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Description
                    Originally created by Uber, Horovod aims to simplify and accelerate the process of distributed deep learning, significantly reducing model training durations from several days or weeks to mere hours or even minutes. By utilizing Horovod, users can effortlessly scale their existing training scripts to leverage the power of hundreds of GPUs with just a few lines of Python code. It offers flexibility for deployment, as it can be installed on local servers or seamlessly operated in various cloud environments such as AWS, Azure, and Databricks. In addition, Horovod is compatible with Apache Spark, allowing a cohesive integration of data processing and model training into one streamlined pipeline. Once set up, the infrastructure provided by Horovod supports model training across any framework, facilitating easy transitions between TensorFlow, PyTorch, MXNet, and potential future frameworks as the landscape of machine learning technologies continues to progress. This adaptability ensures that users can keep pace with the rapid advancements in the field without being locked into a single technology.
                
            
        
            Description
                    Distributed AI represents a computing approach that eliminates the necessity of transferring large data sets, enabling data analysis directly at its origin. Developed by IBM Research, the Distributed AI APIs consist of a suite of RESTful web services equipped with data and AI algorithms tailored for AI applications in hybrid cloud, edge, and distributed computing scenarios. Each API within the Distributed AI framework tackles the unique challenges associated with deploying AI technologies in such environments. Notably, these APIs do not concentrate on fundamental aspects of establishing and implementing AI workflows, such as model training or serving. Instead, developers can utilize their preferred open-source libraries like TensorFlow or PyTorch for these tasks. Afterward, you can encapsulate your application, which includes the entire AI pipeline, into containers for deployment at various distributed sites. Additionally, leveraging container orchestration tools like Kubernetes or OpenShift can greatly enhance the automation of the deployment process, ensuring efficiency and scalability in managing distributed AI applications. This innovative approach ultimately streamlines the integration of AI into diverse infrastructures, fostering smarter solutions.
                
            
        
            API Access
            
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    Flyte
            
            
        
        
    
        
        
            
                
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            Pricing Details
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                Vendor Details
Company Name
Horovod
Website
horovod.ai/
Vendor Details
Company Name
IBM
Country
United States
Website
developer.ibm.com/apis/catalog/edgeai--distributed-ai-apis/Introduction/
Product Features
Deep Learning
                                        Convolutional Neural Networks
                                        
                                    
                                    
                                    
                                        Document Classification
                                        
                                    
                                    
                                    
                                        Image Segmentation
                                        
                                    
                                    
                                    
                                        ML Algorithm Library
                                        
                                    
                                    
                                    
                                        Model Training
                                        
                                    
                                    
                                    
                                        Neural Network Modeling
                                        
                                    
                                    
                                    
                                        Self-Learning
                                        
                                    
                                    
                                    
                                        Visualization