Blog

For ModCon 2023 we'll be hosting a competition, where the best projects from around the community will be presented on stage.

Python libraries such as NumPy and Pandas are so popular because they make engineers and data scientists productive. The first time I used NumPy, I was struck, not by its vast array of supported math functions, but by the ease with which I could manipulate arrays and matrices and, in particular, slicing rows, columns, or submatrices from large tabular datasets. 

Mojo SDK v0.5 is now available for download and includes exciting new features. In this blog post, I’ll discuss what these features are and how to use them with code examples. ICYMI, in last week’s Modular community livestream, we dove deep into all things Mojo SDK v0.5 with live demos of the examples shared in this blog post, while answering your questions live!

We are happy to welcome Mostafa Hagog to Modular, who recently joined to lead our high performance numeric kernels, graph compiler, and low level heterogeneous runtime teams! These technology areas are critical low-level components of our AI Engine, and are directly responsible for delivering state of the art performance across many categories of hardware.

We released Mojo local download for Linux systems on September 7th, and in just over a month we’ve seen tens of thousands of downloads and amazing community projects such as llama.🔥and Infermo. We deeply value your feedback, as Mojicians, and we appreciate every single PR, feature request, and bug report.

Jeff, Abdul, and Chris presented a talk about Mojo and how it relates to MLIR and LLVM, at the LLVM Developer Conference 2023.

Mojo SDK was released in September 2023. As someone who relies on the simplicity of Python and also cares about high performance delivered by languages like C, I was excited to try out Mojo. I felt the same joy and thrill I had experienced when I first discovered programming and ran “Hello World” in QBasic and Turbo Pascal.

Mojo allows you to access the entire Python ecosystem, but environments can vary depending on how Python was installed. It's worth taking some time to understand exactly how modules and packages work in Python, as there are a few complications to be aware of. If you've had trouble calling into Python code before, this will help you get started.

How do you guarantee that your software is portable, runs reliably, and scales easily in production environments? The short answer is: Use containers. Container technologies like Docker and Kubernetes are popular tools for building and deploying software applications, but until recently they were considered exotic infrastructure for IT/Ops experts. 

AI systems take an incredible amount of time to build and get right - I know because I have helped scale some of the largest AI systems in the world, which have directly and indirectly impacted billions of people. If I step back and reflect briefly - we were promised mass production self-driving cars 10+ years ago, and yet we still barely have any autonomous vehicles on the road today.