Speedup (35000x) your AI Development with Mojo: Goodbye Python
Dr. Manish Kumar Saraf - DSC, PhD, MBA,
Associate Director | Artificial |Intelligence | Generative AI Architect | MAS (Muti-Agent System | Knowledge Graph | LLMs | Certified | Automation | Vector Database | Author | Innovator
Mojo is new programming language (just like python, julia etc) for AI Developers. However it is 35000 times faster that python. Mojo means “a magical charm” or “magical powers.” Interestingly, it utilizes all capabilities libraries of python with a performance of C. It provides a powerful systems programming and metaprogramming features. It is developed by a leading company called Modular AI witch was founded by Chris Lattner and Tim Davis. Modular AI builds a programming language with powerful compile-time metaprogramming, incorporating adaptive compilation techniques, caching throughout the compilation process, and other things.
Pros and Cons of Mojo
Mojo's most of the syntax is similar to Python. Mojo is super fast, accelerate and scalable programming language. Mojo allows you to leverage the entire Python ecosystem so you can continue to use tools you are familiar with. Mojo is designed to become a superset of Python over time by preserving Python’s dynamic features while adding new primitives for system programming.
Mojo already supports many core features of Python, including async/await, error handling, and variadic, but Mojo is still in a very early stage of the development stage, and Mojo doesn’t even support classes yet
Hands-on Experience with Mojo
You can start testing Modular AI with JupyterHub-based Playground. You can import Python packages and use them the way you want. However, Mojo provides many other powerful features tool. So don't forget to explore it.
Please signup to Mojo Playgroup to have access of Mojo jupyterlab.
Start with "HelloMojo notebook" which is the very basic notebook, where you can run hello world like program.
You can explore other notebook books too for benchmark testing.
Benchmarking of Mandelbrot Algorithm
Mandelbrot algorithm involves computing an iterative complex function for each pixel until it "escapes" the complex circle of radius 2, counting the number of iterations to escape. Plotting the number of iterations to escape with some color gives us the canonical Mandelbrot set plot. To render it we can directly leverage Python's `matplotlib` right from Mojo!
Another benchmark test was performed with sequential implementation against the parallel implementation.
The above table shows 2x seedup when MAX_ITERS = 200, which can goes upto 4.89x when MAX_ITERS increase to 1000
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Comparison of Mojo with other language
Mojo is 35000 times faster than python.
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