Jupyter Notebook Examples¶

This section provides interactive Jupyter notebook examples demonstrating Tracelet's capabilities in real-world scenarios.

Available Notebooks¶

Getting Started¶

1. Quick Start Notebook¶

File: notebooks/01_quick_start.ipynb
Description: Basic introduction to Tracelet with a simple PyTorch training loop
Topics: Installation, basic logging, metric visualization
Duration: 10-15 minutes

2. Multi-Backend Comparison¶

File: notebooks/02_multi_backend.ipynb
Description: Compare different experiment tracking backends side-by-side
Topics: MLflow, W&B, ClearML configuration and output comparison
Duration: 20-30 minutes

Framework Integrations¶

3. PyTorch Integration Deep Dive¶

File: notebooks/03_pytorch_integration.ipynb
Description: Comprehensive PyTorch integration with TensorBoard
Topics: Scalar metrics, histograms, images, model graphs
Duration: 30-45 minutes

4. PyTorch Lightning Integration¶

File: notebooks/04_lightning_integration.ipynb
Description: End-to-end Lightning training with automatic metric capture
Topics: Lightning modules, callbacks, multi-GPU training
Duration: 25-35 minutes

Advanced Topics¶

5. Computer Vision Workflow¶

File: notebooks/05_computer_vision.ipynb
Description: Complete CV pipeline with image classification
Topics: Data loading, augmentation, model training, prediction visualization
Duration: 45-60 minutes

6. Custom Plugin Development¶

File: notebooks/06_custom_plugins.ipynb
Description: Building custom backends and metric collectors
Topics: Plugin architecture, custom backends, metric routing
Duration: 60+ minutes

Running the Notebooks¶

Prerequisites¶

# Install Tracelet with all extras
pip install tracelet[all]

# Install Jupyter
pip install jupyter notebook

# Or use JupyterLab
pip install jupyterlab

Local Setup¶

# Clone the repository
git clone https://github.com/prassanna-ravishankar/tracelet.git
cd tracelet

# Install in development mode
pip install -e ".[all,dev]"

# Launch Jupyter
jupyter notebook notebooks/
# or
jupyter lab notebooks/

Cloud Environments¶

The notebooks are compatible with popular cloud platforms:

Google Colab¶

Kaggle Kernels¶

Upload notebooks to Kaggle and enable GPU/TPU as needed
Install Tracelet in the first cell: !pip install tracelet[all]

Azure Notebooks / AWS SageMaker¶

Compatible with both platforms
Ensure you have appropriate credentials for your chosen backends

Notebook Contents Overview¶

Quick Start Example¶

# Cell 1: Installation and imports
!pip install tracelet[mlflow]

import tracelet
import torch
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter

# Cell 2: Start experiment tracking
tracelet.start_logging(
    exp_name="notebook_demo",
    project="jupyter_examples",
    backend="mlflow"
)

# Cell 3: Simple training loop with automatic metric capture
writer = SummaryWriter()

for epoch in range(10):
    # Simulated training
    loss = 1.0 / (epoch + 1)  # Decreasing loss
    accuracy = min(0.95, epoch * 0.1)  # Increasing accuracy

    # Metrics automatically captured by Tracelet
    writer.add_scalar('Loss/Train', loss, epoch)
    writer.add_scalar('Accuracy/Train', accuracy, epoch)

    print(f"Epoch {epoch}: Loss={loss:.3f}, Acc={accuracy:.3f}")

# Cell 4: View results
print("Check your MLflow UI to see the logged metrics!")
print("Run: mlflow ui")

Interactive Features¶

Visualizations¶

Metric plots: Real-time plotting with matplotlib/plotly
Model visualizations: Architecture diagrams and parameter distributions
Prediction samples: Image predictions, confusion matrices

Backend Comparisons¶

Side-by-side comparison: Same experiment logged to multiple backends
Feature comparison: Backend-specific capabilities
Performance benchmarks: Logging speed and storage requirements

Best Practices for Notebook Development¶

1. Environment Setup¶

# Always start with clear environment setup
%load_ext tensorboard
%matplotlib inline

import warnings
warnings.filterwarnings('ignore')

2. Experiment Organization¶

# Use descriptive experiment names
exp_name = f"notebook_experiment_{datetime.now().strftime('%Y%m%d_%H%M%S')}"

3. Resource Management¶

# Always clean up resources
try:
    # Your training code here
    pass
finally:
    tracelet.stop_logging()
    writer.close()

4. Interactive Widgets¶

# Use ipywidgets for interactive parameter tuning
from ipywidgets import interact, IntSlider

@interact(learning_rate=(0.001, 0.1, 0.001), batch_size=[16, 32, 64, 128])
def train_model(learning_rate=0.01, batch_size=32):
    # Training code with interactive parameters
    pass

Contributing Notebooks¶

We welcome contributions of new notebook examples! Please see our Contributing Guidelines for details on:

Notebook structure and formatting
Documentation standards
Testing procedures
Submission process

Notebook Template¶

Use our template for consistent formatting:

# Standard header for all notebooks
"""
Tracelet Example: [TITLE]

Description: [Brief description of what this notebook demonstrates]
Estimated time: [X] minutes
Prerequisites: [List any required knowledge or setup]

Author: [Your name]
Date: [Creation date]
"""