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Performance Optimization

Transfer Learning is designed to efficiently process videos and generate guides, but there are several ways to optimize its performance for your specific use case. This page covers techniques to improve processing speed, reduce resource usage, and handle large videos.

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Batch Processing

Transfer Learning uses batch processing to efficiently handle frame analysis. You can adjust batch settings to optimize performance:

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Batch Size

The batch size determines how many frames are processed in a single batch: 
# Process 20 frames per batch
transfer-learning process-video video.mp4 --batch-size 20
  • Smaller batch sizes: Lower memory usage, but more overhead
  • Larger batch sizes: Higher memory usage, but less overhead

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Concurrent Batches

The number of concurrent batches determines how many batches are processed simultaneously: 
# Process 4 batches concurrently
transfer-learning process-video video.mp4 --max-concurrent 4
  • Fewer concurrent batches: Lower CPU/GPU usage, but slower processing
  • More concurrent batches: Higher CPU/GPU usage, but faster processing

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Frame Extraction

Optimizing frame extraction can significantly improve performance:

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Frame Interval

The frame interval determines how frequently frames are extracted from the video: 
# Extract frames every 60 seconds
transfer-learning process-video video.mp4 --frame-interval 60
  • Shorter intervals: More detailed analysis, but more processing time
  • Longer intervals: Less detailed analysis, but faster processing

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Selective Extraction

For advanced use cases, you can implement selective frame extraction: 
# Extract frames only when there are significant changes
transfer-learning process-video video.mp4 --extraction-mode scene-change

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Model Selection

The choice of AI models affects both quality and performance:

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OpenAI Models

For guide generation, you can choose different OpenAI models: 
# Use a faster model
transfer-learning generate-guide data/video --model gpt-3.5-turbo

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Whisper Models

For transcription, you can choose different Whisper models: 
# Use a smaller, faster model
transfer-learning transcribe video.mp4 --model tiny

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Hardware Acceleration

Transfer Learning can leverage hardware acceleration for certain operations:

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GPU Acceleration

For transcription, you can use GPU acceleration: 
# Use CUDA for transcription
transfer-learning transcribe video.mp4 --device cuda

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CPU Optimization

You can optimize CPU usage by adjusting the number of worker processes: 
# Use 8 worker processes
transfer-learning process-video video.mp4 --workers 8

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Caching

Transfer Learning uses caching to avoid redundant processing:

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Cache Configuration

You can configure caching behavior: 
CACHE_DIR=.cache/
CACHE_TTL=86400  # Cache time-to-live in seconds

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Skip Cache

For certain operations, you can skip the cache: 
# Force regeneration without using cache
transfer-learning generate-guide data/video --skip-cache

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Memory Management

For large videos or systems with limited memory:

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Memory Limits

You can set memory limits for processing: 
MAX_MEMORY_PERCENT=80  # Use up to 80% of available memory

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Streaming Processing

For very large videos, use streaming processing: 
# Process video in streaming mode
transfer-learning process-video large-video.mp4 --streaming

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Optimization Profiles

Transfer Learning includes predefined optimization profiles:

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Fast Profile

Optimized for speed at the cost of some quality: 
# Use the fast profile
transfer-learning process-video video.mp4 --profile fast
Settings:
  • Larger frame interval (60s)
  • Larger batch size (20)
  • More concurrent batches (8)
  • Faster models

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Quality Profile

Optimized for quality at the cost of speed: 
# Use the quality profile
transfer-learning process-video video.mp4 --profile quality
Settings:
  • Smaller frame interval (10s)
  • Smaller batch size (5)
  • Fewer concurrent batches (2)
  • Higher-quality models

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Balanced Profile

Balanced speed and quality (default): 
# Use the balanced profile
transfer-learning process-video video.mp4 --profile balanced

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Distributed Processing

For very large workloads, you can distribute processing across multiple machines:

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Worker Configuration

Configure worker nodes: 
WORKER_MODE=True
COORDINATOR_URL=http://coordinator-host:8000

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Coordinator Configuration

Configure the coordinator node: 
COORDINATOR_MODE=True
LISTEN_PORT=8000

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Monitoring Performance

Use the built-in monitoring tools to identify bottlenecks: 
# View performance metrics
transfer-learning metrics --type performance

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Optimization Checklist

  • Adjust batch size and concurrency based on your hardware
  • Choose appropriate frame interval for your video content
  • Select models that balance quality and speed
  • Enable hardware acceleration if available
  • Configure caching appropriately
  • Monitor performance metrics to identify bottlenecks
  • Use optimization profiles for common scenarios
  • Consider distributed processing for large workloads