Sibylla: To Retry or Not To Retry on Deep Learning Job Failure

GPUs are highly contended resources in shared clusters for deep learning (DL) training. However, our analysis with a real-world trace reveals that a non-negligible number of jobs running on the cluster undergo failures and are blindly retried by the job scheduler. Unfortunately, these job failures often repeat and waste GPU resources, limiting effective GPU utilization across the cluster. In this paper, we introduce Sibylla which informs whether an observed failure of DL training will repeat or not upon retry on the failure. Sibylla employs a machine learning model based on RNNs that trains on stdout and stderr logs of failed jobs and can continuously update the model on new log messages without hand-constructing labels for the new training samples. With Sibylla, the job scheduler is learning-enhanced, performing a retry for a failed job only when it is highly likely to succeed with the retry. We evaluate the effectiveness of Sibylla under a variety of scenarios using trace-driven simulations. Sibylla improves cluster utilization and reduces job completion time (JCT) by up to 15%

Therapeutic Potential of Human Fetal Mesenchymal Stem Cells in Musculoskeletal Disorders: A Narrative Review

Mesenchymal stem cells (MSCs) have emerged as a promising therapeutic approach for diverse diseases and injuries. The biological and clinical advantages of human fetal MSCs (hfMSCs) have recently been reported. In terms of promising therapeutic approaches for diverse diseases and injuries, hfMSCs have gained prominence as healing tools for clinical therapies. Therefore, this review assesses not the only biological advantages of hfMSCs for healing human diseases and regeneration, but also the research evidence for the engraftment and immunomodulation of hfMSCs based on their sources and biological components. Of particular clinical relevance, the present review also suggests the potential therapeutic feasibilities of hfMSCs for musculoskeletal disorders, including osteoporosis, osteoarthritis, and osteogenesis imperfecta