you make a public fork of this repository on GitHub, any code you write there will also become public, so remember not to put your work into that fork.

Feedback & Contributions

This is still a new class, and we are always working to improve the labs. If you find things that are annoying or that make it hard for you to effectively complete the labs and learn from them, please let us know. If you'd like to open a PR, feel free, but it probably be worthwhile to check with us before doing so to discuss your improvement.