tensorboard

# TensorBoard: Visualization Toolkit for ML ## When to Use This Skill Use TensorBoard when you need to: - **Visualize training metrics** like loss and accuracy over time - **Debug models** with histograms and distributions - **Compare experiments** across multiple runs - **Visualize model graphs** and architecture - **Project embeddings** to lower dimensions (t-SNE, PCA) - **Track hyperparameter** experiments - **Profile performance** and identify bottlenecks - **Visualize images and text** during training **Users**: 20M+ downloads/year | **GitHub Stars**: 27k+ | **License**: Apache 2.0 ## Installation ```bash # Install TensorBoard pip install tensorboard # PyTorch integration pip install torch torchvision tensorboard # TensorFlow integration (TensorBoard included) pip install tensorflow # Launch TensorBoard tensorboard --logdir=runs # Access at http://localhost:6006 ``` ## Quick Start ### PyTorch ```python from torch.utils.tensorboard import SummaryWriter # Create writer writer = SummaryWriter('runs/experiment_1') # Training loop for epoch in range(10): train_loss = train_epoch() val_acc = validate() # Log metrics writer.add_scalar('Loss/train', train_loss, epoch) writer.add_scalar('Accuracy/val', val_acc, epoch) # Close writer writer.close() # Launch: tensorboard --logdir=runs ``` ### TensorFlow/Keras ```python import tensorflow as tf # Create callback tensorboard_callback = tf.keras.callbacks.TensorBoard( log_dir='logs/fit', h...