d95e205d4b
Eliminate the entire mixed_precision runtime indirection layer: - Delete crates/ml-core/src/mixed_precision.rs (training_dtype, ensure_training_dtype, align_dim_for_tensor_cores) - Inline ~100 call sites across 130 files to constants: training_dtype(&device) → candle_core::DType::BF16 ensure_training_dtype(x) → x.to_dtype(candle_core::DType::BF16) align_dim_for_tensor_cores(x, &device) → (x + 7) & !7 - Remove re-exports from ml-dqn, ml-supervised, ml lib.rs - Clean config/toml/json/shell references No CPU/Metal training path exists — BF16 is the only dtype. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
148 lines
6.5 KiB
TOML
148 lines
6.5 KiB
TOML
# ML Training Configuration
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# Centralized configuration for all training parameters
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[training]
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# Basic training parameters
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total_episodes = 10000 # Total training episodes
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steps_per_episode = 1000 # Steps per episode
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batch_size = 32 # Training batch size
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replay_buffer_size = 100000 # Experience replay buffer size
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target_update_frequency = 1000 # Target network update frequency
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checkpoint_frequency = 500 # Model checkpointing frequency
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evaluation_frequency = 100 # Evaluation frequency
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target_performance_threshold = 0.8 # Performance threshold for convergence
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episodes_per_checkpoint = 500 # Episodes per checkpoint
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adversarial_training_frequency = 1000 # Adversarial training frequency
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[learning_rate]
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# Learning rate scheduling
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schedule_type = "exponential_decay" # constant, linear_decay, exponential_decay, step_decay
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initial = 0.001 # Initial learning rate
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decay_rate = 0.995 # Decay rate (for exponential)
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decay_steps = 1000 # Decay steps
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final_rate = 0.0001 # Final rate (for linear decay)
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decay_factor = 0.5 # Decay factor (for step decay)
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step_size = 1000 # Step size (for step decay)
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[exploration]
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# Exploration scheduling
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schedule_type = "exponential_decay" # linear_decay, exponential_decay, curriculum_based
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initial = 1.0 # Initial exploration rate
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decay_rate = 0.995 # Decay rate (for exponential)
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final_rate = 0.01 # Final rate (for linear decay)
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decay_steps = 5000 # Decay steps (for linear decay)
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base_rate = 0.1 # Base rate (for curriculum based)
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complexity_factor = 0.1 # Complexity factor (for curriculum based)
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[coordination]
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# Multi-agent coordination settings
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enable_experience_sharing = true # Enable experience sharing between agents
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enable_centralized_critic = true # Enable centralized critic
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coordination_frequency = 10 # Coordination update frequency
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rebalancing_frequency = 100 # Resource allocation rebalancing frequency
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[environment]
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# Training environment parameters
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available_capital = 1000000.0 # Available capital for simulation
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market_features = 5 # Number of market features
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volatility_base = 0.02 # Base market volatility
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volume_base = 0.1 # Base market volume
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spread_base = 0.3 # Base market spread
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var_threshold = 0.015 # VaR threshold
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max_drawdown_limit = 0.0 # Maximum drawdown limit
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[reward_simulation]
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# Reward simulation parameters
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reward_range_min = -0.05 # Minimum reward
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reward_range_max = 0.05 # Maximum reward
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financial_reward_max = 0.1 # Maximum financial reward
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execution_quality_max = 0.05 # Maximum execution quality reward
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risk_adjusted_max = 0.08 # Maximum risk-adjusted reward
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cooperation_reward = 0.0 # Base cooperation reward
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exploration_reward = 0.0 # Base exploration reward
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[adversarial]
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# Adversarial training scenarios
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high_volatility_var = 0.25 # High volatility scenario VaR
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high_volatility_drawdown = 0.15 # High volatility max drawdown
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news_event_var = 0.40 # News event scenario VaR
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news_event_drawdown = 0.25 # News event max drawdown
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pnl_scenarios = [-0.05, 0.02, -0.08, 0.15, -0.12] # P&L scenarios for high volatility
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news_pnl_scenarios = [-0.20, -0.15, -0.10, 0.05, 0.08] # P&L scenarios for news events
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[evaluation]
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# Evaluation parameters
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eval_episodes = 10 # Number of evaluation episodes
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eval_frequency = 100 # Evaluation frequency (episodes)
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convergence_window = 100 # Window for convergence detection
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performance_threshold = 0.8 # Performance threshold
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[checkpointing]
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# Model checkpointing
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checkpoint_dir = "checkpoints" # Checkpoint directory
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save_frequency = 500 # Save frequency (episodes)
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keep_checkpoints = 10 # Number of checkpoints to keep
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model_save_format = "safetensors" # Model save format
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[curriculum]
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# Curriculum learning parameters
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enable_curriculum = false # Enable curriculum learning
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initial_difficulty = 0.1 # Initial difficulty level
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max_difficulty = 1.0 # Maximum difficulty level
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difficulty_increment = 0.05 # Difficulty increment per milestone
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milestone_episodes = 1000 # Episodes per difficulty milestone
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[hyperparameter_search]
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# Hyperparameter optimization
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enable_search = false # Enable hyperparameter search
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search_algorithm = "random" # random, grid, bayesian
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max_trials = 100 # Maximum trials
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search_space = [ # Search space definition
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{ param = "learning_rate", type = "float", min = 0.0001, max = 0.01 },
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{ param = "batch_size", type = "int", min = 16, max = 128 },
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{ param = "gamma", type = "float", min = 0.9, max = 0.999 }
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]
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[data_pipeline]
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# Training data pipeline
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data_buffer_size = 1000000 # Data buffer size
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shuffle_buffer = 10000 # Shuffle buffer size
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prefetch_size = 10 # Prefetch size for data loading
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num_parallel_workers = 4 # Number of parallel data workers
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data_augmentation = false # Enable data augmentation
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[validation]
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# Training validation
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validation_split = 0.2 # Validation split ratio
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min_training_samples = 10000 # Minimum training samples
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max_dataset_age_days = 30 # Maximum dataset age in days
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cross_validation_folds = 5 # Number of cross-validation folds
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[early_stopping]
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# Early stopping parameters
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enable_early_stopping = true # Enable early stopping
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patience = 100 # Patience (episodes without improvement)
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min_delta = 0.001 # Minimum improvement threshold
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restore_best_weights = true # Restore best weights on early stop
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[distributed]
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# Distributed training
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enable_distributed = false # Enable distributed training
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num_workers = 1 # Number of distributed workers
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communication_backend = "nccl" # Communication backend
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gradient_compression = false # Enable gradient compression
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[memory_optimization]
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# Memory optimization
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gradient_checkpointing = false # Enable gradient checkpointing
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memory_efficient_attention = true # Enable memory efficient attention
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cpu_offload = false # Enable CPU offloading
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[logging]
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# Training logging
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log_frequency = 100 # Log frequency (steps)
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tensorboard_log_dir = "logs/tensorboard" # TensorBoard log directory
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wandb_project = "foxhunt-ml" # Weights & Biases project name
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wandb_entity = "foxhunt" # Weights & Biases entity
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log_gradients = false # Log gradient histograms
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log_weights = false # Log weight histograms |