Have you ever desired to construct quicker and more efficient apps yet discovered that standard programming languages are incapable of meeting your requirements?
This is when Mojo Language comes into play.
Mojo is a new programming language developed by Modular, a company that specializes in AI infrastructure.
It is particularly designed to enable developers to construct quicker and more robust apps for several use cases, including AI development. In this post, we’ll look at what Mojo is, how it works, and why you should use it on your next project.
The Origins of Mojo
The creators of Mojo recognized a need for a programming language that combined the usability of Python with the speed and efficiency of C.
And, they established a language that would allow developers to construct high-performance apps without having to learn standard hardware description languages.
As a result, they created Mojo, which is meant to be fully compatible with the Python ecosystem while providing C-level speed.
Modular: The Organization Behind Mojo Language
So, who are the creators of this futuristic language?
Modular is a world-leading AI infrastructure startup created by Chris Lattner and Tim Davis, who met at Google and realized that too complicated and fragmented infrastructure was limiting AI’s global influence.
The aim and objective of Modular are to rethink AI infrastructure to enhance humanity. Their culture and principles place a premium on clients, high-quality production software, and delivering meaningful outcomes
The Goals of Mojo
Mojo was designed to provide a unique programming model for machine learning accelerators.
Modular determined that Mojo should allow general-purpose programming because today’s CPUs have tensors and other AI accelerators. Furthermore, because Python is extensively used in machine learning and other fields, Modular chose to join the Python ecosystem.
The use of Python further eased the design of the language. Because the majority of the syntax had already been determined, the corporation could instead concentrate on building the compilation mechanism and providing specialized programming capabilities.
Important Features to Consider
The progressive types feature of Mojo Language enables developers to use type annotations to enhance efficiency and error checks.
Developers may design more effective code that detects mistakes at compile time, eliminating bugs and enhancing efficiency, by supplying more thorough type information.
Zero Cost Abstractions
Developers can take control of storage by inline-allocating data into structures thanks to the zero-cost abstractions provided by the Mojo Language.
By doing away with the requirement for extra memory allocations, this feature improves code efficiency and lowers the amount of boilerplate code.
Integrated Language Auto-Tuning
The language-integrated auto-tuning functionality in Mojo Language enables developers to automatically determine the parameters’ ideal values to benefit from the target hardware.
With this functionality, it is simpler to optimize code without having to manually adjust it for every hardware combination.
Mojo Language is a flexible and effective tool for AI development since it also provides the entire power of MLIR(Multi-Level Intermediate Representation), a parallel heterogeneous runtime, and quick build times.
Mojo makes use of MLIR, which allows programmers to utilize vectors, threads, and hardware units for AI for parallelization. Mojo supports parallel processing over several cores as opposed to Python’s single-threaded execution.
With a 35,000x speedup over Python, Mojo beats other languages in terms of speed.
Interoperability with the whole Python ecosystem is one of Mojo’s unique advantages. This implies that you may combine Mojo with any arbitrary libraries, such as Numpy and Matplotlib, as well as your bespoke code.
For instance, you may visualize data in your Mojo code using the Python library Matplotlib.
Mojo is also quite flexible, allowing users to simply add pre- and post-processing steps to models or swap out existing steps for new ones.
To improve their models and the Modular stack, developers can use kernel fusion, graph rewrites, shape functions, and other techniques.
Why Would You Choose It Over Python?
Python is a popular AI development language because of its flexibility and high-performance compiled libraries. However, it is significantly slower than languages such as C++.
Therefore, programmers learn to use Python wrappers around faster languages to avoid using Python for performance-critical areas.
This leads to the two-language issue, in which models must be transformed from Python into a speedier implementation, such as ONNX or torch script, which does not support all of Python’s capabilities. Python also has performance issues and a lack of good parallel processing.
Even experienced programmers find it difficult to study, debug, and address performance issues due to the two-language dilemma.
There are also basic restrictions to what Python can do due to the way the language is structured. While the real-world implementation of algorithms employing basic components may appear easy, it is significantly more difficult and necessitates the use of a quicker language.
How To Get Started?
You can start using Mojo Language right now in their Playground to get started. The JupyterHub-based Playground gives lessons and the opportunity to create your own Mojo code, even though Mojo is still a work in progress.
You may register for access on their website to access the Playground.
To reach its performance goals, Mojo employs next-generation compiler technologies with integrated caching, multithreading, and cloud distribution technologies.
It adds additional primitives to systems programming and aspires to become a Python superset over time. To begin coding with Mojo, use the Mojo compiler to run a Mojo program from the terminal in the same way that Python does.
The language is currently under development and is aimed at developers with experience in systems programming.
Mojo is considered a Python superset. And, it has the potential to revolutionize programming by combining the simplicity of Python with the performance of C++ and Rust.
It can take advantage of the full Python library ecosystem, allowing developers to create high-performance libraries without the need for C, C++, Rust, or CUDA.
It enables the production of portable code thanks to its autotuning and compile-time meta-programming features.
Though it is still a work in progress, it has the potential to become a formidable programming tool and transform the programming industry. So, we should stay tuned for future developments from Modular!
İlke Candan Bengi
Hello Chris, sorry for the late response! I believe having a basic understanding/experience of Python beforehand could be helpful before starting with Mojo.