The demand for machine learning (ML) has increased significantly in recent decades,
enabling several applications, such as speech recognition, computer vision, and …

Oobleck enables resilient distributed training of large DNN models with guaranteed fault
tolerance. It takes a planning-execution co-design approach, where it first generates a set of …

Training Deep Neural Networks (DNNs) is a popular workload in both enterprises and cloud
data centers. Existing schedulers for DNN training consider GPU as the dominant resource …

Large Language Models (LLMs) have presented impressive performance across several
transformative tasks. However, it is non-trivial to efficiently utilize large-scale cluster …

LLMs have seen rapid adoption in all domains. They need to be trained on high-end high-
performance computing (HPC) infrastructures and ingest massive amounts of input data …

Processing input data plays a vital role in ML training, impacting accuracy, throughput, and
cost. The input pipeline, which is responsible for feeding data-hungry GPUs/TPUs with …

Large deep learning models have recently garnered substantial attention from both
academia and industry. Nonetheless, frequent failures are observed during large model …

Checkpoints play an important role in training long running machine learning (ML) models.
Checkpoints take a snapshot of an ML model and store it in a non-volatile memory so that …

Modern GPU clusters support Deep Learning training (DLT) jobs in a distributed manner.
Job scheduling is the key to improve the training performance, resource utilization and …

Organizations often build separate training and inference clusters for deep learning, and use
separate schedulers to manage them. This leads to problems for both: inference clusters …