Transfer learning is an effective technique for tuning a deep learning model when training data or computational resources are limited. Instead of training a new model from scratch, the parameters of an existing base model are adjusted for the new task. The accuracy of such a fine-tuned model depends on the suitability of the base model chosen. Model search automates the selection of such a base model by evaluating the suitability of candidate models for a specific task. This entails inference with each candidate model on task-specific data. With thousands of models available through model stores, the computational cost of model search is a major bottleneck for efficient transfer learning. In this work, we present Alsatian , a novel model search system. Based on the observation that many candidate models overlap to a significant extent and following a careful bottleneck analysis, we propose optimization techniques that are applicable to many model search frameworks. These optimizations include: (i) splitting models into individual blocks that can be shared across models, (ii) caching of intermediate inference results and model blocks, and (iii) selecting a beneficial search order for models to maximize sharing of cached results. In our evaluation on state-of-the-art deep learning models from computer vision and natural language processing, we show that Alsatian outperforms baselines by up to 14x.

Data centers produce a significant and increasing amount of CO 2 emissions. In the past, these have been predominantly due to energy generation for powering data centers. With the transition to energy sources with lower carbon production, the embodied carbon (i.e., CO 2 and other greenhouse gas emissions during production, transport, and end-of-life) plays an increasing role when planning server lifecycles. While replacing an old server with newer hardware will typically reduce the power consumption of individual tasks, due to better efficiency of modern CPUs, offsetting the embodied carbon of new hardware can take months to tens of years, depending on the grid carbon intensity. In this demo, we invite attendees to interactively analyze the ecological lifecycles of modern database servers for different workloads and grid carbon intensities. Attendees can compare servers with different CPU architectures and estimate ecological deployment cycles for database servers.