{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-yashh130021-vscode-ai-debugger","slug":"aiml-debugger","name":"AI/ML Debugger","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=yashh130021.vscode-ai-debugger","page_url":"https://unfragile.ai/aiml-debugger","categories":["code-editors"],"tags":["AI/ML","artificial intelligence","auto-tuning","AWS SageMaker","Azure ML","cloud debugging","cross-model comparison","data-centric debugging","debugging","deep learning","differential privacy","distributed debugging","experiment tracking","explainability","flax","Google Vertex AI","hyperparameter optimization","jax","keybindings","LIME","machine learning","MLflow","model benchmarking","model visualization","Neptune","neural network","performance profiling","plugin extensible","privacy-aware training","pytorch","SHAP","tensor","tensorflow","Weights & Biases","zero-config setup"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-yashh130021-vscode-ai-debugger__cap_0","uri":"capability://code.generation.editing.interactive.model.architecture.visualization.with.layer.level.inspection","name":"interactive model architecture visualization with layer-level inspection","description":"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.","intents":["I need to understand the structure of my neural network model at a glance","I want to verify that my model architecture matches my design before training","I need to inspect tensor shapes flowing through each layer to debug shape mismatches"],"best_for":["ML engineers building custom neural network architectures","researchers prototyping novel model designs","teams debugging model definition errors before training"],"limitations":["Requires model to be importable and instantiable in Python environment — dynamic models or models with conditional layers may not render completely","Visualization performance degrades with very large models (1000+ layers)","Does not capture runtime-generated layers or models built with functional APIs that bypass standard layer registration"],"requires":["VS Code 1.60+","Python 3.7+","PyTorch 1.9+, TensorFlow 2.4+, or JAX 0.2.0+","Model file accessible in current workspace"],"input_types":["Python model definition files (.py)","Jupyter notebooks (.ipynb) with model definitions"],"output_types":["Interactive SVG/canvas visualization in VS Code webview","JSON metadata with layer names, shapes, parameter counts"],"categories":["code-generation-editing","visualization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-yashh130021-vscode-ai-debugger__cap_1","uri":"capability://data.processing.analysis.real.time.tensor.inspection.with.statistical.analysis.and.anomaly.detection","name":"real-time tensor inspection with statistical analysis and anomaly detection","description":"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.","intents":["I need to inspect what values are flowing through my model during training to debug NaN/Inf issues","I want to monitor activation distributions to detect vanishing or exploding gradients","I need to understand if my model is learning by observing weight and activation statistics in real-time"],"best_for":["ML engineers debugging training instability and convergence issues","researchers analyzing model behavior during training","teams implementing custom training loops who need visibility into tensor state"],"limitations":["Tensor capture adds 5-15% overhead to training speed depending on capture frequency and tensor size","Memory overhead scales with number of tensors captured — large models with many intermediate tensors may require filtering","Requires training code to be running in same Python process as VS Code extension — distributed training across multiple machines requires separate debugging setup per machine","Cannot capture tensors from compiled/optimized code paths (e.g., CUDA kernels, TorchScript compiled functions)"],"requires":["VS Code 1.60+","Python 3.7+ with debugpy or similar debugging protocol support","PyTorch, TensorFlow, or JAX installed in active Python environment","Training script running in VS Code's integrated debugger or attached to extension"],"input_types":["Running training process with tensor operations","Breakpoint-triggered snapshots or continuous sampling"],"output_types":["Histogram visualizations with statistical summaries","Time-series charts of tensor statistics across training steps","Anomaly alerts with flagged tensors and suggested fixes"],"categories":["data-processing-analysis","debugging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-yashh130021-vscode-ai-debugger__cap_10","uri":"capability://data.processing.analysis.data.pipeline.analysis.and.preprocessing.inspection.with.drift.detection","name":"data pipeline analysis and preprocessing inspection with drift detection","description":"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).","intents":["I need to understand what preprocessing is being applied to my data","I want to visualize sample batches to verify data loading is correct","I need to detect if my validation set has different distribution than training set"],"best_for":["ML engineers debugging data pipeline issues","data scientists analyzing data quality and distribution","teams implementing data validation and monitoring"],"limitations":["Data inspection requires loading data into memory — large datasets may exceed available memory","Drift detection uses statistical tests that may not be sensitive to all types of distribution changes","Does not detect label noise or data quality issues beyond distribution shifts","Requires data to be in standard formats — custom data loaders may not be supported"],"requires":["VS Code 1.60+","Python 3.7+","PyTorch, TensorFlow, or pandas installed","Data files accessible in workspace or via data loader"],"input_types":["Data loader (PyTorch DataLoader, TensorFlow tf.data, pandas DataFrame)","Data files (CSV, images, text)"],"output_types":["Sample batch visualizations","Dataset statistics (mean, std, min, max, distribution)","Drift detection reports with statistical test results","Data quality alerts"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-yashh130021-vscode-ai-debugger__cap_11","uri":"capability://safety.moderation.differential.privacy.implementation.with.dp.sgd.and.privacy.budget.tracking","name":"differential privacy implementation with dp-sgd and privacy budget tracking","description":"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.","intents":["I need to train a model with differential privacy guarantees to protect sensitive data","I want to understand the privacy-utility tradeoff and how much noise to add","I need to track privacy budget consumption during training"],"best_for":["ML engineers building privacy-preserving models for regulated industries","researchers studying differential privacy","organizations handling sensitive data requiring formal privacy guarantees"],"limitations":["DP-SGD adds computational overhead (5-20% slower than standard training) due to gradient clipping and noise addition","Privacy guarantees require careful tuning of noise scale and clipping threshold — incorrect configuration may provide weak privacy","Privacy budget is consumed with each training step — total privacy budget is fixed and cannot be recovered","Differential privacy typically requires larger models or more training data to maintain accuracy"],"requires":["VS Code 1.60+","Python 3.7+","PyTorch, TensorFlow, or JAX with DP-SGD implementation (e.g., Opacus for PyTorch)","Understanding of differential privacy concepts (epsilon, delta, privacy budget)"],"input_types":["Training loop code","Model definition","Privacy parameters (epsilon, delta, noise scale)"],"output_types":["Privacy budget tracking charts","DP-SGD configuration recommendations","Privacy-utility tradeoff visualizations","Trained model with privacy guarantees"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-yashh130021-vscode-ai-debugger__cap_12","uri":"capability://data.processing.analysis.cross.model.comparison.with.architecture.and.performance.metrics","name":"cross-model comparison with architecture and performance metrics","description":"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.","intents":["I want to compare the performance of different model architectures to choose the best one","I need to understand the tradeoffs between model size and accuracy","I want to compare inference speed and memory usage across different models"],"best_for":["ML engineers selecting models for production deployment","researchers comparing model architectures and training approaches","teams evaluating model variants for performance and efficiency"],"limitations":["Comparison requires models to be compatible (same input/output shapes) — cannot compare models for different tasks","Performance metrics must be computed on same dataset and hardware for fair comparison","Cross-framework comparison (PyTorch vs TensorFlow) requires converting models to common format","Does not account for training time or computational cost of different models"],"requires":["VS Code 1.60+","Python 3.7+","PyTorch, TensorFlow, or JAX","Multiple trained models or model definitions"],"input_types":["Trained model files (.pt, .pb, .pkl, etc.)","Model definitions (Python code)","Performance metrics (CSV, JSON)"],"output_types":["Side-by-side architecture comparison tables","Performance metrics comparison charts","Resource usage comparison (memory, GPU, inference time)","Recommendation for best model based on criteria"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-yashh130021-vscode-ai-debugger__cap_13","uri":"capability://planning.reasoning.ai.powered.root.cause.analysis.for.training.failures.with.llm.debugging.copilot","name":"ai-powered root cause analysis for training failures with llm debugging copilot","description":"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.","intents":["I got a NaN loss error and don't know what caused it — I need suggestions for debugging","My model is not converging — I want AI suggestions for what might be wrong","I got an out-of-memory error and need help reducing memory usage"],"best_for":["ML engineers debugging training failures without deep expertise","teams needing quick diagnostics for common training issues","researchers exploring novel architectures and encountering unfamiliar errors"],"limitations":["LLM suggestions are heuristic-based and may not apply to specific model architectures or datasets","Requires network connectivity to LLM provider (API key and rate limits apply)","LLM provider and model are not documented — unclear if using OpenAI, Anthropic, or other provider","Suggestions may be generic or incorrect for edge cases — human judgment required","Privacy concern: error logs and model state are sent to external LLM service"],"requires":["VS Code 1.60+","Python 3.7+","Network connectivity to LLM provider","API key for LLM service (configuration not documented)"],"input_types":["Error messages and stack traces","Training logs and metrics","Model definition and hyperparameters","Data statistics"],"output_types":["Diagnostic suggestions in chat interface","Recommended fixes and debugging steps","Links to relevant documentation or examples"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-yashh130021-vscode-ai-debugger__cap_14","uri":"capability://tool.use.integration.remote.debugging.for.cloud.based.training.on.aws.sagemaker.google.vertex.ai.and.azure.ml","name":"remote debugging for cloud-based training on aws sagemaker, google vertex ai, and azure ml","description":"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.","intents":["I need to debug my training job running on AWS SageMaker without SSH-ing into the instance","I want to see real-time logs and metrics from my Google Vertex AI training job in VS Code","I need to set breakpoints and inspect model state on a remote Azure ML training job"],"best_for":["ML teams running training on cloud platforms","researchers debugging large-scale training jobs","organizations using managed ML services for training"],"limitations":["Requires cloud platform credentials and permissions to access training jobs","Remote debugging adds network latency — stepping through code may be slow","Breakpoints and tensor inspection may not work with optimized/compiled code on cloud instances","Cloud platform charges apply for running training jobs with debugging enabled","Does not support all cloud platforms — only AWS SageMaker, Google Vertex AI, Azure ML documented"],"requires":["VS Code 1.60+","Python 3.7+","Cloud platform account and credentials (AWS, GCP, or Azure)","Training job running on cloud platform","Network connectivity to cloud platform"],"input_types":["Cloud platform credentials","Training job ID or name","Model definition and training code"],"output_types":["Real-time logs from remote training job","Metrics and performance data","Remote debugging session with breakpoints","Tensor inspection from remote model"],"categories":["tool-use-integration","debugging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-yashh130021-vscode-ai-debugger__cap_15","uri":"capability://data.processing.analysis.execution.timeline.visualization.with.performance.markers.and.bottleneck.highlighting","name":"execution timeline visualization with performance markers and bottleneck highlighting","description":"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.","intents":["I want to see a timeline of what my training is doing at each step","I need to understand where time is being spent during training","I want to identify periods where GPU is idle or underutilized"],"best_for":["ML engineers optimizing training performance","researchers analyzing training dynamics","teams profiling training for bottleneck identification"],"limitations":["Timeline capture adds overhead to training (5-15% depending on granularity)","Large training jobs generate very large timeline traces (100MB-1GB+) that may slow down VS Code","Timeline visualization may be difficult to interpret for very long training runs (1000+ steps)","Bottleneck detection is heuristic-based and may miss subtle performance issues"],"requires":["VS Code 1.60+","Python 3.7+","PyTorch, TensorFlow, or JAX with profiling support"],"input_types":["Training execution with profiling enabled","Performance trace data"],"output_types":["Interactive timeline chart with CPU/GPU utilization","Bottleneck highlighting and recommendations","Detailed operation timing information"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-yashh130021-vscode-ai-debugger__cap_16","uri":"capability://code.generation.editing.hot.swapping.of.model.components.with.live.code.reload.during.training","name":"hot-swapping of model components with live code reload during training","description":"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.","intents":["I want to modify my model architecture during training and see the effect without restarting","I need to change my loss function or optimizer and continue training with the new configuration","I want to experiment with different layer configurations without losing training progress"],"best_for":["ML researchers experimenting with model modifications","teams iterating on model design during training","developers prototyping novel architectures"],"limitations":["Hot-swapping only works for code changes — cannot change model architecture in ways that affect saved weights (e.g., adding/removing layers)","Reloading modules can cause unexpected behavior if modules have side effects or global state","Not compatible with compiled/optimized code paths (TorchScript, JAX jit)","May cause training instability if loss function or optimizer is changed mid-training","Requires training code to use standard Python module imports — custom import mechanisms may not work"],"requires":["VS Code 1.60+","Python 3.7+","PyTorch, TensorFlow, or JAX with standard Python execution","Training code using standard module imports"],"input_types":["Model code files (.py)","Training loop code"],"output_types":["Reloaded model with modified code","Continued training with new configuration","Training metrics and checkpoints"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-yashh130021-vscode-ai-debugger__cap_17","uri":"capability://tool.use.integration.plugin.extensibility.system.for.custom.debugging.and.analysis.tools","name":"plugin extensibility system for custom debugging and analysis tools","description":"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.","intents":["I want to build a custom analysis tool that integrates with the debugger","I need to add domain-specific visualizations for my model type","I want to integrate my custom experiment tracking system with the debugger"],"best_for":["ML teams building custom debugging tools","organizations with specialized ML workflows requiring custom extensions","researchers building novel analysis and visualization tools"],"limitations":["Plugin API documentation is not provided in available materials — unclear what APIs are available","Plugins must be written in JavaScript/TypeScript (VS Code extension language) — may require learning new language for Python-focused developers","Plugin security model is not documented — unclear what access control plugins have","Plugin marketplace and discovery mechanism are not documented"],"requires":["VS Code 1.60+","JavaScript/TypeScript knowledge","VS Code Extension API knowledge","Plugin API documentation (not provided)"],"input_types":["Plugin code (JavaScript/TypeScript)","Plugin manifest (package.json)"],"output_types":["Installed plugin in VS Code","Custom UI panels and visualizations","Extended debugging capabilities"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-yashh130021-vscode-ai-debugger__cap_2","uri":"capability://code.generation.editing.step.through.training.execution.with.epoch.and.batch.level.control","name":"step-through training execution with epoch and batch-level control","description":"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.","intents":["I want to pause training at specific epochs to inspect model weights and activations","I need to debug a training loop by stepping through batches and checking loss values","I want to conditionally stop training when a metric crosses a threshold to investigate anomalies"],"best_for":["ML engineers debugging training loop logic and convergence behavior","researchers investigating model behavior at specific training stages","teams building custom training loops who need fine-grained execution control"],"limitations":["Stepping through training adds significant latency (5-30 seconds per step depending on batch size and model complexity) — not suitable for production training","Conditional breakpoints based on loss/metric values require computing those metrics at each step, adding overhead even when breakpoint is not triggered","Does not work with distributed training across multiple GPUs/machines without separate debugger attachment per process","Cannot step through compiled/optimized code paths (TorchScript, JAX jit-compiled functions)"],"requires":["VS Code 1.60+","Python 3.7+ with debugpy support","Training script running in VS Code's integrated debugger","PyTorch, TensorFlow, or JAX with standard Python execution (not compiled)"],"input_types":["Training loop code with standard Python control flow","Breakpoint definitions (line-based, conditional, or epoch/batch-based)"],"output_types":["Paused execution state with access to all variables and tensors","Debug console for evaluating expressions and inspecting model state"],"categories":["code-generation-editing","debugging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-yashh130021-vscode-ai-debugger__cap_3","uri":"capability://data.processing.analysis.gradient.flow.monitoring.and.activation.visualization","name":"gradient flow monitoring and activation visualization","description":"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.","intents":["I need to detect vanishing or exploding gradients in my deep network","I want to visualize which layers are learning and which are stagnant","I need to understand activation patterns to diagnose dead neurons or saturation issues"],"best_for":["ML engineers training deep neural networks and debugging convergence issues","researchers analyzing network behavior and layer-wise learning dynamics","teams implementing custom loss functions or training algorithms who need gradient visibility"],"limitations":["Gradient capture adds 10-20% overhead to backward pass computation","Activation capture requires storing intermediate tensors in memory — large models with many layers may exceed available GPU/CPU memory","Does not work with gradient checkpointing or other memory optimization techniques that recompute activations","Cannot visualize gradients from compiled/optimized code paths or custom CUDA kernels"],"requires":["VS Code 1.60+","Python 3.7+","PyTorch 1.9+, TensorFlow 2.4+, or JAX 0.2.0+","Training script with standard autograd/gradient computation"],"input_types":["Training loop with backward pass and gradient computation","Model definition with standard layer types"],"output_types":["Heatmaps showing gradient magnitude per layer across training steps","Time-series charts of gradient norms and activation statistics","Alerts for vanishing/exploding gradients with suggested fixes"],"categories":["data-processing-analysis","debugging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-yashh130021-vscode-ai-debugger__cap_4","uri":"capability://tool.use.integration.experiment.tracking.integration.with.mlflow.weights.biases.and.neptune","name":"experiment tracking integration with mlflow, weights & biases, and neptune","description":"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.","intents":["I want to log all my training metrics to MLflow/W&B without adding tracking code to my script","I need to compare metrics across multiple training runs to find the best hyperparameters","I want to automatically capture model artifacts and environment metadata for reproducibility"],"best_for":["ML teams running multiple experiments and needing centralized tracking","researchers comparing model variants and hyperparameter configurations","organizations requiring experiment reproducibility and audit trails"],"limitations":["Requires API credentials for tracking service (MLflow server URL, W&B API key, Neptune API token) — must be configured in extension settings","Network latency for pushing metrics to remote tracking service may add 50-200ms per logging call","Does not support custom metrics or complex metric structures beyond standard scalars and arrays","Tracking service outages will not block training but will cause metrics to be lost if not buffered locally"],"requires":["VS Code 1.60+","Python 3.7+","MLflow, Weights & Biases, or Neptune SDK installed in Python environment","API credentials for tracking service (API key, server URL, or project ID)","Network connectivity to tracking service"],"input_types":["Training metrics (scalars, arrays) from training loop","Model artifacts (weights, checkpoints)","Hyperparameter dictionaries","Environment metadata (Python version, package versions, hardware info)"],"output_types":["Logged experiments in MLflow/W&B/Neptune backend","Experiment comparison tables and charts within VS Code","Downloadable artifacts and model checkpoints"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-yashh130021-vscode-ai-debugger__cap_5","uri":"capability://data.processing.analysis.cpu.gpu.profiling.with.bottleneck.identification.and.performance.recommendations","name":"cpu/gpu profiling with bottleneck identification and performance recommendations","description":"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.","intents":["I need to understand why my training is slow and where the bottleneck is","I want to see if my GPU is being fully utilized or if I/O is the limiting factor","I need to optimize memory usage to fit larger batches or models on my hardware"],"best_for":["ML engineers optimizing training performance for production workloads","researchers maximizing GPU utilization for large-scale experiments","teams migrating models to new hardware and needing performance baselines"],"limitations":["Profiling adds 5-30% overhead to training speed depending on profiling granularity","GPU profiling requires NVIDIA GPU with CUDA support and NVIDIA profiling tools installed (nsys, nvprof)","Detailed profiling generates large trace files (100MB-1GB+) that may slow down VS Code UI","Does not profile distributed training across multiple machines — requires separate profiling per machine","Recommendations are heuristic-based and may not apply to all model architectures or hardware configurations"],"requires":["VS Code 1.60+","Python 3.7+","PyTorch 1.9+, TensorFlow 2.4+, or JAX 0.2.0+","NVIDIA GPU with CUDA 11.0+ for GPU profiling (optional for CPU-only profiling)","NVIDIA profiling tools (nsys, nvprof) installed for detailed GPU profiling"],"input_types":["Training loop code","Model definition","Data loader configuration"],"output_types":["Flame graphs showing function call hierarchy and execution time","Timeline charts with GPU/CPU utilization and memory usage","Bottleneck report with identified performance issues","Optimization recommendations (e.g., increase batch size, reduce data loading time)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-yashh130021-vscode-ai-debugger__cap_6","uri":"capability://code.generation.editing.jupyter.notebook.debugging.and.conversion.to.python.scripts","name":"jupyter notebook debugging and conversion to python scripts","description":"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.","intents":["I want to debug my Jupyter notebook cell-by-cell with breakpoints and tensor inspection","I need to convert my notebook to a production Python script while preserving the logic","I want to run my notebook code through the debugger to find bugs before deploying"],"best_for":["Data scientists and ML engineers working in notebooks who need debugging capabilities","teams converting exploratory notebooks to production code","researchers sharing notebook-based experiments that need to be debugged"],"limitations":["Notebook debugging requires notebook to be running in VS Code's notebook kernel — does not support remote Jupyter servers without additional configuration","Conversion to Python script may lose notebook-specific features (markdown cells, output formatting, interactive widgets)","Bidirectional sync between notebook and script is one-way (script changes can be reflected back, but notebook UI changes are not automatically synced)","Does not support notebooks with complex dependencies or custom kernels"],"requires":["VS Code 1.60+ with Jupyter extension","Python 3.7+","Jupyter installed in Python environment","Notebook file (.ipynb) in workspace"],"input_types":["Jupyter notebook (.ipynb) with Python code cells","Markdown cells and output"],"output_types":["Converted Python script (.py) with cell structure preserved","Debug session with breakpoints and tensor inspection","Execution results and output"],"categories":["code-generation-editing","debugging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-yashh130021-vscode-ai-debugger__cap_7","uri":"capability://automation.workflow.multi.gpu.and.distributed.cluster.debugging.with.synchronized.breakpoints","name":"multi-gpu and distributed cluster debugging with synchronized breakpoints","description":"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).","intents":["I need to debug my distributed training code and inspect model state across all GPUs","I want to set breakpoints that pause all training processes simultaneously","I need to understand how gradients are synchronized across distributed processes"],"best_for":["ML engineers training large models on multi-GPU or multi-machine clusters","researchers debugging distributed training algorithms","teams implementing custom distributed training logic"],"limitations":["Requires debugger attachment to all training processes — adds significant overhead and complexity","Synchronized breakpoints across multiple machines introduce network latency (100-500ms per breakpoint)","Does not work with asynchronous distributed training frameworks or parameter servers","Requires all training processes to be running on machines accessible from VS Code (same network or SSH tunnel)","Debugging large distributed systems (100+ GPUs) may be impractical due to synchronization overhead"],"requires":["VS Code 1.60+","Python 3.7+","PyTorch 1.9+, TensorFlow 2.4+, or JAX 0.2.0+","Distributed training framework (PyTorch DDP, TensorFlow distributed, JAX pmap)","Network connectivity between all training machines and VS Code host","SSH access or direct network access to all training machines"],"input_types":["Distributed training code with standard distributed training APIs","Model definition and training loop"],"output_types":["Synchronized breakpoint pauses across all training processes","Aggregated tensor inspection from all processes","Gradient synchronization visualization"],"categories":["automation-workflow","debugging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-yashh130021-vscode-ai-debugger__cap_8","uri":"capability://data.processing.analysis.model.explainability.with.shap.lime.and.grad.cam.integration","name":"model explainability with shap, lime, and grad-cam integration","description":"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.","intents":["I need to understand which features are most important for my model's predictions","I want to generate saliency maps to visualize which parts of an image the model is focusing on","I need to explain individual predictions to stakeholders or for debugging model behavior"],"best_for":["ML engineers building interpretable models for regulated industries","data scientists debugging model predictions and understanding model behavior","teams building explainable AI systems for production deployment"],"limitations":["SHAP and LIME computation is expensive — generating explanations for a single prediction can take 10-60 seconds depending on model size and feature count","Grad-CAM only works for image models with convolutional layers — not applicable to other model types","Explanations are approximate and may not fully capture model behavior, especially for complex non-linear models","Requires access to training data for SHAP background samples — may not be available in production environments"],"requires":["VS Code 1.60+","Python 3.7+","SHAP, LIME, and/or Grad-CAM libraries installed","Trained model in PyTorch, TensorFlow, or JAX format","Input data for generating explanations"],"input_types":["Trained model","Input samples (images, tabular data, text)","Training data for SHAP background samples (optional)"],"output_types":["Feature importance scores and charts","Saliency maps for image models","Decision plots and force plots","LIME local explanations"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-yashh130021-vscode-ai-debugger__cap_9","uri":"capability://planning.reasoning.hyperparameter.optimization.with.optuna.integration.and.learning.rate.range.testing","name":"hyperparameter optimization with optuna integration and learning rate range testing","description":"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.","intents":["I want to automatically find the best hyperparameters for my model without manual tuning","I need to identify the optimal learning rate range before training","I want to visualize how different hyperparameters affect model performance"],"best_for":["ML engineers tuning models for optimal performance","researchers exploring hyperparameter sensitivity","teams building AutoML pipelines"],"limitations":["Hyperparameter optimization requires running many training trials — total time scales with number of trials and model size","Search space definition requires understanding of hyperparameter ranges and distributions","Optimization results are specific to the dataset and model architecture — may not transfer to other problems","Learning rate range test assumes standard training setup — may not work with custom optimizers or learning rate schedules"],"requires":["VS Code 1.60+","Python 3.7+","Optuna installed in Python environment","PyTorch, TensorFlow, or JAX with training loop"],"input_types":["Training loop code","Model definition","Hyperparameter search space definition"],"output_types":["Optimization history with trial results","Parameter importance plots","Learning rate range test results","Best hyperparameter configuration"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["VS Code 1.60+","Python 3.7+","PyTorch 1.9+, TensorFlow 2.4+, or JAX 0.2.0+","Model file accessible in current workspace","Python 3.7+ with debugpy or similar debugging protocol support","PyTorch, TensorFlow, or JAX installed in active Python environment","Training script running in VS Code's integrated debugger or attached to extension","PyTorch, TensorFlow, or pandas installed","Data files accessible in workspace or via data loader","PyTorch, TensorFlow, or JAX with DP-SGD implementation (e.g., Opacus for PyTorch)"],"failure_modes":["Requires model to be importable and instantiable in Python environment — dynamic models or models with conditional layers may not render completely","Visualization performance degrades with very large models (1000+ layers)","Does not capture runtime-generated layers or models built with functional APIs that bypass standard layer registration","Tensor capture adds 5-15% overhead to training speed depending on capture frequency and tensor size","Memory overhead scales with number of tensors captured — large models with many intermediate tensors may require filtering","Requires training code to be running in same Python process as VS Code extension — distributed training across multiple machines requires separate debugging setup per machine","Cannot capture tensors from compiled/optimized code paths (e.g., CUDA kernels, TorchScript compiled functions)","Data inspection requires loading data into memory — large datasets may exceed available memory","Drift detection uses statistical tests that may not be sensitive to all types of distribution changes","Does not detect label noise or data quality issues beyond distribution shifts","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.23,"quality":0.5,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:31.090Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=aiml-debugger","compare_url":"https://unfragile.ai/compare?artifact=aiml-debugger"}},"signature":"WQF5DwoxJCPAKudn4ULvfYdEMmLZXwu9CEYigsxwhNxB5K7jXxvKh9582CtOgRmYCvssIDKXYuwDl3pTO3cmAw==","signedAt":"2026-06-20T16:01:51.478Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/aiml-debugger","artifact":"https://unfragile.ai/aiml-debugger","verify":"https://unfragile.ai/api/v1/verify?slug=aiml-debugger","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}