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7.8 Choosing a Framework

Framework selection builds on our understanding of framework specialization across computing environments. Engineers must evaluate three interdependent factors when choosing a framework: model requirements, hardware constraints, and software dependencies. The TensorFlow ecosystem demonstrates how these factors shape framework design through its variants: TensorFlow, TensorFlow Lite, and TensorFlow Lite Micro.

Figure 7.11 illustrates key differences between TensorFlow variants. Each variant represents specific trade-offs between computational capability and resource requirements. These trade-offs manifest in supported operations, binary size, and integration requirements.

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7.8.2 Software Dependencies

Figure 7.12 reveals three key software considerations that differentiate TensorFlow variants: operating system requirements, memory management capabilities, and accelerator support. These differences reflect each variant’s optimization for specific deployment environments.

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7.8.3 Hardware Constraints

Figure 7.13 quantifies the hardware requirements across TensorFlow variants through three metrics: base binary size, memory footprint, and processor architecture support. These metrics demonstrate the progressive optimization for constrained computing environments.

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