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Performance Considerations

This section covers performance optimization and scaling considerations for Tabular SSL.

Memory Optimization

Batch Processing

  1. Dynamic Batch Sizes 

    from tabular_ssl import TabularSSL

model = TabularSSL(
    input_dim=10,
    batch_size=32,  # Adjust based on available memory
    gradient_accumulation_steps=4  # Accumulate gradients
)

  2. Memory-Efficient Attention 

    model = TabularSSL(
    input_dim=10,
    attention_type='memory_efficient',  # Use memory-efficient attention
    chunk_size=64  # Process attention in chunks
)

Model Optimization

  1. Parameter Sharing 

    model = TabularSSL(
    input_dim=10,
    share_parameters=True,  # Share parameters across layers
    parameter_efficiency=True  # Use parameter-efficient methods
)

  2. Quantization 

    from tabular_ssl.utils import quantize_model

# Quantize model to reduce memory usage
quantized_model = quantize_model(
    model,
    precision='int8'  # Use 8-bit quantization
)

Training Speed

Hardware Acceleration

  1. GPU Support 

    model = TabularSSL(
    input_dim=10,
    device='cuda',  # Use GPU
    mixed_precision=True  # Enable mixed precision training
)

  2. Multi-GPU Training 

    model = TabularSSL(
    input_dim=10,
    distributed=True,  # Enable distributed training
    num_gpus=4  # Use 4 GPUs
)

Optimization Techniques

  1. Efficient Data Loading 

    from tabular_ssl.data import DataLoader

loader = DataLoader(
    num_workers=4,  # Use multiple workers
    pin_memory=True,  # Pin memory for faster transfer
    prefetch_factor=2  # Prefetch data
)

  2. Cached Computations 

    model = TabularSSL(
    input_dim=10,
    cache_attention=True,  # Cache attention computations
    cache_size=1000  # Cache size
)

Scaling Considerations

Data Scaling

  1. Large Datasets 

    from tabular_ssl.data import StreamingDataLoader

# Use streaming data loader for large datasets
loader = StreamingDataLoader(
    data_path='large_dataset.csv',
    batch_size=32,
    chunk_size=10000  # Process data in chunks
)

  2. Distributed Data Processing 

    from tabular_ssl.data import DistributedDataLoader

# Use distributed data loader
loader = DistributedDataLoader(
    data_path='large_dataset.csv',
    num_workers=4,
    world_size=4  # Number of processes
)

Model Scaling

  1. Model Parallelism 

    model = TabularSSL(
    input_dim=10,
    model_parallel=True,  # Enable model parallelism
    num_devices=4  # Split model across 4 devices
)

  2. Pipeline Parallelism 

    model = TabularSSL(
    input_dim=10,
    pipeline_parallel=True,  # Enable pipeline parallelism
    num_stages=4  # Number of pipeline stages
)

Performance Monitoring

Metrics

  1. Training Metrics 

    from tabular_ssl.utils import TrainingMonitor

monitor = TrainingMonitor(
    metrics=['loss', 'accuracy', 'memory_usage'],
    log_interval=100
)

  2. System Metrics 

    from tabular_ssl.utils import SystemMonitor

monitor = SystemMonitor(
    metrics=['gpu_usage', 'memory_usage', 'throughput'],
    log_interval=1
)

Profiling

  1. Model Profiling 

    from tabular_ssl.utils import profile_model

# Profile model performance
profile = profile_model(
    model,
    input_size=(32, 10),  # Batch size, input dimension
    num_runs=100
)

  2. Memory Profiling 

    from tabular_ssl.utils import profile_memory

# Profile memory usage
memory_profile = profile_memory(
    model,
    input_size=(32, 10)
)

Best Practices

Memory Management

  1. Batch Size Selection
  2. Start with small batch sizes
  3. Gradually increase if memory allows

  4. Use gradient accumulation for large batches

  5. Model Architecture

  6. Use parameter-efficient architectures
  7. Implement memory-efficient attention
  8. Consider model quantization

Training Optimization

  1. Hardware Utilization
  2. Use GPU acceleration
  3. Enable mixed precision training

  4. Implement distributed training

  5. Data Processing

  6. Use efficient data loaders
  7. Implement data prefetching
  8. Cache frequent computations