###### Abstract
In addition to graph algorithms, the successful experience of machine learning in the field of systems and resource scheduling has prompted researchers to use machine learning to solve neural network segmentation and scheduling problems. [PERSON] et al. [PERSON] et al. (2019) proposed Parallex, a learning linear model that adjusts the size of variables to achieve adaptive tensor partitioning. [PERSON] et al. [PERSON] et al. (2019) determined the credit size for scheduling using Bayesian optimization. However, these two methods are based on traditional machine learning, where the main body tuning model is relatively simple, and the performance of parallel execution is limited. With the development of deep reinforcement learning, [PERSON] et al. [PERSON] et al. (2019) predicted the placement of operations in a neural network based on reinforcement learning. As this method requires the manual grouping of operators, [PERSON] et al. [PERSON] et al. (2019) further proposed Hierarchical, an end-to-end hierarchical architecture that uses Grouper to automatically group computer graph operators and uses Placer to select the best execution equipment for each group of operators. [PERSON] et al. [PERSON] et al. (2019) introduced the method of minimizing the cross-entropy into the sampling process of reinforcement learning to prove the improvement of the upper and lower bounds of the Markov process. [PERSON] et al. [PERSON] et al. (2019) proposed Placeto to learn generalizable device placement policies that can be applied to any graph with a graph neural network (GNN). Graph algorithm (such as Mesh-TensorFlow, FlexFlow, and GPip) and machine learning (including RL methods) are two mainstream parallel search methods for strategies and they are orthogonal and have different advantages. Graph algorithm methods have less expensive search costs than RL methods, but they need to pay attention to the training details and have to weigh the communication, computation, memory and throughput costs of each tensor and operation. RL methods used a data-driven approach as a search strategy and used rewards to guide learning. Therefore, RL methods cost more than graph algorithm methods, but they do not need to focus on the details of parallel training and can automate the scheduling of executions on arbitrary computational graphs. Although these methods have increased the upper bounds of the performance optimization, there are still some problems to be improved upon:1. Existing distributed parallel techniques are mainly guided by runtime or communication. The distributed training performance evaluation has a single dimension, which cannot describe the distributed training performance of large-scale learning models in a fine-grained manner. 2. The parallel strategy search process relies on the real distributed environment, which is expensive (usually takes several hours or even days). 3. Hierarchical and Placeto use the policy gradient method to update the reinforcement learning algorithm with a large variance and low sampling efficiency, which is conducive to algorithm convergence. In response to the aforementioned problems, this paper proposes Trinity, a deep network adaptive distributed parallel training method based on reinforcement learning, to solve the problem of optimizing large-scale complex neural network partition and schedule strategies. Our contributions are as follows:  
1. We qualitatively analyze the characteristics of deep learning large models and establish quantitative evaluation models. In this paper, a quantitative evaluation model is used to describe the execution performance of different distributed parallel strategies under multi-dimensional attributes (such as parameters, samples, operators, etc. ), to guide the automatic search and tuning methods of distributed parallel strategies;
2. We divided the operators into groups according to their attributes to determine the degree of parallelism and used Node2 vec to embed the operations.