How Could We Power Python Using Rust and Go ?

17 octobre 2024

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How Could We Power Python Using Rust and Go ?

Where do we start? What is our question?

If we want to use python to do your AI App and we know how to code in python but want performance. Can you use Rust PyO3 and Go language at the same time for their advantages in speed and concurrency management?

Then, which part should be delegated to Rust and which other part should be delegated to Go?

1. Python as the Main Language for AI Logic

Python will remain the core language where you'll develop the AI logic. Python is widely used for machine learning due to its extensive libraries and ecosystem like TensorFlow, PyTorch, and scikit-learn. However, it is relatively slow for CPU-bound tasks, so Rust and Go can be introduced to handle performance-critical sections.

2. Rust for Performance-Critical Parts

Rust can be used to write CPU-intensive operations where you need low-level control, speed, and memory safety. For instance:

Numerical computations
Matrix operations
Large data manipulations
Serialization/Deserialization processes
Anything where low-level optimizations or memory handling is necessary

Use PyO3 to create Rust extensions for Python. You can call Rust functions from Python and handle tasks like matrix multiplications, optimizations, or direct hardware interaction in Rust.

3. Go for Concurrency

Go excels at handling concurrent and parallel operations, making it ideal for:

Managing server-side tasks
Handling multiple requests (if your app is distributed)
High-performance networking
Tasks that involve multiple threads or processes

We could probably use gRPC or RESTful APIs between Go and Python. Go will handle concurrent tasks while sending results back to Python.

Scenario Example: AI App with Heavy Matrix Operations and High-Volume API Requests

Python is used for model inference.
Rust is used to speed up matrix operations and other CPU-bound parts of the code.
Go handles incoming HTTP requests (from users) and parallel processing of tasks like logging, managing API calls, etc.

Step 1: Rust Module for Heavy Computation

Create a simple Rust function using PyO3 for a matrix multiplication task.

Rust Code (lib.rs):

"""

use pyo3::prelude::*;
use numpy::{PyArray1, PyArray2};
use ndarray::Array2;

/// Perform matrix multiplication in Rust.
#[pyfunction]
fn matrix_multiply<'py>(py: Python<'py>, a: &PyArray2<f64>, b: &PyArray2<f64>)
->

PyResult<&'py PyArray2<f64>> {
let a = a.as_array();
let b = b.as_array();
let result = a.dot(&b);

Ok(PyArray2::from_array(py, &result))
}

#[pymodule]
fn rust_math(py: Python, m: &PyModule)
->

PyResult<()> {
m.add_function(wrap_pyfunction!(matrix_multiply, m)?)?;
Ok(())
}

"""

Build the Rust Code: Compile it to a shared library, so it can be loaded into Python. (maturin develop # Use maturin to build Rust Python extensions)

Step 2: Python Code Using Rust Extension

Python Code:

"""

import rust_math

import numpy as np

# Use the Rust module for matrix multiplication

def perform_matrix_operations():

a = np.random.rand(1000, 1000)

b = np.random.rand(1000, 1000)

result = rust_math.matrix_multiply(a, b)

print(result)

if __name__ == "__main__":

  perform_matrix_operations()

"""

Step 3: Go Code for Concurrent HTTP Requests

In Go, we'll probably write the API to handle incoming requests and spawn concurrent tasks.

Go Code (main.go):

"""

package main

import (
"fmt"
"log"
"net/http"
"sync"
)

var wg sync.WaitGroup

func processRequest(w http.ResponseWriter, r *http.Request) {
defer wg.Done()
fmt.Fprintf(w, "Processing Request: %s", r.URL.Path)
}

func handleRequests() {
http.HandleFunc("/", func(w http.ResponseWriter, r *http.Request) {
wg.Add(1)
go processRequest(w, r)
})
log.Fatal(http.ListenAndServe(":8080", nil))
}

func main() {
handleRequests()
wg.Wait() // Wait for all goroutines to finish
}

"""

Step 4: Connecting Python and Go

We might can communicate between Python and Go through RESTful APIs or gRPC. Here's how you would call the Go server from Python:

Python API Request to Go:

"""

import requests

# Making a request to Go server

response = requests.get('http://localhost:8080/')
print(response.text)

"""

Final Architecture:

Python: Core AI logic, orchestrates everything.
Rust: Handles performance-heavy computations like matrix multiplication.
Go: Manages concurrent requests and API handling.

This architecture ensures:

Python handles high-level AI logic.
Rust optimizes specific tasks.
Go efficiently manages concurrency and APIs.

Did you liked this idea?

Chikara Houses have some other articles about AI and IT in general, some are a collab with @Creditizens Youtube channel and have videos and example code snippets to get an idea. Continue Reading