A collection of posts that focus on topics related to Google's TensorFlow python library.

Apple's new M1 Max and M1 Pro MacBooks

The specs are indeed intimidating: Up to 32 GPU cores and up to 16 CPU cores. Pair that with 64 GB of RAM, and you're well equipped for any workload. And, not to forget the design. Well, it seems Apple did it again.

Writing TensorFlow code that scales

Most of the time, we write and debug our code locally. After we've passed any tests, we then deploy the scripts to a remote environment. If we're fortunate, we might have access to multiple GPUs.

A Template for Custom and Distributed Training

Custom training loops offer great flexibility. You can quickly add new functionality and gain deep insight into how your algorithm works under the hood. However, setting up custom algorithms over and over is tedious. The general layout often is the same; it’s only tiny parts that change.

Writing Machine Learning Code that scales

After you have finally created that training script it’s time to scale things up. From a local development environment, be it an IDE or Colab, to a large computer cluster, it’s quite a stretch. The following best practices make this transition easier.