What problem does it solve?
TensorBoard solves the challenge of understanding and debugging model training by providing unified, visual summaries of metrics, weights, activations, embeddings, graphs, and performance traces so engineers can quickly spot regressions, diagnose bottlenecks, and compare experiments.
Core Features & Use Cases
- Training metrics & trends: Plot loss, accuracy, learning rate and other scalars over time to monitor convergence and detect issues early.
- Model inspection & debugging: Visualize weight and activation histograms, gradients, and model graphs to find architecture or training anomalies.
- Experiment comparison & tracking: Aggregate multiple runs for side-by-side comparison and hyperparameter analysis to select the best configuration.
- Embeddings & projector: Project high-dimensional embeddings (PCA, t-SNE, UMAP) with metadata and optional label images for semantic analysis.
- Performance profiling: Capture CPU/GPU traces, memory usage, and kernel-level details to identify bottlenecks and optimize throughput.
- Real-world example: Compare three training runs with different learning rates, inspect weight distribution shifts, and use the profiler traces to find the slow operator causing degraded throughput.
Quick Start
Launch TensorBoard pointing at your training log directory (for example runs or logs) to visualize metrics, histograms, model graphs, embeddings, and profiler results.
