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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.

Today, we’re excited to announce general availability of MAX 24.2, with several big features.

Model deployment is often the domain of IT professionals and cloud infrastructure experts who understand how to securely and reliably host model endpoints that scale with usage demand. Thankfully, Amazon SageMaker is fully managed and handles all the underlying infrastructure, allowing developers and data scientists like you and me, who are not IT experts, to use simple APIs to host secure, low-latency, and highly scalable model endpoints.

In the field of natural language processing (NLP), semantic search focuses on understanding the context and intent behind queries, going beyond mere keyword matching to provide more relevant and contextually appropriate results. This approach relies on advanced embedding models to convert text into high-dimensional vectors, capturing the complex semantics of language.

Mojo as a language offers three main benefits, namely the 3 P’s: Performance, Programmability and Portability. It enables users to write fast code, do so easier than many alternative languages, and allows code to be run across different CPU platforms, with GPU support on the roadmap.

In this blog post, we introduce the MAX Engine C API, gradually building awareness of its capabilities. The C API enables the integration of the MAX Engine into high-performance application code, facilitating running inference with models from PyTorch, TensorFlow and ONNX suitable for environment that does not require Python dependencies.

March 14th aka 3/14 or 3.14 is known as $\pi$ Day, and it honors the mathematical constant $\pi$ (pi), which represents the ratio of a circle's circumference to its diameter. On this special day, I wanted to dedicate a blog post to the beauty of mathematics, numerical methods, $\pi$, and Mojo. So join me on this journey as I implement a fast vectorized Monte Carlo approximation method of calculating $\pi$. Happy $\pi$ Day!