rust-mlops-template

A work in progress to build out solutions in Rust for MLOPs. This repo is more of a cookbook style. For a more gentle step by step guide to MLOps with Rust, please see my lecture notes as a Rust MDBook here.

Demo Hitlist (Will Solve hopefully almost every day/weekly)

• Do an inline python example
• Train a model in PyTorch with CPU: https://github.com/LaurentMazare/tch-rs
• Train a model in PyTorch with GPU: https://github.com/LaurentMazare/tch-rs
• Serve out ONNX with a Rust web framework like Actix
• ONNX Command-Line Tool
• Simple async network example: (network discovery or chat system)
• Rust SQLite Example
• Rust AWS Lambda
• Simple Rust GUI
• Rust Whisper Tool with C++ Bindings
• Fast Keyword Extraction (NLP)
• Emit Random Mediterranean Meals via CLI
• Web Assembly Rust

Advanced Aspirational Demos

• Building a database in Rust
• Building a search engine in Rust
• Building a web server in Rust
• Building a batch processing systems in Rust
• Build a command-line chat system
• Build a locate clone
• Build a load-testing tool

Motivation

One of the key goals of this project is to determine workflows that do not involve the #jcpennys (Jupyter, Conda, Pandas, Numpy, Sklearn) stack for #mlops. In particular I am not a fan of the conda installation tool (it is superfluous as I demonstrate in the Python MLOps Template) vs containerized workflows that use the Python Standard Library (Docker + pip + virtualenv) and this is a good excuse to find other solutions outside of that stack. For example:

• Why not also find a more performant Data Frame library, faster speed, etc.
• Why not have a compiler?
• Why not have a simple packaging solution?
• Why not have a very fast computational speed?
• Why not be able to write both for the Linux Kernel and general purpose scripting?
• Why not see if there is a better solution than Python (which is essentially two languages scientific python and regular Python)?
• Python is one of the least green languages in terms of energy efficiency, but Rust is one of the best.

In The Beginning Was the Command-Line

What could #mlops and #datascience look like in 2023 without #jupyternotebook and "God Tools" as the center of the universe? It could be the command line. In the beginning, it was the command line, and it may be the best solution for this domain.

"What would the engineer say after you had explained your problem and enumerated all the dissatisfactions in your life? He would probably tell you that life is a very hard and complicated thing; that no interface can change that; that anyone who believes otherwise is a sucker; and that if you don't like having choices made for you, you should start making your own." -Neal Stephensen

Using Data (i.e. Data Science)

• StackOverflow https://survey.stackoverflow.co/2022/#section-most-loved-dreaded-and-wanted-programming-scripting-and-markup-languages[states that #rust is on 7th year as the most loved language 87% of developers want to continue developing](https://survey.stackoverflow.co/2022/#section-most-loved-dreaded-and-wanted-programming-scripting-and-markup-languages) in and ties with Python as the most wanted technology. Clearly there is traction.
• According to http://www.modulecounts.com/[Modulecounts] it looks like an exponential growth curve to Rust.

Getting Started

This repository is a GitHub Template and you can use it to create a new repository that uses GitHub Codespaces. It is pre-configured with Rust, Cargo and other useful extensions like GitHub Copilot.

Install and Setup

There are a few options:

• You can follow the Official Install Guide for Rust
• Create a repo with this template

Once you install you should check to see things work:

rustc --version

Other option is to run make rust-version which checks both the cargo and rust version. To run everything locally do: make all and this will format/lint/test all projects in this repository.

Rust CLI Tools Ecosystem

You can see there several tools which help you get things done in Rust:

rust-version:
	@echo "Rust command-line utility versions:"
	rustc --version 			#rust compiler
	cargo --version 			#rust package manager
	rustfmt --version			#rust code formatter
	rustup --version			#rust toolchain manager
	clippy-driver --version		#rust linter

Hello World Setup

This is an intentionally simple full end-to-end hello world example. I used some excellent ideas from @kyclark, author of the command-line-rust book from O'Reilly here. You can recreate on your own following these steps

Create a project directory

• cargo new hello

This creates a structure you can see with tree hello

hello/
├── Cargo.toml
└── src
    └── main.rs
1 directory, 2 files

The Cargo.toml file is where the project is configured, i.e. if you needed to add a dependency. The source code file has the following content in main.rs. It looks a lot like Python or any other modern language and this function prints a message.

fn main() {
    println!("Hello, world MLOPs!");
}

To run the project you cd into hello and run cargo run i.e. cd hello && cargo run. The output looks like the following:

@noahgift ➜ /workspaces/rust-mlops-template/hello (main ✗) $ cargo run
   Compiling hello v0.1.0 (/workspaces/rust-mlops-template/hello)
    Finished dev [unoptimized + debuginfo] target(s) in 0.36s
     Running `target/debug/hello`
Hello, world MLOPs!

To run without all of the noise: cargo run --quiet. To run the binary created ./target/debug/hello

Run with GitHub Actions

GitHub Actions uses a Makefile to simplify automation

name: Rust CI/CD Pipeline
on:
  push:
    branches: [ "main" ]
  pull_request:
    branches: [ "main" ]
env:
  CARGO_TERM_COLOR: always
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v1
    - uses: actions-rs/toolchain@v1
      with:
          toolchain: stable
          profile: minimal
          components: clippy, rustfmt
          override: true
    - name: update linux
      run: sudo apt update 
    - name: update Rust
      run: make install
    - name: Check Rust versions
      run: make rust-version
    - name: Format
      run: make format
    - name: Lint
      run: make lint
    - name: Test
      run: make test
    

To run everything locally do: make all.

Simple Marco-Polo Game

Change into MarcoPolo directory and run cargo run -- play --name Marco and you should see the following output:

Polo

First Big Project: Deduplication Command-Line Tool

I have written command-line deduplication tools in many languages so this is what I choose to build a substantial example. The general approach I use is as follows:

1. Walk the filesystem and create a checksum for each file
2. If the checksum matches an existing checksum, then mark it as a duplicate file

Getting Started

• Create new project: crate new dedupe
• Check latest clap version: https://crates.io/crates/clap and put this version in the Cargo.toml The file should look similar to this.

[package]
name = "dedupe"
version = "0.1.0"
edition = "2021"

[dependencies]
clap = "4.0.32"

[dev-dependencies]
assert_cmd = "2"

• Next up make a test directory: mkdir tests that is parallel to src and put a cli.rs inside
• touch a lib.rs file and use this for the logic then run cargo run
• Inside this project I also created a Makefile to easily do everything at once:

format:
	cargo fmt --quiet

lint:
	cargo clippy --quiet

test:
	cargo test --quiet

run:
	cargo run --quiet

all: format lint test run

Now as I build code, I can simply do: make all and get a high quality build.

Next, let's create some test files:

echo "foo" > /tmp/one.txt
echo "foo" > /tmp/two.txt
echo "bar" > /tmp/three.txt

The final version works: cargo run -- --path /tmp

@noahgift ➜ /workspaces/rust-mlops-template/dedupe (main ✗) $ cargo run -- --path /tmp
    Finished dev [unoptimized + debuginfo] target(s) in 0.03s
     Running `target/debug/dedupe --path /tmp`
Searching path: "/tmp"
Found 5 files
Found 1 duplicates
Duplicate files: ["/tmp/two.txt", "/tmp/one.txt"]

Next things to complete for dedupe (in another repo):

• Switch to subcommands and create a search and dedupe subcommand
• Add better testing with sample test files
• Figure out how to release packages for multiple OS versions in GitHub

More MLOps project ideas

• Query Hugging Face dataset cli
• Summarize News CLI
• Microservice Web Framework, trying actix to start, that has a calculator API
• Microservice Web Framework deploys pre-trained model
• Descriptive Statistics on a well known dataset using https://www.pola.rs/[Polars] inside a CLI
• Train a model with PyTorch (probably via bindings to Rust)

Actix Microservice

• Refer to Actix getting started guide
• cargo new calc && cd calc
• add dependency to Cargo.toml

[package]
name = "calc"
version = "0.1.0"
edition = "2021"

# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html

[dependencies]
actix-web = "4"

• create a src/lib.rs and place inside

//calculator functions

//Add two numbers
pub fn add(a: i32, b: i32) -> i32 {
    a + b
}

//Subtract two numbers
pub fn subtract(a: i32, b: i32) -> i32 {
    a - b
}

//Multiply two numbers
pub fn multiply(a: i32, b: i32) -> i32 {
    a * b
}

//Divide two numbers
pub fn divide(a: i32, b: i32) -> i32 {
    a / b
}

In the main.rs put the following:

//Calculator Microservice
use actix_web::{get, web, App, HttpResponse, HttpServer, Responder};

#[get("/")]
async fn index() -> impl Responder {
    HttpResponse::Ok().body("This is a calculator microservice")
}

//library add route using lib.rs
#[get("/add/{a}/{b}")]
async fn add(info: web::Path<(i32, i32)>) -> impl Responder {
    let result = calc::add(info.0, info.1);
    HttpResponse::Ok().body(result.to_string())
}

//library subtract route using lib.rs
#[get("/subtract/{a}/{b}")]
async fn subtract(info: web::Path<(i32, i32)>) -> impl Responder {
    let result = calc::subtract(info.0, info.1);
    HttpResponse::Ok().body(result.to_string())
}

//library multiply route using lib.rs
#[get("/multiply/{a}/{b}")]
async fn multiply(info: web::Path<(i32, i32)>) -> impl Responder {
    let result = calc::multiply(info.0, info.1);
    HttpResponse::Ok().body(result.to_string())
}

//library divide route using lib.rs
#[get("/divide/{a}/{b}")]
async fn divide(info: web::Path<(i32, i32)>) -> impl Responder {
    let result = calc::divide(info.0, info.1);
    HttpResponse::Ok().body(result.to_string())
}

//run it
#[actix_web::main]
async fn main() -> std::io::Result<()> {
    HttpServer::new(|| {
        App::new()
            .service(index)
            .service(add)
            .service(subtract)
            .service(multiply)
            .service(divide)
    })
    .bind(("127.0.0.1", 8080))?
    .run()
    .await
}

Next, use a Makefile to ensure a simple workflow

format:
	cargo fmt --quiet

lint:
	cargo clippy --quiet

test:
	cargo test --quiet

run:
	cargo run 

all: format lint test run

Run make all then test out the route by adding two numbers at /add/2/2

Hugging Face Example

• Uses rust-bert crate
• Create new project cargo new hfdemo and cd into it: cd hfdemo
• Create a new library file: touch src/lib.rs
• Add packages to Cargo.toml

[package]
name = "hfdemo"
version = "0.1.0"
edition = "2021"

[dependencies]
rust-bert = "0.19.0"
clap = {version="4.0.32", features=["derive"]}
wikipedia = "0.3.4"

The library code is in lib.rs and the subcommands from clap live in main.rs. Here is the tool in action:

@noahgift ➜ /workspaces/rust-mlops-template/hfdemo (main ✗) $ cargo run sumwiki --page argentina
    Finished dev [unoptimized + debuginfo] target(s) in 4.59s
     Running `target/debug/hfdemo sumwiki --page argentina`
Argentina is a country in the southern half of South America. It covers an area of 2,780,400 km2 (1,073,500 sq mi), making it the second-largest country in South America after Brazil. It is also the fourth-largest nation in the Americas and the eighth-largest in the world.

Hugging Face Q/A Example

cd into hfqa and run cargo run

```bash
cargo run --quiet -- answer --question "What is the best book from 1880 to read?" --context "The Adventures of Huckleberry Finn was released in 1880"
Answer: The Adventures of Huckleberry Finn

Hugging Face Lyrics Analysis using Zero Shot Classification with SQLite

• Listen to Maná - En El Muelle De San Blás

@noahgift ➜ /workspaces/rust-mlops-template/sqlite-hf (main ✗) $ cargo run --quiet -- classify
Classify lyrics.txt
rock: 0.06948944181203842
pop: 0.27735018730163574
hip hop: 0.034089818596839905
country: 0.7835917472839355
latin: 0.6906086802482605

Print the lyrics:

cargo run --quiet -- lyrics | less | head

Lyrics lyrics.txt
Uh-uh-uh-uh, uh-uh
Ella despidió a su amor
El partió en un barco en el muelle de San Blas
El juró que volvería
Y empapada en llanto, ella juró que esperaría
Miles de lunas pasaron
Y siempre ella estaba en el muelle, esperando
Muchas tardes se anidaron
Se anidaron en su pelo y en sus labios

Hugging Face GPU Translation CLI

Full working example here: https://github.com/nogibjj/rust-pytorch-gpu-template/tree/main/translate

Goal: Translate a spanish song to english

• cargo new translate and cd into it fully working GPU Hugging Face Translation CLI in Rust

run it: time cargo run -- translate --path lyrics.txt

/*A library that uses Hugging Face to Translate Text
*/
use rust_bert::pipelines::translation::{Language, TranslationModelBuilder};
use std::fs::File;
use std::io::Read;

//build a function that reads a file and returns a string
pub fn read_file(path: String) -> anyhow::Result<String> {
    let mut file = File::open(path)?;
    let mut contents = String::new();
    file.read_to_string(&mut contents)?;
    Ok(contents)
}

//build a function that reads a file and returns an array of the lines of the file
pub fn read_file_array(path: String) -> anyhow::Result<Vec<String>> {
    let mut file = File::open(path)?;
    let mut contents = String::new();
    file.read_to_string(&mut contents)?;
    let array = contents.lines().map(|s| s.to_string()).collect();
    Ok(array)
}


//build a function that reads a file and translates it
pub fn translate_file(path: String) -> anyhow::Result<()> {
    let model = TranslationModelBuilder::new()
        .with_source_languages(vec![Language::Spanish])
        .with_target_languages(vec![Language::English])
        .create_model()?;
    let text = read_file_array(path)?;
    //pass in the text to the model
    let output = model.translate(&text, None, Language::English)?;
    for sentence in output {
        println!("{}", sentence);
    }
    Ok(())
}

Polars Example

• Example here

• cd into polarsdf and run cargo run

cargo run -- sort --rows 10

You can see an example of how Polars can be used to sort a dataframe in a Rust cli program.

Parallelism

One of the outstanding features of Rust is safe, yet easy paralielism. This project demos parallelism by benchmarking a checksum of several files.

We can see how trivial it is to speed up a program with threads:

Here is the function for the serial version:

// Create a checksum of each file and store in a HashMap if the checksum already exists, add the file to the vector of files with that checksum
pub fn checksum(files: Vec<String>) -> Result<HashMap<String, Vec<String>>, Box<dyn Error>> {
    let mut checksums = HashMap::new();
    for file in files {
        let checksum = md5::compute(std::fs::read(&file)?);
        let checksum = format!("{:x}", checksum);
        checksums
            .entry(checksum)
            .or_insert_with(Vec::new)
            .push(file);
    }
    Ok(checksums)
}

cargo --quiet run -- serial

➜  parallel git:(main) ✗ time cargo --quiet run -- serial
Serial version of the program
d41d8cd98f00b204e9800998ecf8427e:
        src/data/subdir/not_utils_four-score.m4a
        src/data/not_utils_four-score.m4a
b39d1840d7beacfece35d9b45652eee1:
        src/data/utils_four-score3.m4a
        src/data/utils_four-score2.m4a
        src/data/subdir/utils_four-score3.m4a
        src/data/subdir/utils_four-score2.m4a
        src/data/subdir/utils_four-score5.m4a
        src/data/subdir/utils_four-score4.m4a
        src/data/subdir/utils_four-score.m4a
        src/data/utils_four-score5.m4a
        src/data/utils_four-score4.m4a
        src/data/utils_four-score.m4a
cargo --quiet run -- serial  0.57s user 0.02s system 81% cpu 0.729 total

vs threads

time cargo --quiet run -- parallel
Parallel version of the program
d41d8cd98f00b204e9800998ecf8427e:
        src/data/subdir/not_utils_four-score.m4a
        src/data/not_utils_four-score.m4a
b39d1840d7beacfece35d9b45652eee1:
        src/data/utils_four-score5.m4a
        src/data/subdir/utils_four-score3.m4a
        src/data/utils_four-score3.m4a
        src/data/utils_four-score.m4a
        src/data/subdir/utils_four-score.m4a
        src/data/subdir/utils_four-score2.m4a
        src/data/utils_four-score4.m4a
        src/data/utils_four-score2.m4a
        src/data/subdir/utils_four-score4.m4a
        src/data/subdir/utils_four-score5.m4a
cargo --quiet run -- parallel  0.65s user 0.04s system 262% cpu 0.262 total

Ok, so let's look at the code:

// Parallel version of checksum using rayon with a mutex to ensure
//that the HashMap is not accessed by multiple threads at the same time
pub fn checksum_par(files: Vec<String>) -> Result<HashMap<String, Vec<String>>, Box<dyn Error>> {
    let checksums = std::sync::Mutex::new(HashMap::new());
    files.par_iter().for_each(|file| {
        let checksum = md5::compute(std::fs::read(file).unwrap());
        let checksum = format!("{:x}", checksum);
        checksums
            .lock()
            .unwrap()
            .entry(checksum)
            .or_insert_with(Vec::new)
            .push(file.to_string());
    });
    Ok(checksums.into_inner().unwrap())
}

The main takeaway is that we use a mutex to ensure that the HashMap is not accessed by multiple threads at the same time. This is a very common pattern in Rust.

Logging in Rust Example

cd into clilog and type: cargo run -- --level TRACE

//function returns a random fruit and logs it to the console
pub fn random_fruit() -> String {
    //randomly select a fruit
    let fruit = FRUITS[rand::thread_rng().gen_range(0..5)];
    //log the fruit
    log::info!("fruit-info: {}", fruit);
    log::trace!("fruit-trace: {}", fruit);
    log::warn!("fruit-warn: {}", fruit);
    fruit.to_string()
}

AWS

Rust AWS S3 Bucket Metadata Information

• You can get it here

Running an optimized version was able to sum all the objects in my AWS Account about 1 second: ./target/release/awsmetas3 account-size

Rust AWS Lambda

cd into rust-aws-lambda

• Rust AWS Lambda docs
• Install AWS VS Code plugin and configure it to use your AWS account.
• See GitHub repo here: https://github.com/awslabs/aws-lambda-rust-runtime#deployment

To deploy: make deploy which runs: cargo lambda build --release

• Test inside of AWS Lambda console
• Test locally with:

cargo lambda invoke --remote \
  --data-ascii '{"command": "hi"}' \
  --output-format json \
  rust-aws-lambda

Result:

cargo lambda invoke --remote \
                --data-ascii '{"command": "hi"}' \
                --output-format json \
                rust-aws-lambda
{
  "msg": "Command hi executed.",
  "req_id": "1f70aff9-dc65-47be-977b-4b81bf83e7a7"
}

Client-Server Example

Example lives here: https://github.com/noahgift/rust-mlops-template/tree/main/rrgame

Current Status

• Client server echo working

cargo run -- client --message "hi" cargo run -- server

A bigger example lives here: https://github.com/noahgift/rust-multiplayer-roulette-game

Containerized Rust Applications

• Working Containerized Rust CLI Example

FROM rust:latest as builder
ENV APP containerized_marco_polo_cli
WORKDIR /usr/src/$APP
COPY . .
RUN cargo install --path .
 
FROM debian:buster-slim
RUN apt-get update && rm -rf /var/lib/apt/lists/*
COPY --from=builder /usr/local/cargo/bin/$APP /usr/local/bin/$APP
ENTRYPOINT [ "/usr/local/bin/containerized_marco_polo_cli" ]

Containerized PyTorch Rust

cd into: pytorch-rust-docker

Here is the Dockerfile

FROM rust:latest as builder
ENV APP pytorch-rust-docker
WORKDIR /usr/src/$APP
COPY . .
RUN apt-get update && rm -rf /var/lib/apt/lists/*
RUN cargo install --path .
RUN cargo build -j 6

• docker build -t pytorch-rust-docker .
• docker run -it pytorch-rust-docker
• Next inside the container run: cargo run -- resnet18.ot Walking_tiger_female.jpg

Tensorflow Rust Bindings

• See tf-rust-example

/*Rust Tensorflow Hello World */

extern crate tensorflow;
use tensorflow::Tensor;

fn main() {
    let mut x = Tensor::new(&[1]);
    x[0] = 2i32;
    //print the value of x
    println!("{:?}", x[0]);
    //print the shape of x
    println!("{:?}", x.shape());
    //create a multidimensional tensor
    let mut y = Tensor::new(&[2, 2]);
    y[0] = 1i32;
    y[1] = 2i32;
    y[2] = 3i32;
    y[3] = 4i32;
    //print the value of y
    println!("{:?}", y[0]);
    //print the shape of y
    println!("{:?}", y.shape());
}

PyTorch

Pre-trained model: cd into pytorch-rust-example then run: cargo run -- resnet18.ot Walking_tiger_female.jpg

PyTorch Binary with embedded pre-trained model

Using included model in binary. See this issue about including PyTorch with binary

Status: Works, but binary cannot pickup PyTorch, so still investigating solutions.

@noahgift ➜ /workspaces/rust-mlops-template/pytorch-binary-cli (main ✗) $ cargo run -- predict --image Walking_tiger_female.jpg 
    Finished dev [unoptimized + debuginfo] target(s) in 0.09s
     Running `target/debug/pytorch-binary-cli predict --image Walking_tiger_female.jpg`
Current working directory: /workspaces/rust-mlops-template/pytorch-binary-cli
Model path: ../model/resnet18.ot
Model size: 46831783
tiger, Panthera tigris                             90.42%
tiger cat                                           9.19%
zebra                                               0.21%
jaguar, panther, Panthera onca, Felis onca          0.07%
tabby, tabby cat                                    0.03%

Web Assembly in Rust

Cd into hello-wasm-bindgen and run make install the make serve

You should see something like this:

/* hello world Rust webassembly*/
use wasm_bindgen::prelude::*;

#[wasm_bindgen]
extern "C" {
    fn alert(s: &str);
}

//export the function to javascript
#[wasm_bindgen]
pub fn marco_polo(s: &str) {
    //if the string is "Marco" return "Polo"
    if s == "Marco" {
        alert("Polo");
    }
    //if the string is anything else return "Not Marco"
    else {
        alert("Not Marco");
    }
}

Kmeans Example

cd into linfa-kmeans and run cargo run -- cluster

Lasso Regression CLI

@noahgift ➜ /workspaces/rust-mlops-template/regression-cli (main ✗) $ cargo run -- train --ratio .9
    Finished dev [unoptimized + debuginfo] target(s) in 0.05s
     Running `target/debug/regression-cli train --ratio .9`
Training ratio: 0.9
intercept:  152.1586901763224
params: [0, -0, 503.58067499818077, 167.75801599203626, -0, -0, -121.6828192430516, 0, 427.9593531331433, 6.412796328606638]
z score: Ok([0.0, -0.0, 6.5939908998261245, 2.2719123245079786, -0.0, -0.0, -0.5183690897253823, 0.0, 2.2777581181031765, 0.0858408096568952], shape=[10], strides=[1], layout=CFcf (0xf), const ndim=1)
predicted variance: -0.014761955865436382

Transcription with Whisper in Rust

• Based on this https://github.com/ggerganov/whisper.cpp[CPP version]
• Rust bindings here: https://github.com/tazz4843/whisper-rs

Rust PyTorch Saturating GPU

• Example repo here: https://github.com/nogibjj/rust-pytorch-gpu-template/blob/main/README.md#pytorch-rust-gpu-example

Rust PyTorch MNIST Saturating GPU

• Example repo here: https://github.com/nogibjj/rust-pytorch-gpu-template/blob/main/README.md#mnist-convolutional-neural-network

Rayon Multi-threaded GPU Stress Test CLI

Stress Test CLI for both CPU and GPU PyTorch using Clap

• cargo new stress cd into stress
• To test CPU for PyTorch do: cargo run -- cpu
• To test GPU for PyTorch do: cargo run -- gpu
• To monitor CPU/Memory run htop
• To monitor GPU run nvidia-smi -l 1
• To use threaded GPU load test use: cargo run -- tgpu

Full working example here: https://github.com/nogibjj/rust-pytorch-gpu-template/tree/main/stress

Rust Stable Diffusion Demo

You can create it this repo for more info: https://github.com/nogibjj/rust-pytorch-gpu-template#stable-diffusion-demo

• clone this repo: https://github.com/LaurentMazare/diffusers-rs
• Follow these setup instructions: https://github.com/LaurentMazare/diffusers-rs#clip-encoding-weights

After all the weights are downloaded run:

cargo run --example stable-diffusion --features clap -- --prompt "A very rusty robot holding a fire torch to notebooks"

Stable Diffusion 2.1 Pegging GPU

Rusty Robot Torching Notebooks

Randomly Select Rust Crates To Work On

cd into rust-ideas

cargo run -- --help cargo run -- popular --number 4 cargo run -- random

@noahgift ➜ /workspaces/rust-mlops-template/rust-ideas (main ✗) $ cargo run -- random
    Finished dev [unoptimized + debuginfo] target(s) in 0.09s
     Running `target/debug/rust-ideas random`
Random crate: "libc"

ONNX Example

cd into OnnxDemo and run make install then cargo run -- infer which invokes a squeezenet model.

Sonos ONNX

Verified this works and is able to invoke runtime in a portable binary: https://github.com/sonos/tract/tree/main/examples/pytorch-resnet

OpenAI

Switching to Rust API Example

Full working example link: https://github.com/nogibjj/assimilate-openai/tree/main/openai

• install Rust via Rustup: curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh Use Rust API for OpenAI (3rd party): https://github.com/deontologician/openai-api-rust

• Create new project: cargo new openai and cd into it

• make format then make lint then cargo run

Working Example:

(.venv) @noahgift ➜ /workspaces/assimilate-openai/openai (main) $ cargo run -- complete -t "The rain in spain"
    Finished dev [unoptimized + debuginfo] target(s) in 0.14s
     Running `target/debug/openai complete -t 'The rain in spain'`
Completing: The rain in spain
Loves gets you nowhere
The rain in spain

lib.rs

/*This uses Open AI to Complete Sentences */

//accets the prompt and returns the completion
pub async fn complete_prompt(prompt: &str) -> Result<String, Box<dyn std::error::Error>> {
    let api_token = std::env::var("OPENAI_API_KEY")?;
    let client = openai_api::Client::new(&api_token);
    let prompt = String::from(prompt);
    let result = client.complete_prompt(prompt.as_str()).await?;
    Ok(result.choices[0].text.clone())
}

Command-line Data Science with Rust (Action Items)

1. go into dscli
2. Figure the way to make Polars work with linfa
3. How can I make a kmeans cluster using Polars

Containerized Actix Continuous Delivery to AWS App Runner

1. cd into webdocker
2. build and run container (can do via Makefile) or

docker build -t fruit . docker run -it --rm -p 8080:8080 fruit

3. push to ECR
4. Tell AWS App Runner to autodeploy

Mixing Python and Rust

Using Rust Module from Python

• Pyo3 Try the getting started guide:

# (replace string_sum with the desired package name)
$ mkdir string_sum
$ cd string_sum
$ python -m venv .env
$ source .env/bin/activate
$ pip install maturin

• Run maturin init and then run maturin develop or make develop
• python
• Run the following python code

import string_sum
string_sum.sum_as_string(5, 20)

The output should look like this: '25'

Using Python from Rust

Follow guide here: https://pyo3.rs/v0.18.0/

• install sudo apt-get install python3-dev
• cargo new pyrust and cd pyrust
• tweak Cargo.toml and add pyo3
• add source code to main.rs
• make run

Hello vscode, I'm Python 3.9.2 (default, Feb 28 2021, 17:03:44) 
[GCC 10.2.1 20210110]

Q: Does the target binary have Python included? A: Maybe. It does appear to be able to run Python if you go to the target /workspaces/rust-mlops-template/pyrust/target/debug/pyrust

Follow up question, can I bring this binary to a "blank" codespace with no Python and what happens!

Day2: Using Rust with Python

Goal: Build a high-performance Rust module and then wrap in a Python command-line tool

Containerized Rust Examples

• cargo new tyrscontainer and cd into tyrscontainer
• copy a Makefile and Dockerfile from webdocker

Note that the rust build system container which is ~1GB is NOT in the final container image which is only 98MB.

FROM rust:latest as builder
ENV APP tyrscontainer
WORKDIR /usr/src/$APP
COPY . .
RUN cargo install --path .
 
FROM debian:buster-slim
RUN apt-get update && rm -rf /var/lib/apt/lists/*
COPY --from=builder /usr/local/cargo/bin/$APP /usr/local/bin/$APP
#export this actix web service to port 8080 and 0.0.0.0
EXPOSE 8080
CMD ["tyrscontainer"]

The final container is very small i.e. 94MB

strings               latest    974d998c9c63   9 seconds ago   94.8MB

The end result is that you can easily test this web service and push to a cloud vendor like AWS and AWS App Runner.

Open AI Raw HTTP Request Example

Code here: https://github.com/nogibjj/assimilate-openai/tree/main/rust-curl-openai

(.venv) @noahgift ➜ /workspaces/assimilate-openai/rust-curl-openai (main) $ cargo run
   Compiling reqwest v0.11.14
   Compiling rust-curl-openai v0.1.0 (/workspaces/assimilate-openai/rust-curl-openai)
    Finished dev [unoptimized + debuginfo] target(s) in 4.78s
     Running `target/debug/rust-curl-openai`
{"id":"cmpl-6rDd8mzOtMx7kKobqV0isiC7TkqU4","object":"text_completion","created":1678141798,"model":"text-davinci-003","choices":[{"text":"\n\nJupiter is the fifth planet from the Sun and the biggest one in our Solar System. It is very bright and can be seen in the night sky. It is named after the Roman god Jupiter. It is usually the third brightest thing you can see in the night sky after the Moon and Venus.","index":0,"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":151,"completion_tokens":62,"total_tokens":213}}

GCP Cloud Run

• Verified GCP Cloud Run works, code here: https://github.com/nogibjj/rust-mlops-template/tree/main/webdocker

Jupyter Notebook and Rust

• evcxr

First we need to compile: cargo install evcxr_jupyter Next, lets do this: evcxr_jupyter --install tldr; it does work! but you must do the following: jupyter notebook --generate-config and then edit cross origin.

to run plotting tutorial do the following:

git clone https://github.com/38/plotters-doc-data

ONNX Series

• Next session try to build a GPT 2 CLI from ORT: https://github.com/pykeio/ort

Working PyTorch + Actix (looking into Distroless as well)

• pytorch image service with actix

References

• Notes for Rust Docker Container

Build System

This build system is a bit unique because it recursives many Rust repos and tests them all!

Language References and Tutorials

• Comprehensive Rust Course Google
• Rust Async Book
• 52 Weeks of Rust
• Command-Line Rust Book
• Command-Line Rust Book Source Code
• awesome rust
• Microsoft Learn Rust
• Rust Machine Learning Book
• CLI Tools

End to End Examples

• Rust micro-server for Text Summarization deployed via Kubernetes and Using Hugging Face Bindings

MLOps/ML Engineering and Data Science

• best-of-ml-rust
• Awesome-Rust-MachineLearning

Rust MLOps Platforms

• wallaroo.ai community install

Cloud Computing

AWS

• Sustainability with Rust
• Rust AWS Lambda
• EFS + Lambda (Great Use Case for Rust
• Rust uses less than half memory of Python AWS Lambda and 1.5 less duration in CPU

Azure

• Azure Rust Functions

Linux Kernel

• Rust makes way to Linux Kernel

Systems Tools

• An extended deduplication example command-line tool

Deep Learning

• Rust bindings for the C++ api of PyTorch
• Rust Pytorch example
• Rust PyTorch with Actix-Web Example

Search Engines

• GitHub Search

Web Microservices and Serverless

• Docker + Actix
• Actix
• AWS Lambda Rust
• Rust AWS Lambda Blog Post

Data Frames

• Polars. You can see an example here.

Authoring Tools

One goal is to reduce using Notebooks in favor of lightweight markdown tools (i.e. the goal is MLOps vs interactive notebooks)

• mdBook
• Quarto
• Jupyter Rust

Computer Vision

• Visualizing real world computer vision, powered by Rust

Linux Tools

• Great example of a cross-platform tool in Rust
• List of Popular Rust based command-line tools

Python and Rust integration

• Pyo3 rust binding for Python
• Inline Python in Rust
• Red Hat: Speed up Python using Rust
• Ruff 12-120X faster linting of Python code
• Convert Python model to Rust TensorFlow
• Hugging Face home grown inference in Rust

GUI

• egui
• eframe
• iced

NLP

• Rake keyword extraction

Onnx

• onnxruntime for Rust
• Build standalone executables with Rust and ONNX
• Hugging Face discussion on Rust
• ort - ONNX Runtime Rust bindings

Static Web

• Zola

Pure Rust Machine Learning

• linfa ML (a bit like sklearn)

Benchmarking

• Python vs Rust https://able.bio/haixuanTao/deep-learning-in-rust-with-gpu--26c53a7f
• Rust is 150x (15,000%) faster, and uses about the same amount of memory compared with Python.
• Rust 26X faster than Python sklearn
• 150+ million users with Rust MLOPs
• all language benchmarks
• polars benchmark vs pandas and dask
• Python vs Rust in AWS Lambda for a Data Guy
• Rust vs Python in AWS Lambda

Delta Lake

• Delta Lake Rust
• Saving 10X or more using Rust versus Spark talk

Testing Tools

• Fuzz Testing Rust
• Code Coverage Tool

Containerized Rust

• Building containerized applications with Rust
• Web Assembly + Rust containerization

Embedded Rust

• Awesome Embedded Rust

ZSH

• Rust ZSH modules

Time Series Rust

• influxdb-rust
• Time Series Polars Rust

Linux and GCC

• GNU GCC and Rust

GTP4 Code Search

• Bloop AI-GTP4 Code Search

C++ vs Rust

• C++ vs Rust

OpenAI

• OpenAI Rust Example

Popularity

• rust worlds fastest growing language

Copilots effect on Programming

• End of programming? I believe instead of end of coding it means level up

Rewrite Python to Rust

• Oxidizing bmap-tools: rewriting a Python project in Rust
• Oxidizing

Training LLMs from Scratch

• replit llm training