TensorFlow look to get more frugal with resources ahead of 2.3 release • DEVCLASS

Data scientists and machine learning types get a last chance for input in the upcoming 2.3 release of machine learning framework TensorFlow with the release candidate, which is now available, showcasing new features to tackle bottlenecks and preprocess data. The former is mostly realised through experimental snapshot and distribution mechanisms, which can now be found in TensorFlow's data module. They allow users to persist the outputs of their preprocessing to use in consequent steps and produce data for parallel iterations over a data set, leading to lesser resource consumption and speedups. If this doesn't help, developers will also find a memory profiler and a tracer for Python code in TensorFlow 2.3. With those, investigating performance bottlenecks should become a bit easier, providing teams with at least some clues as to what they could investigate to speed up their code.