  The rapidly enlarging neural network models are becoming increasingly
challenging to run on a single device. Hence model parallelism over multiple
devices is critical to guarantee the efficiency of training large models.
Recent proposals fall short either in long processing time or poor performance.
Therefore, we propose Celeritas, a fast framework for optimizing device
placement for large models. Celeritas employs a simple but efficient model
parallelization strategy in the Standard Evaluation, and generates placement
policies through a series of scheduling algorithms. We conduct experiments to
deploy and evaluate Celeritas on numerous large models. The results show that
Celeritas not only reduces the placement policy generation time by 26.4\% but
also improves the model running time by 34.2\% compared to most advanced
methods.
