Engineering Insights

Accelerate Large Model Training using DeepSpeed

Announcing Evaluation on the Hub

Grouping and aggregations on Vitess

I love my job. One of the best feelings is when I find an interesting paper and use it to solve a real problem. It feels like I found a cheat code. Instead of having to do a lot of hard thinking, I can just stand on the shoulders of really big people and take a shortcut. Here, I want to share a recent project that I could solve using a public paper. Sharding databases Vitess is a database proxy that creates an illusion of a single database, when in reality the query is sent to multiple MySQL ins

Learning to play Minecraft with Video PreTraining

We trained a neural network to play Minecraft by Video PreTraining (VPT) on a massive unlabeled video dataset of human Minecraft play, while using only a small amount of labeled contractor data. With fine-tuning, our model can learn to craft diamond tools, a task that usually takes proficient humans over 20 minutes (24,000 actions). Our model uses the native human interface of keypresses and mouse movements, making it quite general, and represents a step towards general computer-using agents.

Getting Started With Embeddings

Convert Transformers to ONNX with Hugging Face Optimum

Consensus algorithms at scale: Part 6 - Completing requests

If you’re still catching up, you can find links to each article in the series at the bottom of this article. The completion of requests is a relatively straightforward topic. However, there are a few concepts to cover before we jump into the more complex part of propagating requests during a leadership change. Recap of parts 1-5 We have redefined the problem of consensus with the primary goal of solving durability in a distributed system, and are approaching the problem top-down. We have shown a

OpenSSL update assessment, and Node.js project plans