Repo containing material for the NYU class (Master of Engineering) I teach on NLP, ML Sys etc. For context on what the class is trying to achieve and, especially what is NOT, please refer to the introductory slides in the relevant folder.
This repo is a WIP: check back often for updates, documentation, new slides etc.
Last update: Fall 2021.
Different sub-projects may have different requirements, as specified in the requirements.txt files to be found in the various folders. We recommend using virtualenv to keep environments isolated, i.e. creating a new environment:
python3 -m venv venv
then activating it and installing the required dependencies:
source venv/bin/activate
pip install -r requirements.txt
The repo is organized by folder: each folder contains either resources - e.g. text corpora or slides - or Python programs, divided by type.
As far as ML is concerned, language-related topics are typically covered through notebooks, MLSys-related concepts are covered through Python scripts (not surprisingly!).
The folder contains some ready-made text files to experiment with some NLP techniques: these corpora are just examples, and everything can be pretty much run in the same fashion if you swap these files (and change the appropriate variables) with other textual data you like better.
This folder contains script covering MLSys concepts: how to organize a ML project, how to publish a model in the cloud etc.. In particular:
- serverless_101 contains a vanilla AWS Lambda endpoint computing explicitely the Y value of a regression model starting from an X input provided by the client.
- serverless_sagemaker contains an AWS Lambda endpoint which uses a Sagemaker internal endpoint to serve a scikit-model, previously trained (why two endpoints? Check the slides!).
- training: contains a sequence of scripts taking a program training a regression model and progressively refactoring to follow industry best-practices (i.e. using Metaflow!).
For more info on each of these topics, please see the slides and the sub-sections below.
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This folder contains Python notebooks that illustrate in Python concepts discussed during the lectures. Please note that notebooks are inherently "exploratory" in nature, so they are good for interactivity and speed but they are not always the right tool for rigorous coding.
Note: most of the dependencies are pretty standard, but some of the "exotic" ones are added with inline statements to make the notebook self-contained.
The folder contains slides discussed during the course: while they provide a guide and a general overview of the concepts, the discussions we have during lectures are very important to put the material in the right context. While numbers in the files follow the order of the lectures as they happened in class, after the first intro lecture the NLP and MLSys "curricula" relatively independent.
Note that, with time, links and references may become obsolete despite my best intentions!
This folder contains simple throw-away scripts useful to test specific tools, like for example logging experiments in a remote dashboard, connecting to the cloud, etc. Script-specific info are below.
The file comet_playground.py is a simple adaptation of Comet onboarding script for sklearn: if run correctly,
the Comet dashboard should start displaying experiments under the chosen project name.
Make sure to set COMET_API_KEY and MY_PROJECT_NAME as env variables before running the script.
TBC
For questions, feedback, comments, please drop me a message at: jacopo dot tagliabue at nyu.edu.