Scheduling For Efficient Large-Scale Machine Learning Training

Over recent years, machine learning techniques have achieved success in many real-world applications. While researchers and practitioners continue to expand machine learning to new application domains and push the boundary of existing applications, they face critical computational challenges due to growing dataset size, increasing model complexity and capacity. These challenges demand new software systems to train large models efficiently and to enable machine learning researchers to easily experiment with