mirror of
https://github.com/chrislusf/seaweedfs
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814e0bb233
🚀 Transform SeaweedFS ML optimizations from hard-coded framework-specific code
to a flexible, configuration-driven system using YAML/JSON rules and templates.
## Key Innovations:
- Rule-based optimization engine with conditions and actions
- Plugin system for framework detection (PyTorch, TensorFlow)
- Configuration manager with YAML/JSON support
- Adaptive learning from usage patterns
- Template-based optimization recipes
## New Components:
- optimization_engine.go: Core rule evaluation and application
- config_manager.go: Configuration loading and validation
- plugins/pytorch_plugin.go: PyTorch-specific optimizations
- plugins/tensorflow_plugin.go: TensorFlow-specific optimizations
- examples/: Sample configuration files and documentation
## Benefits:
- Zero-code customization through configuration files
- Support for any ML framework via plugins
- Intelligent adaptation based on workload patterns
- Production-ready with comprehensive error handling
- Backward compatible with existing optimizations
This replaces hard-coded optimization logic with a flexible system that can
adapt to new frameworks and workload patterns without code changes.
283 lines
8.1 KiB
YAML
283 lines
8.1 KiB
YAML
# Custom ML Optimization Configuration
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# This configuration demonstrates the flexible, recipe-based optimization system
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version: "1.0.0"
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name: "Custom ML Optimization Configuration"
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description: "Production-ready configuration for diverse ML workloads"
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author: "ML Infrastructure Team"
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tags: ["production", "custom", "ml", "multi-framework"]
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# Global optimization settings
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settings:
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default_strategy: "adaptive"
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max_concurrent_rules: 8
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confidence_threshold: 0.65
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adaptive_learning: true
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metrics_collection: true
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debug: false
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memory_limit_mb: 1024
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cpu_limit_percent: 15
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experimental_features:
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neural_optimization: false
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predictive_caching: true
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multi_tier_storage: true
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# Custom optimization rules
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rules:
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- id: "large_model_chunked_loading"
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name: "Large Model Chunked Loading"
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description: "Optimize loading for models larger than 1GB using chunked approach"
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priority: 100
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conditions:
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- type: "file_context"
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property: "type"
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operator: "equals"
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value: "model"
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weight: 1.0
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- type: "file_context"
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property: "size"
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operator: "greater_than"
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value: 1073741824 # 1GB
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weight: 0.9
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actions:
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- type: "chunked_load"
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target: "file"
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parameters:
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chunk_size: 134217728 # 128MB chunks
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parallel_chunks: 4
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memory_mapping: true
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lazy_loading: true
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compression: false
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- id: "training_data_pipeline_optimization"
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name: "Training Data Pipeline Optimization"
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description: "Optimized data pipeline for training workloads"
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priority: 95
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conditions:
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- type: "workload_context"
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property: "workload_type"
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operator: "equals"
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value: "training"
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weight: 1.0
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- type: "access_pattern"
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property: "pattern_type"
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operator: "in"
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value: ["sequential", "strided", "batch"]
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weight: 0.8
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- type: "file_context"
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property: "type"
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operator: "equals"
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value: "dataset"
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weight: 0.9
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actions:
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- type: "data_pipeline"
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target: "dataset"
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parameters:
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prefetch_buffer: 16
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parallel_reads: 8
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shuffle_buffer: 10000
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cache_dataset: true
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compression_aware: true
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- id: "inference_latency_optimization"
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name: "Inference Latency Optimization"
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description: "Low-latency optimizations for real-time inference"
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priority: 90
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conditions:
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- type: "workload_context"
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property: "workload_type"
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operator: "equals"
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value: "inference"
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weight: 1.0
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- type: "workload_context"
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property: "batch_size"
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operator: "less_equal"
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value: 8
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weight: 0.7
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actions:
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- type: "inference_optimization"
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target: "model"
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parameters:
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preload_model: true
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memory_pool: true
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batch_optimization: false
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warm_up_iterations: 5
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precision: "fp16"
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- id: "distributed_training_coordination"
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name: "Distributed Training Coordination"
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description: "Coordinate file access across distributed training nodes"
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priority: 85
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conditions:
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- type: "system_context"
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property: "gpu_count"
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operator: "greater_than"
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value: 4
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weight: 0.8
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- type: "workload_context"
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property: "workload_type"
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operator: "equals"
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value: "training"
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weight: 1.0
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actions:
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- type: "distributed_coordination"
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target: "workload"
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parameters:
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node_awareness: true
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data_locality: true
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gradient_sync: true
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communication_optimization: true
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- id: "gpu_memory_aware_caching"
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name: "GPU Memory Aware Caching"
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description: "Cache optimization considering available GPU memory"
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priority: 80
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conditions:
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- type: "system_context"
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property: "gpu_count"
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operator: "greater_than"
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value: 0
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weight: 0.9
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- type: "system_context"
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property: "available_memory"
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operator: "greater_than"
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value: 8589934592 # 8GB
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weight: 0.6
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actions:
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- type: "gpu_aware_cache"
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target: "file"
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parameters:
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gpu_memory_threshold: 0.7 # Use up to 70% of GPU memory
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cpu_gpu_coordination: true
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unified_memory: false
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cache_priority: "gpu_first"
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# Optimization templates for different use cases
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templates:
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- id: "research_experimentation"
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name: "Research & Experimentation Template"
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description: "Flexible template for ML research with adaptive optimizations"
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category: "research"
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rules:
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- "large_model_chunked_loading"
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- "training_data_pipeline_optimization"
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- "gpu_memory_aware_caching"
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parameters:
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optimization_level: "adaptive"
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experiment_tracking: true
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resource_monitoring: true
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flexible_caching: true
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- id: "production_training"
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name: "Production Training Template"
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description: "High-performance template for production ML training"
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category: "production_training"
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rules:
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- "training_data_pipeline_optimization"
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- "distributed_training_coordination"
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- "gpu_memory_aware_caching"
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- "large_model_chunked_loading"
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parameters:
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optimization_level: "maximum"
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fault_tolerance: true
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checkpoint_optimization: true
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monitoring: "comprehensive"
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- id: "real_time_inference"
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name: "Real-time Inference Template"
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description: "Ultra-low latency template for real-time ML inference"
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category: "inference"
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rules:
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- "inference_latency_optimization"
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- "gpu_memory_aware_caching"
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parameters:
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optimization_level: "latency"
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batch_processing: false
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memory_pool: true
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warm_up: true
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- id: "batch_inference"
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name: "Batch Inference Template"
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description: "Throughput-optimized template for batch inference workloads"
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category: "batch_inference"
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rules:
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- "large_model_chunked_loading"
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- "gpu_memory_aware_caching"
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- "training_data_pipeline_optimization" # Reuse for batch data processing
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parameters:
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optimization_level: "throughput"
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batch_processing: true
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parallel_inference: true
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queue_management: true
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# Framework-specific configurations
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frameworks:
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pytorch:
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enabled: true
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version: "2.0+"
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rules:
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- "large_model_chunked_loading"
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- "training_data_pipeline_optimization"
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- "gpu_memory_aware_caching"
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parameters:
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dataloader_optimization: true
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tensor_parallelism: true
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gradient_compression: true
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mixed_precision: true
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compile_optimization: true
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tensorflow:
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enabled: true
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version: "2.10+"
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rules:
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- "training_data_pipeline_optimization"
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- "distributed_training_coordination"
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- "inference_latency_optimization"
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parameters:
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dataset_optimization: true
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xla_compilation: true
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mixed_precision: true
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tensorrt_optimization: true
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savedmodel_optimization: true
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huggingface:
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enabled: true
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rules:
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- "large_model_chunked_loading"
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- "inference_latency_optimization"
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parameters:
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transformer_optimization: true
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model_parallelism: true
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attention_optimization: true
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tokenizer_caching: true
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jax:
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enabled: true
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rules:
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- "distributed_training_coordination"
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- "gpu_memory_aware_caching"
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parameters:
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jit_compilation: true
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device_parallelism: true
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gradient_transformation: true
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# Custom metadata for configuration management
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metadata:
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config_version: "1.0.0"
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created_by: "ML Infrastructure Team"
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last_updated: "2024-01-15"
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compatible_with: ["seaweedfs-ml-v1", "seaweedfs-ml-v2"]
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environment: "production"
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regions: ["us-west-2", "eu-west-1"]
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gpu_types: ["V100", "A100", "H100"]
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use_cases:
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- "large_language_models"
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- "computer_vision"
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- "recommendation_systems"
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- "time_series_forecasting"
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- "reinforcement_learning"
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performance_targets:
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training_throughput: "high"
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inference_latency: "low"
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resource_efficiency: "optimal"
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scalability: "horizontal"
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