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tf-torch-cpu_1.26.1
  • Quantization User Guide
    • Use Cases
    • AIMET Quantization Features
    • AIMET Quantization Workflow
    • Debugging Guidelines
  • Compression User Guide
    • Overview
    • Use Case
    • Compression ratio selection
      • Visualization
        • Overview
        • Design
        • Compression
        • Starting a Bokeh Server Session:
        • How to use the tool
    • Model Compression
      • Weight SVD
      • Spatial SVD
      • Channel Pruning
        • Overall Procedure
        • Channel Selection
        • Winnowing
        • Weight Reconstruction
    • Optional techniques to get better compression results
      • Rank Rounding
      • Per-layer Fine-tuning
    • FAQs
    • References
  • API Documentation
    • AIMET APIs for PyTorch
      • PyTorch Model Quantization API
      • PyTorch Model Compression API
        • Introduction
        • Top-level API for Compression
        • Greedy Selection Parameters
        • TAR Selection Parameters
        • Spatial SVD Configuration
        • Weight SVD Configuration
        • Channel Pruning Configuration
        • Configuration Definitions
        • Code Examples
      • PyTorch Model Visualization API for Compression
        • Top-level API Compression
        • Code Examples
      • PyTorch Model Visualization API for Quantization
        • Top-level API Quantization
        • Code Examples
    • AIMET APIs for TensorFlow
      • TensorFlow Model Guidelines
      • TensorFlow Model Quantization API
      • TensorFlow Model Compression API
        • Introduction
        • Top-level API for Compression
        • Greedy Selection Parameters
        • Spatial SVD Configuration
        • Channel Pruning Configuration
        • Configuration Definitions
        • Code Examples
        • Weight SVD Top-level API
        • Code Examples for Weight SVD
      • TensorFlow Model Visualization API for Quantization
        • Top-level API for Visualization of Weight tensors
        • Code Examples for Visualization of Weight tensors
      • Using AIMET Tensorflow APIs with Keras Models
        • Introduction
        • APIs
        • Code Example
        • Utility Functions
    • AIMET APIs for Keras
      • Keras Model Quantization API
    • AIMET APIs for ONNX
      • ONNX Model Quantization API
    • Indices and tables
  • Examples Documentation
    • Browse the notebooks
    • Running the notebooks
      • Install Jupyter
      • Download the Example notebooks and related code
      • Run the notebooks
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