What happens when a team of mechanical engineers get tired of looking at flat images of 3D models over Zoom? Meet the team behind Caddy, a new CAD app for mixed reality. They join Pascal Hartig (@passy) on the Meta Tech Podcast to talk about teaching themselves to code, disrupting the CAD software space, how they integrated Caddy with Llama 3, and so much more! You can download Caddy today to check it out yourself! Download or listen to the podcast episode below: You can also find the episode wherever you get your podcasts, including: Apple Podcasts Spotify PocketCasts Overcast Castro The&n ..read more

The history of Rust at Meta goes all the way back to 2016, when we first started using it for source control. Today, it has been widely embraced at Meta and is one of our primary supported server-side languages (along with C++, Python, and Hack). But that doesn’t mean there weren’t any growing pains. Aida G., a member of one of Meta’s first Rust teams, joins Pascal Hartig (@passy) on the latest Meta Tech Podcast to dive into the challenges of getting Rust to interact with Meta’s large amount of existing C++ code. Fortunately, the release of cxx, safe interop between C++, and even async Rust ha ..read more

We’re sharing how we streamline system reliability investigations using a new AI-assisted root cause analysis system. The system uses a combination of heuristic-based retrieval and large language model-based ranking to speed up root cause identification during investigations. Our testing has shown this new system achieves 42% accuracy in identifying root causes for investigations at their creation time related to our web monorepo. Investigation is a critical part of ensuring system reliability, and a prerequisite to mitigating issues quickly. This is why Meta is investing in advancing our su ..read more

We’re introducing parameter vulnerability factor (PVF), a novel metric for understanding and measuring AI systems’ vulnerability against silent data corruptions (SDCs) in model parameters. PVF can be tailored to different AI models and tasks, adapted to different hardware faults, and even extended to the training phase of AI models. We’re sharing results of our own case studies using PVF to measure the impact of SDCs in model parameters, as well as potential methods of identifying SDCs in model parameters. Reliability is an important aspect of any successful AI implementation. But the growin ..read more

As we continue to focus our AI research and development on solving increasingly complex problems, one of the most significant and challenging shifts we’ve experienced is the sheer scale of computation required to train large language models (LLMs). Traditionally, our AI model training has involved a training massive number of models that required a comparatively smaller number of GPUs. This was the case for our recommendation models (e.g., our feed and ranking models) that would ingest vast amounts of information to make accurate recommendations that power most of our products. With the adven ..read more

Meta is currently operating many data centers with GPU training clusters across the world. Our data centers are the backbone of our operations, meticulously designed to support the scaling demands of compute and storage. A year ago, however, as the industry reached a critical inflection point due to the rise of artificial intelligence (AI), we recognized that to lead in the generative AI space we’d need to transform our fleet.  Our increased focus on AI was driven both by its rise in driving business outcomes and the huge growth in these types of workloads’ computational needs. In additio ..read more

MobileConfig enables developers to centrally manage a mobile app’s configuration parameters in our data centers. Once a parameter value is changed on our central server, billions of app devices automatically fetch and apply the new value without app updates. These remotely managed configuration parameters serve various purposes such as A/B testing, feature rollout, and app personalization. MobileConfig has been in production since 2015 and serves some of the world’s most widely used apps, including Facebook, Instagram, and Messenger.  In this blog, we describe how MobileConfig enables rap ..read more

At Meta, Bento, our internal Jupyter notebooks platform, is a popular tool that allows our engineers to mix code, text, and multimedia in a single document. Use cases run the entire spectrum from what we call “lite” workloads that involve simple prototyping to heavier and more complex machine learning workflows. However, even though the lite workflows require limited compute, users still have to go through the same process of reserving and provisioning remote compute – a process that takes time – before the notebook is ready for any code execution. To address this problem, we have invested in ..read more

In recent years, Meta’s data management systems have evolved into a composable architecture that creates interoperability, promotes reusability, and improves engineering efficiency.  We’re sharing how we’ve achieved this, in part, by leveraging Velox, Meta’s open source execution engine, as well as work ahead as we continue to rethink our data management systems.  Data is at the core of every product and service at Meta. To efficiently process data generated by billions of people, Data Infrastructure teams at Meta have built a variety of data management systems over the last decade ..read more

Today, the internet (like most digital infrastructure in general) relies heavily on the security offered by public-key cryptosystems such as RSA, Diffie-Hellman (DH), and elliptic curve cryptography (ECC). But the advent of quantum computers has raised real questions about the long-term privacy of data exchanged over the internet. In the future, significant advances in quantum computing will make it possible for adversaries to decrypt stored data that was encrypted using today’s cryptosystems. Existing algorithms have reliably secured data for a long time. However, Shor’s algorithm can efficie ..read more