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# Code Overview
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Below is an overview of the FastDeploy code structure and functionality organized by directory.
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- ```custom_ops```: Contains C++ operators used by FastDeploy for large model inference. Operators for different hardware are placed in corresponding subdirectories (e.g., `cpu_ops`, `gpu_ops`). The root-level `setup_*.py` files are used to compile these C++ operators.
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- ```dockerfiles```: Stores Dockerfiles for building FastDeploy runtime environment images.
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- ```docs```: Documentation related to the FastDeploy codebase.
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- ```fastdeploy```
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- ```agent```: Scripts for launching large model services.
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- ```cache_manager```: Cache management module for large models.
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- ```engine```: Core engine classes for managing large model execution.
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- ```entrypoints```: User-facing APIs for interaction.
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- ```input```: Input processing module, including preprocessing, multimodal input handling, tokenization, etc.
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- ```model_executor```
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- ```layers```: Layer modules required for large model architecture.
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- ```model_runner```: Model inference execution module.
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- ```models```: Built-in large model classes in FastDeploy.
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- ```ops```: Python-callable operator modules compiled from `custom_ops`, organized by hardware platform.
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- ```output```: Post-processing for large model outputs.
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- ```platforms```: Platform-specific modules for underlying hardware support.
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- ```scheduler```: Request scheduling module for large models.
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- ```metrics```: Core component for collecting, managing, and exporting Prometheus metrics, tracking key runtime performance data (e.g., request latency, resource utilization, successful request counts).
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- ```splitwise```: Modules related to PD disaggragation deployment.
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- ```scripts```/```tools```: Utility scripts for FastDeploy operations (e.g., compilation, unit testing, code style fixes).
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- ```test```: Code for unit testing and validation.
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