v0.2.3: Sparklines, Anomaly Detection, TensorBoard

What's New

Sparklines

Token-efficient trend visualization using Unicode block characters:

loss: ▇▆▅▄▃▂▁▁ ↓ (1.5→0.2)

Conveys trend in ~10 tokens vs 50+ for raw numbers.

Anomaly Detection

Automatically flags training issues:

• Loss spikes - Robust z-score using Median Absolute Deviation (MAD)
• Overfitting - Val/train loss ratio divergence
• Plateaus - No improvement over extended period
• Gradient issues - Vanishing or exploding gradients
• NaN/Inf - Critical failure detection

Zero tokens for healthy runs - only outputs when problems exist.

TensorBoard Support

Optional parsing of tfevents files (requires pip install tensorboard):

runwise tb              # List TB runs
runwise tb -r train_1   # Summarize specific run

New CLI Flags

• --no-spark - Disable sparklines (faster)
• --no-anomalies - Skip anomaly detection

New MCP Tools

• detect_anomalies - Run anomaly detection on a run
• get_sparkline - Get sparkline visualization for metrics

Install

pip install runwise==0.2.3

Full Changelog: v0.2.2...v0.2.3