Tonic/trackio-experiments

Trackio Experiments Dataset

This dataset stores experiment tracking data for ML training runs, particularly focused on SmolLM3 fine-tuning experiments with comprehensive metrics tracking.

Dataset Structure

The dataset contains the following columns:

• —experiment_id: Unique identifier for each experiment
• —name: Human-readable name for the experiment
• —description: Detailed description of the experiment
• —created_at: Timestamp when the experiment was created
• —status: Current status (running, completed, failed, paused)
• —metrics: JSON string containing training metrics over time
• —parameters: JSON string containing experiment configuration
• —artifacts: JSON string containing experiment artifacts
• —logs: JSON string containing experiment logs
• —last_updated: Timestamp of last update

Metrics Structure

The metrics field contains JSON arrays with the following structure:

[
  {
    "timestamp": "2025-07-20T11:20:01.780908",
    "step": 25,
    "metrics": {
      "loss": 1.1659,
      "accuracy": 0.759,
      "learning_rate": 7e-08,
      "grad_norm": 10.3125,
      "epoch": 0.004851130919895701,
      
      // Advanced Training Metrics
      "total_tokens": 1642080.0,
      "truncated_tokens": 128,
      "padding_tokens": 256,
      "throughput": 3284160.0,
      "step_time": 0.5,
      "batch_size": 8,
      "seq_len": 2048,
      "token_acc": 0.759,
      
      // Custom Losses
      "train/gate_ortho": 0.0234,
      "train/center": 0.0156,
      
      // System Metrics
      "gpu_memory_allocated": 17.202261447906494,
      "gpu_memory_reserved": 75.474609375,
      "gpu_utilization": 85.2,
      "cpu_percent": 2.7,
      "memory_percent": 10.1
    }
  }
]

Supported Metrics

Core Training Metrics

• —loss: Training loss value
• —accuracy: Model accuracy
• —learning_rate: Current learning rate
• —grad_norm: Gradient norm
• —epoch: Current epoch progress

Advanced Token Metrics

• —total_tokens: Total tokens processed in the batch
• —truncated_tokens: Number of tokens truncated during processing
• —padding_tokens: Number of padding tokens added
• —throughput: Tokens processed per second
• —step_time: Time taken for the current training step
• —batch_size: Current batch size
• —seq_len: Sequence length
• —token_acc: Token-level accuracy

Custom Losses (SmolLM3-specific)

• —train/gate_ortho: Gate orthogonality loss
• —train/center: Center loss component

System Performance Metrics

• —gpu_memory_allocated: GPU memory currently allocated (GB)
• —gpu_memory_reserved: GPU memory reserved (GB)
• —gpu_utilization: GPU utilization percentage
• —cpu_percent: CPU usage percentage
• —memory_percent: System memory usage percentage

Usage

This dataset is automatically used by the Trackio monitoring system to store and retrieve experiment data. It provides persistent storage for experiment tracking across different training runs.

Integration

The dataset is used by:

• —Trackio Spaces for experiment visualization
• —Training scripts for logging metrics and parameters
• —Monitoring systems for experiment tracking
• —SmolLM3 fine-tuning pipeline for comprehensive metrics capture

Privacy

This dataset is private by default to ensure experiment data security. Only users with appropriate permissions can access the data.

Examples

Sample Experiment Entry

{
  "experiment_id": "exp_20250720_130853",
  "name": "smollm3_finetune",
  "description": "SmolLM3 fine-tuning experiment with comprehensive metrics",
  "created_at": "2025-07-20T11:20:01.780908",
  "status": "running",
  "metrics": "[{\"timestamp\": \"2025-07-20T11:20:01.780908\", \"step\": 25, \"metrics\": {\"loss\": 1.1659, \"accuracy\": 0.759, \"total_tokens\": 1642080.0, \"throughput\": 3284160.0, \"train/gate_ortho\": 0.0234, \"train/center\": 0.0156}}]",
  "parameters": "{\"model_name\": \"HuggingFaceTB/SmolLM3-3B\", \"batch_size\": 8, \"learning_rate\": 3.5e-06, \"max_seq_length\": 12288}",
  "artifacts": "[]",
  "logs": "[]",
  "last_updated": "2025-07-20T11:20:01.780908"
}

License

This dataset is part of the Trackio experiment tracking system and follows the same license as the main project.