The Mueller Minute
Zach MuellerI discuss common problems I've seen when it comes to distributed training, provide general tips, and talk about cool things I've found that week.
| Platform |  | Pricing | Only free issues | Publishes | Daily |
|---|---|---|---|---|---|
| Issues | 36 | Subscribers | Read | muellerminute.substack.com |
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Latest Issues
Recent posts by this newsletter. Browse the email archive.
DataLoader Dispatching
When constrained by a variety of reasons to where you can’t include multiple copies (or mmaps) of datasets in memory, be it too many concurrent streams, low resource availability, or a slow CPU, dispatching is here to help.
Dataset Sharding
When performing distributed data parallelism, we split the dataset every batch so every device sees a different chunk of the data. There are different methods for doing so. One example is sharding at the dataset level, shown here.
Synchronous Training
Synchronous training is a distributed training paradigm where all worker processes (e.g., GPUs) perform forward and backward passes on their respective data shards and then synchronize their results, typically gradients, before updating the...
Smart Parameter Sharding
Smart parameter sharding refers to advanced strategies for partitioning model parameters across distributed devices, going beyond simple uniform sharding. The goal is to optimize the placement of parameter shards based on various factors su...
Authors
The writers behind this newsletter.
I've been in the field for almost a decade. I was a fast.ai community leader, and quickly learned how training pipelines are built and operated. Then I moved to Hugging Face, where work on the accelerate project and manage the Trainer.
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