Coding Like Python, Running Like C++? My Hands-On Experience with Mojo!

Introduction

Mojo is a systems programming language designed specifically for high-performance AI infrastructure and heterogeneous hardware. Its Pythonic syntax makes it easy for Python programmers to learn, and it fully integrates with the existing Python ecosystem, including a rich set of AI and machine learning libraries.

It is the first programming language built from the ground up using MLIR. MLIR is a modern compiler infrastructure aimed at heterogeneous hardware (from CPUs to GPUs and other AI ASICs). This means you can write all your code in one language without needing any hardware-specific libraries.

Installation

Install uv

curl -LsSf https://astral.sh/uv/install.sh | sh

Create a project

uv init hello
cd hello

Create a virtual environment

uv venv && source .venv/bin/activate

Install Mojo

uv pip install mojo --extra-index-url https://modular.gateway.scarf.sh/simple/

Verify installation

mojo --version

Install Extensions

Extension link: Mojo for Visual Studio Code

Testing

Create hello.mojo

def main():
    print("Hello, World!")

Run

mojo hello.mojo

Conclusion

The core experience of Mojo is the combination of Python’s ease of use and C++/CUDA’s performance, making it particularly suitable for scientific computing and AI scenarios. The only downside is that the development tools and ecosystem are still in their early stages, requiring some exploration.

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