Attenuate Class Imbalance Problem for Pneumonia Diagnosis Using Ensemble Parallel Stacked Pre-Trained Models

Ravikumar, Aswathy; Sriraman, Harini

doi:10.32604/cmc.2023.035848

Cited by 4 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The single-node and multi-node acceleration on CNN and LSTM was deeply analyzed for different use cases but was not expanded to GAN [36], [37]. The impact of the multinode on spark and GPU was examined for medical use cases [35], [38]. The impact of multi-node TPU on GAN for double precision was developed but lacked deployment and did not address the current bottleneck issues in TPU [34].…”

Section: Key Takeawaysmentioning

confidence: 99%

Computationally Efficient Neural Rendering for Generator Adversarial Networks Using a Multi-GPU Cluster in a Cloud Environment

Ravikumar

Sriraman

2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

Due to its fantastic performance in the quality of the images created, Generator Adversarial Networks have recently become a viable option for image reconstruction. The main problem with employing GAN is how expensive the computations are. Researchers have developed techniques for distributing GANs across multiple nodes. However, these techniques typically do not scale because they frequently separate the components (Discriminator and Generator), leading to high communication overhead or encountering distribution-related problems unique to GAN training. In this study, the training procedure for the GAN is parallelized and carried out over many Graphical Processing Units (GPUs). TensorFlow's built-in logic and a custom loop were tweaked for more control over the resources allotted to each GPU worker. In this study, GPU image processing improvements and multi-GPU learning are used. The GAN model is accelerated using Distributed TensorFlow with synchronous data-parallel training on a single system and several GPUs. Acceleration was accomplished using the Genesis Cloud Platform and the NVIDIA®GeForceTM GTX 108 GPU accelerator. The speed-up of 1.322 for two GPUs, 1.688 for three GPUs, and 1.7792 for four GPUs using multi-GPU acceleration. The parameter server model's data initialization and image production bottlenecks are removed, but the results' speed-up is not linear. Increasing the number of GPUs and removing the connectivity constraint will accelerate things even more. The bottlenecks are detected using new network lines and resources, and solutions are suggested. Recomputation and quantization are the two techniques to reduce the amount of GPU acceleration in memory. Deployment and versioning are essential for successfully operating multi-node GAN models in MLflow. Properly deploying and versioning these models can improve scalability, reproducibility, and collaboration across teams working on the same model. MLflow provides built-in tools for versioning and tracking model performance, making it easier to manage multiple versions of the model and reproduce it in different environments.

show abstract

Section: Key Takeawaysmentioning

confidence: 99%

Computationally Efficient Neural Rendering for Generator Adversarial Networks Using a Multi-GPU Cluster in a Cloud Environment

Ravikumar

Sriraman

2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

Enhancing Zero-Day Attack Detection with XAI-Driven ML Models and SMOTE Analysis

Sruthi,

Ravikumar,

Sriraman

2024

2024 3rd International Conference on Artificial Intelligence for Internet of Things (AIIoT)

View full text Add to dashboard Cite

Evaluation of the Distributed Strategies for Data Parallel Deep Learning Model in TensorFlow

Ravikumar,

Sriraman

2023

Scalable and Distributed Machine Learning and Deep Learning Patterns

View full text Add to dashboard Cite

Distributed deep learning is a branch of machine intelligence in which the runtime of deep learning models may be dramatically lowered by using several accelerators. Most of the past research reports the performance of the data parallelism technique of DDL. Nevertheless, additional parallelism solutions in DDL must be investigated, and their performance modeling for specific applications and application stacks must be reported. Such efforts may aid other researchers in making more informed judgments while creating a successful DDL algorithm. Distributed deep learning strategies are becoming increasingly popular as they allow for training complex models on large datasets in a much shorter time than traditional training methods. TensorFlow, a popular open-source framework for building and training machine learning models, provides several distributed training strategies. This chapter provides a detailed evaluation of the different TensorFlow strategies for medical data. The TensorFlow distribution strategy API is utilized to perform distributed training in TensorFlow.

show abstract

Attenuate Class Imbalance Problem for Pneumonia Diagnosis Using Ensemble Parallel Stacked Pre-Trained Models

Cited by 4 publications

References 43 publications

Computationally Efficient Neural Rendering for Generator Adversarial Networks Using a Multi-GPU Cluster in a Cloud Environment

Computationally Efficient Neural Rendering for Generator Adversarial Networks Using a Multi-GPU Cluster in a Cloud Environment

Enhancing Zero-Day Attack Detection with XAI-Driven ML Models and SMOTE Analysis

Evaluation of the Distributed Strategies for Data Parallel Deep Learning Model in TensorFlow

Contact Info

Product

Resources

About