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Abstract
Training a Deep learning model is a highly computational demanding task and requires a high-end graphics card for parallelism in training a Deep Convolutional Neural Network for numerical calculation. These production level cards are not easily accessible to everyone and are extremely costly. The paper discusses a distributed training strategy used for training a deep convolutional neural network on multiple consumer graphics cards along with the deployment architecture for inference on a SaaS application.
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