MNIST in Every Framework

Summary

I hire not the researcher who has trained 10000 models once, I hire the researcher who has trained 1 model 10000 times - Bruce Lee, probably

We implement and train a MNIST MLP classifier in increasingly unorthodox frameworks and languages. The goal is to practice coding in different styles and to learn new languages. Are you a deep learning researcher who wants to learn a new language? Has PhD life turned your brain into mush that can only understand 28*28 pixel handwritten characters? You've come to the right place!

	Done?	Run command:	Data:	Training:	Network:	Autograd:
PyTorch		python pytorch/main.py	torchvision	Custom	torch.nn	torch
PyTorch-Lightning		python lightning/main.py	torchvision	lightning	torch.nn	torch
TensorFlow		python tensorflow/main.py	tfds	Keras	Keras	tf
JAX		python haiku/main.py	tfds	Custom	haiku	JAX
NumPy		python numpy/main.py	Custom	Custom	Custom	Custom
Julia		julia julia/main.jl	Custom	Custom	Flux	Zygote
Rust		cargo run	Custom	?	?	?
Haskell		N/A
OCaml		N/A
C		N/A
Assembly?!		N/A

The table above shows the progress of each project. Making an MNIST classifier generally requires some code to do at least four things: load and read the files, define and manage network architecture and parameters, make a training loop and training steps, and compute gradients. I show roughly what each project does for each of these tasks in the table. You will notice that I have to do more custom stuff as I move down the list towards languages that are less popular for scientific computing and ML.

Installation and Running

Since we use lots of different languages and frameworks, this project would be a nightmare to install from scratch. Instead, we provide a Dockerfile for installing and running the project with minimal pain. The only dependency you need is a working Docker installation.

Prerequisites

Clone this repo:

git clone https://github.com/Charl-AI/MNIST-in-every-framework.git
cd MNIST-in-every-framework

Check that your Docker installation is working:

docker run hello-world

Option 1: Build and run the Docker image manually

First, build the image:

docker build -f .devcontainer/Dockerfile . -t mnist-in-every-framework

Next, run the desired project, replacing <run command> with the command to run for the project. The MNIST dataset will be downloaded automatically upon running the container. Outputs of the project will be logged to your local filesystem in the outputs/ directory so you can view the results.

docker run --rm -it -v $(pwd)/outputs:/app/outputs mnist-in-every-framework <run command>

Option 2: Use VSCode Remote Containers

If you use VSCode with the remote containers extension, you can install the dependencies and open a VSCode development environment for this project by simply running Remote Containers: Open folder in Container from the command palette. You can then run any project you want by pasting the run command into the integrated terminal.

Option 3: Codespaces!

(New as of Nov 2022: GitHub provides a free tier with 80 hours per month of compute.)

You can open this repo without any local installation in a cloud-hosted 'codespace'. The dependencies will be automatically installed on the remote cloud machine, which you can access through your browser, VSCode, or even PyCharm. Simply start the codespace using the button in the GitHub UI. This is the easiest way to try out this project.