Desa aikmual

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 [...] Read More... The post Improving machine learning iteration speed with faster application build and packaging appeared first on Engineering at Meta. http://dlvr.it/T22Qcp

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 [...] Read More... The post Lazy is the new fast: How Lazy Imports and Cinder accelerate machine learning at Meta appeared first on Engineering at Meta. http://dlvr.it/T1Z9nc

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 [...] Read More... The post How Meta is advancing GenAI appeared first on Engineering at Meta. http://dlvr.it/T1G1WR