Search for: All records

Interactive notebook programming is universal in modern ML and AI workflows, with interactive deep learning training (IDLT) emerging as a dominant use case. To ensure responsiveness, platforms like Jupyter and Colab reserve GPUs for long-running notebook sessions, despite their intermittent and sporadic GPU usage, leading to extremely low GPU utilization and prohibitively high costs. In this paper, we introduce NotebookOS, a GPU-efficient notebook platform tailored for the unique requirements of IDLT. NotebookOS employs replicated notebook kernels with Raft-synchronized replicas distributed across GPU servers. To optimize GPU utilization, NotebookOS oversubscribes server resources, leveraging high inter-arrival times in IDLT workloads, and allocates GPUs only during active cell execution. It also supports replica migration and automatic cluster scaling under high load. Altogether, this design enables interactive training with minimal delay. In evaluation on production workloads, NotebookOS saved over 1,187 GPU hours in 17.5 hours of real-world IDLT, while significantly improving interactivity. 

Online recommender systems have proven to have ubiquitous applications in various domains. To provide accurate recommendations in real time it is imperative to constantly train and deploy models with the latest data samples. This retraining involves adjusting the model weights by incorporating newly-arrived streaming data into the model to bridge the accuracy gap. To provision resources for the retraining, typically the compute is hosted on VMs, however, due to the dynamic nature of the data arrival patterns, stateless functions would be an ideal alternative over VMs, as they can instantaneously scale on demand. However, it is non-trivial to statically configure the stateless functions because the model retraining exhibits varying resource needs during different phases of retraining. Therefore, it is crucial to dynamically configure the functions to meet the resource requirements, while bridging the accuracy gap. In this paper, we propose Sandpiper, an adaptive framework that leverages stateless functions to deliver accurate predictions at low cost for online recommender systems. The three main ideas in Sandpiper are (i) we design a data-drift monitor that automatically triggers model retraining at required time intervals to bridge the accuracy gap due to incoming data drifts; (ii) we develop an online configuration model that selects the appropriate function configurations while maintaining the model serving accuracy within the latency and cost budget; and (iii) we propose a dynamic synchronization policy for stateless functions to speed up the distributed model retraining leading to cloud cost minimization. A prototype implementation on AWS shows that Sandpiper maintains the average accuracy above 90%, while 3.8× less expensive than the traditional VM-based schemes.