DYNAMIC RESOURCE ALLOCATION FOR DEEP LEARNING TRAINING USING TENSORFLOW ON KUBERNETES CLUSTER
Distributed deep learning training nowadays use static resource allocation. Using parameter server architecture, deep learning training is carried out by several parameter server (ps) nodes and worker nodes. Their numbers are constant while the training is running, hence static. Consider a traini...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/39082 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |