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Distributed Deep Learning Platform

Description

Caicloud's distributed deep learning platform utilizes the updated containerized cloud computing technology to support the latest machine learning and deep learning algorithm framework to gain insights from massive enterprise data, hence the transformation of enterprise hardware resources and human resources into computing power. It aims to provide a one-stop experience from data processing, model training to service hosting.

Advantages of Solution

One-stop

One-stop experience in code writing, data storage, data processing, model training, model repository and model hosting.

Efficiency

Efficiently manage and allocate hardware resources, support unified scheduling of model training and model services to gain speedy experience.

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Ease of Use

Easy to operate, fast to get started, users do not need operation and maintenance and management software and hardware configuration, graphically construct complex deep learning applications.

Open Source Standards

Lead the cloud native distributed machine learning trend, establish industry standards, and build an enterprise-level AI platform.

Support Multi-frameworks

Support multiple deep learning and machine learning frameworks such as TensorFlow, PyTorch, Caffe, and MXNet.

Deployment Architecture

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Application Scenario

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High-performance Computing Cluster

Implement a large number of high-performance servers to implement computing resource management, including resource allocation, task scheduling, elastic scaling, and resource monitoring.

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Image and Video Computing and Processing

Implement multi-GPU for large-scale distributed computing, speed up model training and iteration.

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Time Series Prediction Modeling

Easy to operate, fast to get started, users do not need operation and maintenance and management software and hardware configuration, graphically construct complex deep learning applications.

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Customer's Benefits

Cost Saving

Through reasonable planning and management of physical resources, resource utilization is improved and resource costs are decreased. Help AI engineers get rid of cumbersome environment building and resource allocation work, thus focusing on data and models.

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Improve Efficiency

Through end-to-end AI solutions: business needs - product design - data collection - data processing - model training - model services - product validation - business realization, and eventually improve AI engineer efficiency.

Output Value

Through AI project process management, it can precipitate AI engineer engineering experience, accelerate model training, and quickly help enterprises realize industrial intelligent upgrade.