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The Case for Centralized AI Model Inference Serving
- AI models are increasingly being used in algorithmic pipelines, leading to different resource requirements compared to traditional algorithms.
- Efficiently processing large-scale inputs with deep learning models can be challenging within these pipelines.
- Centralized inference serving, where a dedicated server handles prediction requests from parallel jobs, is proposed as a solution.
- An experiment comparing decentralized and centralized inference approaches using a ResNet-152 image classifier on 1,000 images is conducted.
- The experiment focuses on Python multiprocessing for parallel processing on a single node.
- Centralized inference using a dedicated server showed improved performance and resource utilization compared to decentralized inference.
- Further enhancements and optimizations can be made, including custom inference handlers, advanced server configurations, and model optimization.
- Batch inference and multi-worker inference strategies are explored to improve throughput and resource utilization.
- Results show that utilizing an inference server can significantly boost overall throughput and efficiency in deep learning workloads.
- Optimizing AI model execution involves designing efficient inference serving architectures and considering various model optimization techniques.
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