###### Abstract
[13] developed a cloud computing-based platform for a distributed Deep Neural Network (DNN) to tackle the communication bottleneck challenge, which happens throughout data-parallel Stochastic Gradient Descent (SGD). The primary methods considerably lower computation time: nevertheless, since they do not have the fault tolerance feature, the complete training process would be disturbed if the GPU node failed during execution. Moreover, [PERSON] et al. [14] introduced the parallelized DNN model for video data analysis and provided fault tolerance using the MapReduce Hadoop framework [15; 16]. The Hadoop framework, named MapReduce, famous for big data analytics, includes built-in fault tolerance. However, the performance of the computational model based on Hadoop is limited because of the Hadoop framework's high I/O latency [17]. Furthermore, due to the Hadoop framework I/O delays, its usability for the applications of iterative processing, like machine learning methods, has been limited. In this study, we used distributed computing models to classify AIPs sequences using a proposed scalable, fault-tolerant, parallelized multilayer DNN model. Spark framework [18] is used to develop the proposed model, a prominent data-related computing method that employs cluster processing nodes. In contrast to Hadoop, Spark offers in-memory computation, which allows data to be kept and processed in distributed memory during the map and reduce phases. This functionality lowers the I/O latency Spark framework, producing it a superior computational framework for machine learning approaches. Furthermore, the proposed model used parallelizing data and implicit coding approaches, considerably lowering DNN training time. In this context, the salient features of our work are summarized below:  
* We proposed a scalable distributed deep generative model to classify AIPs and non-AIP sequences from large-scale peptide sequences. * The proposed model considers the dataset's non-linearity by utilizing multi-stack processing layers and a non-linear activation function. * The proposed model efficiently distributes data (i.e., model code) and computation across several processing nodes utilizing Spark to obtain massive parallelism. Furthermore, the proposed model incorporates fault-tolerance and scalability characteristics to create a robust system. * The proposed model performance is extensively measured employing various performance measurement metrics, including scalability, speedup, accuracy, and execution time. Spark cluster computing has been used to develop the proposed parallel model. Furthermore, the proposed model employs several feature extraction approaches to obtain the best features. Lastly, different computational and statistical-based metrics are utilized to analyze the proposed scalable and parallel model. We used Sensitivity (SN), Specificity (SP), Accuracy (ACC), and Matthew's correlation coefficient (MCC) to analyze statistical-based measurement metrics. In contrast, we used efficiency in terms of scalability, speedup, and computational times to evaluate computational-based measurement metrics. The following structure is this paper's remainder. Section 2 describes the deep neural network background. Section 3 describes the proposed model structure (i.e., architecture) and implementation. Section 4 describes the performance assessment and experimental outcomes. Section 5 includes a discussion.