The code is configurable via Gin and runs on GPU/TPU/CPUs. You can easily install the library and all necessary dependencies by running: pip install -e . To see all available options please run python main.py We recommend using the ctpu tool to create a Cloud TPU and corresponding Compute Engine VM. We use v3-128 Cloud TPU v3 Pod for training models on ImageNet in 128x128 resolutions.

Scaling clustering algorithms to massive data sets is a challenging task. Recently, several successful approaches based on data summarization methods, such as coresets and sketches, were proposed. While these techniques provide provably good and small summaries, they are inherently problem dependent -- the practitioner has to commit to a fixed clustering objective before even exploring the data. However, can one construct small data summaries for a wide range of clustering problems simultaneously? We affirmatively answer this question by proposing an efficient algorithm that constructs such one-shot summaries for a large family of k-clustering problems while retaining strong theoretical guarantees.