Table of Contents

Class CompressionConfig

Namespace
AiDotNet.Deployment.Configuration
Assembly
AiDotNet.dll

Configuration for model compression - reducing model size while preserving accuracy.

public class CompressionConfig
Inheritance
CompressionConfig
Inherited Members

Remarks

For Beginners: Model compression makes your trained AI model smaller and faster to load. Think of it like compressing a ZIP file - you get a smaller file that can be restored to its original form.

Why use compression?

  • Smaller model files (50-90% size reduction)
  • Faster model loading and deployment
  • Lower storage and bandwidth costs
  • Enables deployment on resource-constrained devices

Trade-offs:

  • Some compression types are lossy (slight accuracy reduction, typically 1-2%)
  • Compression/decompression adds a small processing overhead

Compression happens automatically when you save (serialize) a model and decompression happens automatically when you load (deserialize) it. You never need to handle compression manually.

Example:

// Use automatic compression (recommended for most cases)
var result = await builder
    .ConfigureModel(model)
    .ConfigureCompression()
    .BuildAsync();

// Or customize compression settings
var result = await builder
    .ConfigureCompression(new CompressionConfig
    {
        Mode = CompressionMode.Full,
        Type = CompressionType.HybridHuffmanClustering,
        NumClusters = 256
    })
    .BuildAsync();

Properties

MaxAccuracyLossPercent

Gets or sets the maximum acceptable accuracy loss percentage (default: 2.0).

public double MaxAccuracyLossPercent { get; set; }

Property Value

double

Remarks

For Beginners: If compression would cause more than this percentage of accuracy loss, the system will warn you or use a less aggressive compression. 2% is acceptable for most applications. Set to 0 for lossless compression only.

MaxIterations

Gets or sets the maximum iterations for clustering algorithms (default: 100).

public int MaxIterations { get; set; }

Property Value

int

Remarks

For Beginners: Limits how long the clustering algorithm runs. More iterations can improve cluster quality but takes longer. 100 iterations is sufficient for most models.

Mode

Gets or sets the compression mode (default: Automatic).

public ModelCompressionMode Mode { get; set; }

Property Value

ModelCompressionMode

Remarks

For Beginners: Choose how compression is applied: - None: No compression (full size, maximum accuracy) - Automatic: System chooses best approach (recommended) - WeightsOnly: Compress only model weights - Full: Compress entire serialized model

NumClusters

Gets or sets the number of clusters for weight clustering (default: 256).

public int NumClusters { get; set; }

Property Value

int

Remarks

For Beginners: This is like choosing how many "bins" to sort weights into. 256 clusters is the industry standard (equivalent to 8-bit quantization). More clusters = higher accuracy but less compression. Fewer clusters = more compression but lower accuracy.

Common values:

  • 16: Aggressive compression (4-bit equivalent)
  • 256: Standard compression (8-bit equivalent, recommended)
  • 65536: Light compression (16-bit equivalent)

Precision

Gets or sets the decimal precision for Huffman encoding (default: 4).

public int Precision { get; set; }

Property Value

int

Remarks

For Beginners: Controls how many decimal places to keep when rounding weights for Huffman encoding. Higher precision = better accuracy but less compression. 4 decimal places is a good default for most models.

RandomSeed

Gets or sets the random seed for reproducible compression (default: null for random).

public int? RandomSeed { get; set; }

Property Value

int?

Remarks

For Beginners: Set this to a specific number if you want compression to produce identical results every time. Useful for testing and debugging. Leave as null for normal usage.

Tolerance

Gets or sets the convergence tolerance for clustering algorithms (default: 1e-6).

public double Tolerance { get; set; }

Property Value

double

Remarks

For Beginners: This determines when the clustering algorithm stops iterating. Smaller values = more precise clusters but slower compression. The default (0.000001) works well for most cases.

Type

Gets or sets the compression algorithm type (default: WeightClustering).

public CompressionType Type { get; set; }

Property Value

CompressionType

Remarks

For Beginners: Different algorithms offer different trade-offs: - WeightClustering: Groups similar weights (good balance of speed and compression) - HuffmanEncoding: Lossless variable-length encoding (no accuracy loss) - HybridHuffmanClustering: Combines both for maximum compression

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