AI/ML Debugger

Provides real-time visual representation of neural network architectures with layer-by-layer breakdown, tensor shape tracking, and parameter counts. The extension hooks into PyTorch, TensorFlow, and JAX execution contexts to intercept model definitions and render them as interactive graphs within VS Code's webview panel, enabling developers to inspect layer connectivity, data flow, and computational graph structure without leaving the editor.

Captures tensor values during training execution and displays them in a dedicated panel with histogram distributions, min/max/mean statistics, and anomaly flagging. The extension instruments training loops at the bytecode level to intercept tensor operations, storing snapshots of tensor state at configurable intervals (per batch, per epoch, or on-demand). Anomaly detection uses statistical methods (z-score, IQR) to flag NaN, Inf, or unusual value distributions that indicate training instability.

Analyzes data pipelines to identify preprocessing steps, data transformations, and potential issues. The extension can inspect data loaders to visualize sample batches, compute dataset statistics, and detect data drift (distribution changes between training and validation sets). Supports common data formats (CSV, images, text) and frameworks (PyTorch DataLoader, TensorFlow tf.data, pandas).

Provides built-in support for differentially private training using DP-SGD (Differentially Private Stochastic Gradient Descent). The extension instruments training loops to apply noise to gradients and track privacy budget (epsilon and delta parameters) throughout training. Visualizes privacy budget consumption and provides recommendations for privacy-utility tradeoffs.

Enables side-by-side comparison of multiple trained models or model architectures. The extension displays architecture differences (layer counts, parameter counts, computational complexity), performance metrics (accuracy, loss, inference time), and resource usage (memory, GPU utilization). Supports comparing models from different frameworks (PyTorch vs TensorFlow) and different training runs.

Integrates an LLM-based debugging assistant that analyzes training errors, logs, and model state to suggest root causes and fixes. When training fails (NaN loss, OOM error, convergence failure), the extension captures error context and sends it to an LLM (provider unknown, likely ChatGPT or similar) which generates diagnostic suggestions. Results are displayed in a chat-like interface within VS Code.

Enables debugging of training jobs running on cloud platforms (AWS SageMaker, Google Vertex AI, Azure ML) directly from VS Code. The extension connects to remote training jobs, captures logs and metrics in real-time, and allows setting breakpoints and inspecting model state on remote machines. Supports attaching to running jobs or launching new jobs with debugging enabled.

Captures execution timeline during training and displays it as an interactive timeline chart showing CPU/GPU utilization, kernel execution times, and data loading delays. The extension automatically highlights bottlenecks (e.g., long data loading times, GPU idle periods) and provides recommendations for optimization. Supports zooming and filtering to focus on specific time ranges or operations.

Enables modifying model code (layer definitions, loss functions, optimizers) during training and reloading changes without restarting the training job. The extension uses Python's module reloading mechanism to apply code changes to the running training process. Useful for experimenting with model modifications without losing training progress.

Provides a plugin API that allows developers to extend the debugger with custom analysis tools, visualizations, and integrations. Plugins can hook into training loops, access model state and metrics, and contribute custom UI panels to VS Code. Supports plugin discovery and installation from a plugin marketplace.

Extends VS Code's standard debugger to add ML-specific breakpoints that pause training at epoch boundaries, batch boundaries, or on custom conditions (e.g., loss threshold exceeded). Developers can step through training iterations, inspect model state at each step, and conditionally resume execution. The extension wraps training loops with instrumentation that yields control back to the debugger at specified granularities without requiring code modification.

Instruments neural network forward and backward passes to capture gradient magnitudes and activation values at each layer, displaying them as heatmaps and time-series charts. The extension hooks into framework-specific autograd systems (PyTorch's autograd, TensorFlow's GradientTape, JAX's grad) to intercept gradients before they are applied to weights. Activation visualization captures intermediate layer outputs during forward pass and renders them as heatmaps or statistical distributions.

Provides built-in connectors to popular experiment tracking platforms that automatically log training metrics, model artifacts, hyperparameters, and environment metadata to external tracking services. The extension intercepts training loop metrics and pushes them to configured tracking backends without requiring developers to add tracking code to their scripts. Supports bidirectional sync: logs metrics to tracking service and pulls historical experiment data for comparison within VS Code.

Profiles training execution to measure CPU and GPU utilization, memory consumption, and kernel execution times. The extension uses framework-specific profilers (PyTorch Profiler, TensorFlow Profiler, JAX nsys integration) to capture detailed performance traces and identifies bottlenecks such as data loading delays, GPU underutilization, or memory bandwidth saturation. Results are visualized as flame graphs, timeline charts, and bottleneck reports with actionable optimization suggestions.

Extends VS Code's notebook debugging to support ML-specific breakpoints and tensor inspection within Jupyter notebooks. The extension can also convert notebooks to standalone Python scripts while preserving cell structure as functions or sections, enabling debugging of notebook code in the standard Python debugger. Supports bidirectional sync: changes in converted scripts can be reflected back to the notebook.

Extends debugging capabilities to distributed training scenarios where models are trained across multiple GPUs or machines. The extension attaches debuggers to all training processes and provides synchronized breakpoints that pause all processes simultaneously, allowing inspection of model state across the distributed system. Supports common distributed training frameworks (PyTorch DDP, TensorFlow distributed strategies, JAX pmap/vmap).

Integrates popular model explainability libraries (SHAP, LIME, Grad-CAM) to generate feature importance scores and visual explanations for model predictions. The extension can generate explanations for individual predictions or batches of predictions, displaying results as feature importance charts, saliency maps, or decision plots. Supports both classification and regression models.

Integrates Optuna hyperparameter optimization framework to automatically search for optimal hyperparameters. The extension provides a UI for defining search spaces, running optimization trials, and visualizing results. Also includes learning rate range test (LR finder) that trains the model for a few epochs with increasing learning rates to identify the optimal learning rate range. Results are visualized as optimization history charts and parameter importance plots.