# okto_version: "1.2"
PROJECT "MonitorFullExample"
DESCRIPTION "Demonstrates complete MONITOR block with all metrics"

ENV {
  accelerator: "gpu"
  min_memory: "16GB"
  precision: "fp16"
}

DATASET {
  train: "examples/datasets/demo_train.jsonl"
  validation: "examples/datasets/demo_train.jsonl"
  format: "jsonl"
  type: "chat"
}

MODEL {
  name: "monitor-full-model"
  base: "oktoseek/base-mini"
  device: "cuda"
}

TRAIN {
  epochs: 10
  batch_size: 32
  learning_rate: 0.0001
  device: "cuda"
}

MONITOR {
  metrics: [
    "loss",
    "val_loss",
    "accuracy",
    "val_accuracy",
    "precision",
    "recall",
    "f1_score",
    "perplexity",
    "confidence",
    "hallucination_score"
  ]
  
  notify_if {
    loss > 2.0
    gpu_usage > 90%
    gpu_temperature > 85
    val_loss > 2.5
    hallucination_score > 0.5
  }
  
  log_system: [
    "gpu_usage",
    "gpu_memory_used",
    "gpu_memory_free",
    "gpu_temperature",
    "cpu_usage",
    "ram_usage"
  ]
  
  log_speed: [
    "tokens_per_second",
    "samples_per_second",
    "throughput",
    "latency",
    "step_time"
  ]
  
  refresh_interval: 2s
  export_to: "runs/logs/system.json"
  dashboard: true
  log_to: "logs/training.log"
}

CONTROL {
  on_epoch_end {
    IF gpu_temperature > 85 {
      SET batch_size = 16
      LOG "GPU temperature high, reducing batch size"
    }
  }
}

EXPORT {
  format: ["okm"]
  path: "export/"
}