Developer Articles (X)

Ownership is a well-known concept in modern programming languages such as Mojo that aims to provide a safe programming model for memory management while ensuring high performance. This allows programmers to build safe abstractions without the need to manually manage memory, making development more efficient and less error-prone. 

There are several clustering algorithms, but k-means — the algorithm we're going to implement from scratch in Python and Mojo🔥 in this blog post — is one of the most popular due to its simplicity and ease of implementation.

MAX Engine is a next-generation compiler and runtime library for running AI inference. With support for PyTorch (TorchScript), ONNX, and native Mojo models, it delivers low-latency, high-throughput inference on a wide range of hardware to accelerate your entire AI workload.

Mojo🔥 24.3 is now available for download and this is a very special release. This is the first major release since Mojo🔥 standard library was open sourced and it is packed with the wholesome goodness of community contributions! The enthusiasm from the Mojo community to enhance the standard library has been truly remarkable. And on behalf of the entire Mojo team, we’d like to thank you for all your feedback, discussion and, contributions to Mojo, helping shape it into a stronger and more inclusive platform for all. 

Today, we're exploring how a multimodal approach can further enhance semantic search by combining textual and visual data and we will discuss how MAX Engine can optimize multiple models for inference.

A matrix is a rectangular collection of row vectors and column vectors that defines linear transformation. A matrix however, is not implemented as a rectangular grid of numbers in computer memory, we store them as a large array of elements in contiguous memory.

Very recently, we announced the open sourcing of the Mojo standard library. This has marked a significant milestone for our community, not only providing the best way to understand the implementation details of various functionalities within the standard library but also creating an excellent opportunity to contribute to Mojo.

This will be your example-driven guide to Mojo SDK 24.2, as part of the latest MAX release. If I had to pick a name for this release, I’d call it MAXimum⚡ Mojo🔥 Momentum 🚀 because there is so much much good stuff in this release, particularly for Python developers, adopting Mojo. 

At Modular, open source is ingrained in our DNA. We firmly believe for Mojo to reach its full potential, it must be open source. We have been progressively open-sourcing more of Mojo and parts of the MAX platform, and today we’re thrilled to announce the release of the core modules from the Mojo standard library under the Apache 2 license!

In this blog post, we will dive deep into the dynamic shapes support in MAX Engine’s 24.2 release. We will do so by first defining what dynamic shapes mean in machine learning, discussing their various types and use cases.