ModelSight is a visual dashboard extension that hooks into local PyTorch or TensorFlow training loops. Stream training metrics, detect overfitting alerts, and explain traceback errors in plain English—completely locally.
Click the simulation buttons below to start a live mockup training run, inspect overfitting triggers, or paste tracebacks to test the diagnostics layer.
ModelSight delivers high-resolution analytics and diagnostics directly inside your text editor workspace.
Streams loss, accuracy, perplexity, and learning rates with low-overhead JSON sockets in real-time.
Flags divergence alerts early if validation loss curves separate from training gradients.
Monitors hardware constraints including GPU power, core temperatures, VRAM, and host RAM.
Decodes Python tracebacks into descriptive error summaries and step-by-step remediation checklists.
ModelSight parses core ML exceptions offline with rule-based heuristics. Examples of handled issues:
Triggered when tensor sizes or batch weights exceed physical VRAM. Remediation suggests gradient accumulation or batch size reductions.
Identifies matrix multiplication shape conflicts or classification target indices mismatch (e.g. 1-indexed targets instead of 0-indexed targets).
Detects exploding gradients or division by zero in custom losses, recommending learning rate decays and gradient clipping.
Streamline your deep learning feedback loop. Get the extension inside VS Code.
ext install CODExGAMERZ.modelsight