Blog

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. 

Learning a new programming language is hard. You have to learn new syntax, keywords, and best practices, all of which can be frustrating when you’re just starting. In this blog post, I want to share a gentle introduction to Mojo from a Python programmer’s perspective.

Today’s AI infrastructure is difficult to evaluate - so many converge on simple and quantifiable metrics like QPS, Latency and Throughput. This is one reason why today’s AI industry is rife with bespoke tools that provide high performance on benchmarks but have significant usability challenges in real-world AI deployment scenarios.

In this post, we describe Modular’s approach to solving this problem and its game-changing benefits, including a new standard in state-of-the-art (SOTA) performance on CPU as compared to existing solutions.

AI is powered by a virtuous circle of data, algorithms (“models”), and compute. Growth in one pushes needs in the others and can grossly affect the developer experience on aspects like usability and performance. Today, we have more data and more AI model research than ever before, but compute isn’t scaling at the same speed due to … well, physics.

The technological progress that has been made in AI over the last ten years is breathtaking — from AlexNet in 2012 to the recent release of ChatGPT, which has taken large foundational models and conversational AI to another level.

The first four requirements address one fundamental problem with how we've been using MLIR: weights are constant data, but shouldn't be managed like other MLIR attributes. Until now, we've been trying to place a square peg into a round hole, creating a lot of wasted space that's costing us development velocity (and, therefore, money for users of the tools).

Machine learning models are getting larger and larger — some might even say, humongous. The world’s most advanced technology companies have been in an arms race to see who can train the largest model (MUM, OPT, GPT-3, Megatron), while other companies focused on production systems have scaled their existing models to great effect. Through all the excitement, what’s gone unsaid is the myriad of practical challenges larger models present for existing AI infrastructure and developer workflows.