Architecture Overview¶

This document provides a detailed overview of Tracelet's architecture for developers and contributors.

High-Level Architecture¶

graph TB
    A[User Code] --> B[Framework Integrations]
    A --> C[TensorBoard SummaryWriter]
    A --> D[Direct API Calls]

    B --> E[Orchestrator]
    C --> E
    D --> E

    E --> F[Plugin System]

    F --> G[Backend Plugins]
    F --> H[Collector Plugins]

    G --> I[MLflow Backend]
    G --> J[W&B Backend]
    G --> K[ClearML Backend]
    G --> L[AIM Backend]

    H --> M[Git Collector]
    H --> N[System Collector]

    I --> O[MLflow Server]
    J --> P[W&B Platform]
    K --> Q[ClearML Platform]
    L --> R[AIM Repository]

Core Components¶

1. Orchestrator (`tracelet.core.orchestrator`)¶

The Orchestrator is the central component that coordinates all metric flow and routing.

Key Responsibilities:

Receives metrics from various sources (TensorBoard, Lightning, direct API)
Routes metrics to appropriate backends based on configuration
Manages thread-safe operations and queueing
Handles backpressure and error recovery

Architecture:

class Orchestrator:
    def __init__(self):
        self._backends: List[BackendInterface] = []
        self._collectors: List[CollectorInterface] = []
        self._queue: Queue = Queue()
        self._worker_threads: List[Thread] = []
        self._running: bool = False

    def route_metric(self, metric: Metric) -> None:
        """Route metric to all configured backends"""

    def start(self) -> None:
        """Start worker threads and collectors"""

    def stop(self) -> None:
        """Stop all operations gracefully"""

2. Plugin System (`tracelet.core.plugins`)¶

The Plugin System provides a modular architecture for extending Tracelet.

Plugin Types:

Backend Plugins: Implement experiment tracking backends
Framework Plugins: Integrate with ML frameworks
Collector Plugins: Gather environment and system data

Plugin Interface:

class PluginInterface:
    @classmethod
    def get_metadata(cls) -> PluginMetadata:
        """Return plugin metadata"""

    def initialize(self, config: dict) -> None:
        """Initialize plugin with configuration"""

    def cleanup(self) -> None:
        """Clean up resources"""

class BackendPlugin(PluginInterface):
    def create_backend(self, config: dict) -> BackendInterface:
        """Factory method to create backend instance"""

class FrameworkPlugin(PluginInterface):
    def patch_framework(self, orchestrator: Orchestrator) -> None:
        """Patch framework to capture metrics"""

3. Experiment Management (`tracelet.core.experiment`)¶

The Experiment class provides the main API for interacting with Tracelet.

Key Features:

Unified API across all backends
Parameter and metric logging
Artifact management
Context management

class Experiment:
    def __init__(self, name: str, project: str):
        self.name = name
        self.project = project
        self._orchestrator = Orchestrator()

    def log_metric(self, name: str, value: float, step: int = None) -> None:
        """Log a scalar metric"""

    def log_params(self, params: dict) -> None:
        """Log experiment parameters"""

    def log_artifact(self, artifact_path: str) -> None:
        """Log an artifact file"""

Framework Integrations¶

TensorBoard Integration (`tracelet.frameworks.pytorch`)¶

Method: Monkey patching torch.utils.tensorboard.SummaryWriter

Implementation Strategy:

Store original methods
Wrap methods with metric capture
Forward calls to original methods
Send captured metrics to orchestrator

class PyTorchFramework:
    def __init__(self):
        self._original_methods = {}
        self._patched = False

    def _patch_tensorboard(self):
        """Patch TensorBoard SummaryWriter methods"""
        from torch.utils.tensorboard import SummaryWriter

        # Store original method
        self._original_methods['add_scalar'] = SummaryWriter.add_scalar

        # Create wrapped method
        def wrapped_add_scalar(self, tag, scalar_value, global_step=None, walltime=None):
            # Capture metric
            self._orchestrator.route_metric(Metric(tag, scalar_value, global_step))

            # Call original method
            return self._original_methods['add_scalar'](self, tag, scalar_value, global_step, walltime)

        # Apply patch
        SummaryWriter.add_scalar = wrapped_add_scalar

PyTorch Lightning Integration (`tracelet.frameworks.lightning`)¶

Method: Monkey patching pytorch_lightning.LightningModule.log

Implementation Strategy:

Patch the log method of LightningModule
Intercept all self.log() calls
Extract metric information and route to orchestrator
Preserve original Lightning logging behavior

class LightningFramework:
    def _patch_lightning_logging(self):
        """Patch Lightning's logging system"""
        from pytorch_lightning.core.module import LightningModule

        # Store original method
        self._original_log = LightningModule.log

        def wrapped_log(module_self, name: str, value, *args, **kwargs):
            # Call original method first
            result = self._original_log(module_self, name, value, *args, **kwargs)

            # Extract step information
            step = self._get_current_step(module_self)

            # Route to Tracelet
            if isinstance(value, (int, float)):
                self._orchestrator.route_metric(Metric(name, float(value), step))

            return result

        # Apply patch
        LightningModule.log = wrapped_log

Backend Implementations¶

Backend Interface¶

All backends implement a common interface:

class BackendInterface:
    def log_metric(self, name: str, value: float, step: int) -> None:
        """Log a scalar metric"""

    def log_params(self, params: dict) -> None:
        """Log experiment parameters"""

    def log_artifact(self, artifact_path: str, artifact_name: str = None) -> None:
        """Log an artifact"""

    def start_experiment(self, name: str, project: str) -> str:
        """Start a new experiment, return experiment ID"""

    def end_experiment(self) -> None:
        """End the current experiment"""

MLflow Backend (`tracelet.backends.mlflow`)¶

Implementation Details:

Uses mlflow.tracking client
Manages MLflow runs automatically
Supports local and remote tracking servers
Handles nested runs for complex experiments

class MLflowBackend(BackendInterface):
    def __init__(self, config: dict):
        import mlflow
        self.mlflow = mlflow
        self.tracking_uri = config.get('tracking_uri', 'file:./mlruns')
        self.mlflow.set_tracking_uri(self.tracking_uri)

    def start_experiment(self, name: str, project: str) -> str:
        experiment = self.mlflow.set_experiment(project)
        run = self.mlflow.start_run(run_name=name)
        return run.info.run_id

W&B Backend (`tracelet.backends.wandb`)¶

Implementation Details:

Uses wandb Python SDK
Manages wandb runs and projects
Supports both cloud and local modes
Handles W&B-specific features (sweeps, artifacts)

ClearML Backend (`tracelet.backends.clearml`)¶

Implementation Details:

Uses clearml SDK
Manages ClearML tasks and projects
Supports offline mode for testing
Integrates with ClearML's experiment management

Data Flow¶

Metric Flow Pipeline¶

Capture: Framework integrations capture metrics from user code
Queue: Metrics are queued for processing to handle high-frequency logging
Route: Orchestrator routes metrics to all configured backends
Transform: Each backend transforms metrics to its native format
Send: Backends send metrics to their respective platforms

# Example metric flow
def metric_flow_example():
    # 1. User code logs metric
    writer.add_scalar('accuracy', 0.95, step=100)

    # 2. TensorBoard patch captures metric
    metric = Metric(name='accuracy', value=0.95, step=100)

    # 3. Orchestrator queues metric
    orchestrator.route_metric(metric)

    # 4. Worker thread processes queue
    for backend in backends:
        backend.log_metric(metric.name, metric.value, metric.step)

    # 5. Backends send to their platforms
    # MLflow: mlflow.log_metric('accuracy', 0.95, step=100)
    # W&B: wandb.log({'accuracy': 0.95}, step=100)
    # ClearML: logger.report_scalar('accuracy', 'accuracy', 0.95, 100)

Threading Model¶

Main Thread: User code execution and framework patching Worker Threads: Background processing of metric queue Collector Threads: Periodic collection of system metrics

class ThreadingModel:
    def __init__(self):
        self._metric_queue = Queue()
        self._worker_pool = ThreadPoolExecutor(max_workers=4)
        self._collector_timer = Timer(interval=30.0, function=self._collect_metrics)

    def _process_metrics(self):
        """Worker thread processes metric queue"""
        while self._running:
            try:
                metric = self._metric_queue.get(timeout=1.0)
                self._route_to_backends(metric)
            except Empty:
                continue

    def _collect_metrics(self):
        """Periodic collection of system metrics"""
        for collector in self._collectors:
            metrics = collector.collect()
            for metric in metrics:
                self._metric_queue.put(metric)

Configuration Management¶

Configuration Hierarchy¶

Default Configuration: Built-in defaults
Environment Variables: System-wide overrides
Configuration Files: Project-specific settings
API Parameters: Runtime overrides

class ConfigurationManager:
    def __init__(self):
        self.config = self._load_default_config()
        self._apply_env_overrides()
        self._load_config_files()

    def _load_default_config(self) -> dict:
        return {
            'backends': ['mlflow'],
            'track_system': True,
            'track_git': True,
            'metrics_interval': 30.0,
            'max_queue_size': 10000
        }

Settings Management (`tracelet.settings`)¶

Pydantic-based Settings:

Type validation
Environment variable integration
Documentation generation
IDE support

from pydantic import BaseSettings

class TraceletSettings(BaseSettings):
    project: str = "default"
    backend: List[str] = ["mlflow"]
    track_system: bool = True
    track_git: bool = True
    track_env: bool = True
    metrics_interval: float = 30.0

    class Config:
        env_prefix = "TRACELET_"
        case_sensitive = False

Error Handling and Recovery¶

Error Strategies¶

Graceful Degradation: Continue operation even if some backends fail Retry Logic: Automatic retry with exponential backoff Circuit Breaker: Temporarily disable failing backends Fallback Modes: Switch to local backends if remote ones fail

class ErrorHandling:
    def __init__(self):
        self._retry_config = {
            'max_retries': 3,
            'backoff_factor': 2.0,
            'max_backoff': 60.0
        }
        self._circuit_breakers = {}

    async def safe_backend_call(self, backend: BackendInterface, operation: str, *args, **kwargs):
        """Safely call backend with error handling"""
        breaker = self._circuit_breakers.get(backend.name)

        if breaker and breaker.is_open():
            return None  # Skip if circuit breaker is open

        try:
            method = getattr(backend, operation)
            return await method(*args, **kwargs)
        except Exception as e:
            self._handle_backend_error(backend, e)
            return None

Performance Considerations¶

Optimization Strategies¶

Batching: Batch multiple metrics for efficient network usage Compression: Compress large artifacts and images Async Operations: Non-blocking metric logging Connection Pooling: Reuse connections to backend services

Memory Management¶

Bounded Queues: Prevent memory leaks from unbounded growth Periodic Cleanup: Clean up completed experiments Resource Limits: Configurable limits on image sizes and queue sizes

Profiling and Monitoring¶

Internal Metrics: Track Tracelet's own performance Debugging Tools: Built-in profiling and debugging capabilities Health Checks: Monitor backend connectivity and health

Security Considerations¶

API Key Management¶

Environment variable storage
Secure credential storage
Key rotation support
Minimal privilege access

Data Privacy¶

Configurable data filtering
PII detection and masking
Local-only modes for sensitive data
Audit logging capabilities

Extension Points¶

Custom Backends¶

Developers can create custom backends by:

Implementing BackendInterface
Creating a backend plugin
Registering the plugin with Tracelet

Custom Collectors¶

Add new data collectors by:

Implementing CollectorInterface
Creating a collector plugin
Configuring collection intervals

Custom Framework Integrations¶

Integrate new frameworks by:

Implementing FrameworkInterface
Creating framework-specific patches
Testing with framework workflows

This architecture provides a solid foundation for experiment tracking while remaining flexible and extensible for future enhancements.

Architecture Overview¶

High-Level Architecture¶

Core Components¶

1. Orchestrator (tracelet.core.orchestrator)¶

2. Plugin System (tracelet.core.plugins)¶

3. Experiment Management (tracelet.core.experiment)¶

Framework Integrations¶

TensorBoard Integration (tracelet.frameworks.pytorch)¶

PyTorch Lightning Integration (tracelet.frameworks.lightning)¶

Backend Implementations¶

Backend Interface¶

MLflow Backend (tracelet.backends.mlflow)¶

W&B Backend (tracelet.backends.wandb)¶

ClearML Backend (tracelet.backends.clearml)¶

Data Flow¶

Metric Flow Pipeline¶

Threading Model¶

Configuration Management¶

Configuration Hierarchy¶

Settings Management (tracelet.settings)¶

Error Handling and Recovery¶

Error Strategies¶

Performance Considerations¶

Optimization Strategies¶

Memory Management¶

Profiling and Monitoring¶

Security Considerations¶

API Key Management¶

Data Privacy¶

Extension Points¶

Custom Backends¶

Custom Collectors¶

Custom Framework Integrations¶

1. Orchestrator (`tracelet.core.orchestrator`)¶

2. Plugin System (`tracelet.core.plugins`)¶

3. Experiment Management (`tracelet.core.experiment`)¶

TensorBoard Integration (`tracelet.frameworks.pytorch`)¶

PyTorch Lightning Integration (`tracelet.frameworks.lightning`)¶

MLflow Backend (`tracelet.backends.mlflow`)¶

W&B Backend (`tracelet.backends.wandb`)¶

ClearML Backend (`tracelet.backends.clearml`)¶

Settings Management (`tracelet.settings`)¶