Introducing Mojo — the brand new programming language for AI developers.

A recent programming language for AI developers was just released: Mojo.

I do know what you is perhaps considering — a recent programming language to learn from scratch … Well, I even have excellent news, Mojo is designed as a superset of Python, so when you already know Python learning Mojo shouldn’t be hard.

But that’s not all. Mojo combines the usability of Python with the performance of C obtaining a speed that’s as much as 35000x faster than Python.

Should you’re into AI and already know Python, Mojo is unquestionably value a try. Here’s every little thing it is advisable find out about Mojo.

Why do we’d like Mojo if we have already got Python?

Python’s simplicity and flexibility made it the language of selection in fields similar to data science, machine learning, and AI. It has tons of packages which might be very useful for anyone working with data, but for libraries that require great performance, Python only acts as a glue layer and low-level bindings to C, C++, and other languages with higher performance.

This enabled the event of libraries similar to numpy and TensorFlow. Nevertheless, this comes with a drawback: constructing these libraries may be very complicated, it requires a low-level understanding of the internals of CPython, requires knowledge of C/C++, etc.

In line with the Mojo doc, the problems brought by Python go deeper and particularly impact the AI field.

Python alone isn’t in a position to address all the problems that applied AI systems need and that’s how Mojo was born. Mojo is a programming language that mixes the usability of Python with the performance of C.

The most effective of each worlds!

But Mojo isn’t a random project that emerged out of nowhere. In reality, Mojo comes from an organization named Modular, co-founded by Chris Lattner, the identical guy who created the Swift programming language and LLVM.

That’s why I feel this project is value taking note of. Now let’s see a few of Mojo’s best features.

Features of Mojo

Mojo comes with many interesting features out of the box. Listed here are a few of them.

1. Mojo is designed as a superset of Python

Mojo goals to be fully compatible with the Python ecosystem.

Which means you might easily work with Mojo when you’re a Python programmer because each programming languages have many functions, features, and libraries in common.

Libraries similar to numpy, pandas, and matplotlib are also available in Mojo. Here’s the way you’d make a plot with matplotlib using Mojo.

That said, Mojo remains to be in a really early stage, so it still misses many features of Python (for instance it doesn’t support classes yet).

Hopefully, in future updates Mojo might be fully compatible with Python.

2. Strong type checking

Mojo leverage types for higher performance and error checking.

Although you’ll be able to still use flexible types like with Python, Mojo helps you to use strict type-checking. This will make your code more predictable, manageable, and secure.

3. Memory ownership and borrowing checker

Mojo supports a owned argument convention that’s used for functions that wish to take exclusive ownership over a price.

This can assist you to reap the benefits of memory safety without the rough edges.

4. Auto-tuning

Mojo has built-in autotuning that helps mechanically find the most effective values to your parameters to reap the benefits of goal hardware.

5. Mojo leverages MLIR

By utilizing the total power of Multi-Level Intermediate Representation (MLIR), Mojo developers can reap the benefits of vectors, threads, and AI hardware units.

This helps Mojo achieve great performance because, unlike Python which works with single-threaded execution, Mojo can work with parallel processing across multiple cores.

That’s certainly one of the explanation why Mojo it’s 35000x faster than Python.

The right way to start using Mojo

Mojo remains to be a piece in progress, but you’ll be able to try it today on the JupyterHub-based Playground. To try Mojo go to this website to register and don’t forget to envision the Mojo box within the “Modular Product Interest” section.

Pleased coding!