Distributed Training, Model Parallelism, Data Parallelism, Pytorch, Aurorain.
parallel training. The ML lifecycle involves multiple distributed systems, from data collection to processing, model training, and serving. ML platform is often hindered by complexity, data communication costs, and system instability. Unifying all distributed systems for ML will be significant.