Aligning Velox and Apache Arrow: Towards composable data management
Meta Engineering
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4d ago
We’ve partnered with Voltron Data and the Arrow community to align and converge Apache Arrow with Velox, Meta’s open source execution engine. Apache Arrow 15 includes three new format layouts developed through this partnership: StringView, ListView, and Run-End-Encoding (REE). This new convergence helps Meta and the larger community build data management systems that are unified, more efficient, and composable. Meta’s Data Infrastructure teams have been rethinking how data management systems are designed. We want to make our data management systems more composable – meaning that instead of i ..read more
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Meta loves Python
Meta Engineering
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1w ago
By now you’re already aware that Python 3.12 has been released. But did you know that several of its new features were developed by Meta? Meta engineer Pascal Hartig (@passy) is joined on the Meta Tech Podcast by Itamar Oren and Carl Meyer, two software engineers at Meta, to discuss their teams’ contributions to the latest Python release, including new hooks that allow for custom JITs like Cinder, Immortal Objects, improvements to the type system, faster comprehensions, and more. Learn how and why they built these new features for Python and how they worked with and engaged with the Python com ..read more
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Simple Precision Time Protocol at Meta
Meta Engineering
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2w ago
While deploying Precision Time Protocol (PTP) at Meta, we’ve developed a simplified version of the protocol (Simple Precision Time Protocol – SPTP), that can offer the same level of clock synchronization as unicast PTPv2 more reliably and with fewer resources. In our own tests, SPTP boasts comparable performance to PTP, but with significant improvements in CPU, memory, and network utilization. We’ve made the source code for the SPTP client and server available on GitHub. We’ve previously spoken in great detail about how Precision Time Protocol is being deployed at Meta, including the protoco ..read more
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DotSlash: Simplified executable deployment
Meta Engineering
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2w ago
We’ve open sourced DotSlash, a tool that makes large executables available in source control with a negligible impact on repository size, thus avoiding I/O-heavy clone operations. With DotSlash, a set of platform-specific executables is replaced with a single script containing descriptors for the supported platforms. DotSlash handles transparently fetching, decompressing, and verifying the appropriate remote artifact for the current operating system and CPU. At Meta, the overwhelming majority of DotSlash files are generated and committed to source control via automation, so we are also releas ..read more
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Improving machine learning iteration speed with faster application build and packaging
Meta Engineering
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3w ago
Slow build times and inefficiencies in packaging and distributing execution files were costing our ML/AI engineers a significant amount of time while working on our training stack. By addressing these issues head-on, we were able to reduce this overhead by double-digit percentages.  In the fast-paced world of AI/ML development, it’s crucial to ensure that our infrastructure can keep up with the increasing demands and needs of our ML engineers, whose workflows include checking out code, writing code, building, packaging, and verification. In our efforts to maintain efficiency and produc ..read more
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Lazy is the new fast: How Lazy Imports and Cinder accelerate machine learning at Meta
Meta Engineering
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1M ago
At Meta, the quest for faster model training has yielded an exciting milestone: the adoption of Lazy Imports and the Python Cinder runtime. The outcome? Up to 40 percent time to first batch (TTFB) improvements, along with a 20 percent reduction in Jupyter kernel startup times. This advancement facilitates swifter experimentation capabilities and elevates the ML developer experience (DevX). Time is of the essence in the realm of machine learning (ML) development. The milliseconds it takes for an ML model to transition from conceptualization to processing the initial training data can dramatic ..read more
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How Meta is advancing GenAI
Meta Engineering
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1M ago
What’s going on with generative AI (GenAI) at Meta? And what does the future have in store? In this episode of the Meta Tech Podcast, Meta engineer Pascal Hartig (@passy) speaks with Devi Parikh, an AI research director at Meta. They cover a wide range of topics, including the history and future of GenAI and the most interesting research papers that have come out recently. And, of course, they discuss some of Meta’s latest GenAI innovations, including: Audiobox, a foundational model for generating sound and soundscapes using natural language prompts. Emu, Meta’s first foundational m ..read more
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How Meta built the infrastructure for Threads
Meta Engineering
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2M ago
On July 5, 2023, Meta launched Threads, the newest product in our family of apps, to an unprecedented success that saw it garner over 100 million sign ups in its first five days. A small, nimble team of engineers built Threads over the course of only five months of technical work. While the app’s production launch had been under consideration for some time, the business finally made the decision and informed the infrastructure teams to prepare for its launch with only two days’ advance notice. The decision was made with full confidence that Meta’s infrastructure teams can deliver based on thei ..read more
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AI debugging at Meta with HawkEye
Meta Engineering
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2M ago
HawkEye is the powerful toolkit used internally at Meta for monitoring, observability, and debuggability of the end-to-end machine learning (ML) workflow that powers ML-based products. HawkEye supports recommendation and ranking models across several products at Meta. Over the past two years, it has facilitated order of magnitude improvements in the time spent debugging production issues. In this post, we will provide an overview of the end-to-end debugging workflows supported by HawkEye, components of the system, and the product surface for Meta product and monetization teams to debug AI mod ..read more
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Building end-to-end security for Messenger
Meta Engineering
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2M ago
We are beginning to upgrade people’s personal conversations on Messenger to use end-to-end encryption (E2EE) by default. Meta is publishing two technical white papers on end-to-end encryption: Our Messenger end-to-end encryption whitepaper describes the core cryptographic protocol for transmitting messages between clients. The Labyrinth encrypted storage protocol whitepaper explains our protocol for end-to-end encrypting stored messaging history between devices on a user’s account. Today, we’re announcing that we’ve begun to upgrade people’s personal conversations on Messenger to use E2EE ..read more
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