{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-ms-toolsai-vscode-ai-remote","slug":"azure-machine-learning-remote","name":"Azure Machine Learning - Remote","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-ai-remote","page_url":"https://unfragile.ai/azure-machine-learning-remote","categories":["code-editors"],"tags":["__web_extension","AML","Azure Machine Learning","Azure ML","Deep Learning"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-ms-toolsai-vscode-ai-remote__cap_0","uri":"capability://tool.use.integration.remote.compute.instance.connection.management","name":"remote-compute-instance-connection-management","description":"Establishes and manages persistent WebSocket and VS Code Server connections to Azure Machine Learning Compute Instances via command-palette-driven authentication flow. Uses Azure identity tokens obtained through the parent Azure Machine Learning extension to authenticate connections, maintaining session state across VS Code restarts. Implements automatic server lifecycle management on the remote compute instance with manual kill-switch commands for troubleshooting hung connections.","intents":["Connect VS Code to a remote Azure ML compute instance without manual SSH configuration","Establish a persistent development session that survives VS Code restarts","Troubleshoot connection issues by terminating and restarting remote servers"],"best_for":["Data scientists using Azure ML Studio who want local IDE experience","Teams standardizing on Azure ML for compute management","Developers avoiding SSH key management and terminal-based remote access"],"limitations":["Requires active Azure ML Compute Instance running (incurs compute costs)","No fallback behavior documented if WebSocket connection drops mid-session","Cannot connect to compute instances outside the current Azure ML workspace scope","Depends entirely on Azure infrastructure availability and network connectivity"],"requires":["Visual Studio Code (minimum version unknown)","Azure Machine Learning extension version 0.6.17 or newer","Active Azure subscription with Azure Machine Learning workspace","At least one provisioned Compute Instance in the workspace","Network access to Azure ML websocket endpoints"],"input_types":["Azure subscription credentials (via Azure extension)","Workspace identifier","Compute instance name"],"output_types":["Authenticated remote development session","VS Code Server connection handle"],"categories":["tool-use-integration","remote-development"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-vscode-ai-remote__cap_1","uri":"capability://code.generation.editing.remote.python.script.execution.with.workspace.context","name":"remote-python-script-execution-with-workspace-context","description":"Executes Python scripts on remote Compute Instance with automatic workspace context injection, allowing scripts to access mounted fileshares, datasets, and workspace metadata without explicit path configuration. Implements a run-and-capture pattern that streams stdout/stderr back to VS Code terminal, providing real-time execution feedback. Scripts execute with the Compute Instance's Python environment and installed packages, inheriting all dependencies configured in the instance's conda/pip environment.","intents":["Run a Python training script on GPU-enabled compute without leaving VS Code","Execute data preprocessing scripts that access Azure ML datasets and fileshares","Test code against production compute environment before submitting formal training jobs"],"best_for":["Data scientists iterating on training code","ML engineers validating scripts before submitting to Azure ML pipelines","Teams avoiding local GPU requirements for development"],"limitations":["No built-in job queuing or scheduling—executes immediately on compute instance","No automatic output capture to Azure ML experiment tracking (requires manual logging)","Cannot specify compute resource constraints (GPU/CPU allocation) per script","Execution blocks VS Code terminal until script completes; no background job support","No timeout enforcement documented—long-running scripts can consume compute indefinitely"],"requires":["Remote Compute Instance connection established and active","Python environment configured on Compute Instance","Script file accessible in remote filesystem or local file synced to compute"],"input_types":["Python script (.py file)","Command-line arguments (implicit via script parameters)"],"output_types":["stdout/stderr stream to VS Code terminal","Exit code","Generated files written to remote filesystem"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-vscode-ai-remote__cap_2","uri":"capability://code.generation.editing.remote.jupyter.notebook.execution.and.kernel.management","name":"remote-jupyter-notebook-execution-and-kernel-management","description":"Executes Jupyter notebooks on remote Compute Instance by proxying kernel communication through the established VS Code Server connection. Implements cell-by-cell execution with output streaming back to VS Code's notebook UI, maintaining kernel state across multiple cell executions. Automatically discovers and connects to Jupyter kernels available on the Compute Instance, supporting both default Python kernels and custom conda environments configured on the instance.","intents":["Run exploratory data analysis notebooks on remote compute without downloading large datasets","Collaborate on notebooks by editing locally in VS Code while executing on shared compute","Access GPU-accelerated notebook execution without local hardware"],"best_for":["Data scientists performing interactive analysis on large datasets","Teams sharing Compute Instances for collaborative notebook development","Researchers requiring GPU acceleration for notebook cells"],"limitations":["Notebook state is ephemeral—kernel restarts lose all variable state (no persistence layer)","No built-in notebook versioning or checkpoint system","Cannot specify kernel resource limits (memory, CPU) per notebook session","Notebook outputs are not automatically saved to Azure ML experiment tracking","No support for notebook scheduling or parameterized execution"],"requires":["Remote Compute Instance connection established","Jupyter installed on Compute Instance","Notebook file (.ipynb) accessible in remote filesystem"],"input_types":["Jupyter notebook (.ipynb file)","Cell code (Python or other kernel language)","Kernel selection (if multiple kernels available)"],"output_types":["Cell execution results (stdout, stderr, plots, tables)","Kernel state (variables, imports)","Generated artifacts (files, plots)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-vscode-ai-remote__cap_3","uri":"capability://code.generation.editing.remote.code.debugging.with.breakpoint.support","name":"remote-code-debugging-with-breakpoint-support","description":"Enables interactive debugging of Python code executing on remote Compute Instance by proxying debugger protocol (likely pdb or debugpy) through the VS Code Server connection. Implements breakpoint setting, step-through execution, variable inspection, and call stack navigation in VS Code's debug UI, with all debugging state maintained on the remote instance. Supports both script debugging and notebook cell debugging with automatic debugger attachment.","intents":["Debug training scripts running on GPU compute without reproducing locally","Inspect variable state and execution flow in data processing pipelines","Diagnose failures in production-like compute environments before deployment"],"best_for":["ML engineers debugging complex training pipelines","Data scientists troubleshooting data processing logic","Teams validating code behavior on production compute configurations"],"limitations":["Debugging latency depends on network round-trip time to Compute Instance","No conditional breakpoints or logpoint support documented","Cannot debug multi-process or distributed training scenarios","Debugger state is lost if connection drops; no automatic reconnection","No time-travel debugging or execution recording capability"],"requires":["Remote Compute Instance connection established","Python debugger (debugpy or pdb) installed on Compute Instance","Script or notebook to debug"],"input_types":["Python script or notebook cell","Breakpoint locations (line numbers)","Watch expressions (variable names)"],"output_types":["Debugger UI (breakpoint hits, variable values, call stack)","Step-through execution control","REPL for expression evaluation"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-vscode-ai-remote__cap_4","uri":"capability://automation.workflow.remote.terminal.command.execution.with.shell.access","name":"remote-terminal-command-execution-with-shell-access","description":"Provides shell terminal access to the remote Compute Instance through VS Code's integrated terminal, executing arbitrary commands (bash, PowerShell, etc.) on the instance. Implements bidirectional I/O streaming between VS Code terminal and remote shell, supporting interactive commands, environment variable access, and file operations. Terminal inherits Compute Instance's environment configuration, including PATH, conda environments, and mounted fileshares.","intents":["Run git operations (clone, pull, push) on remote compute without local repository","Execute system commands for environment inspection or package installation","Manage Compute Instance resources (file cleanup, permission changes) directly from VS Code"],"best_for":["DevOps engineers managing Compute Instance infrastructure","Data scientists performing ad-hoc system administration","Teams automating environment setup via shell scripts"],"limitations":["No shell session persistence—terminal state is lost if connection drops","No command history synchronization across multiple VS Code sessions","Cannot execute commands requiring interactive authentication (unless credentials pre-configured)","No built-in command logging or audit trail","Terminal output is not captured to Azure ML experiment tracking"],"requires":["Remote Compute Instance connection established","Shell environment (bash, PowerShell, etc.) available on Compute Instance"],"input_types":["Shell commands (bash, PowerShell, etc.)","Environment variables","File paths"],"output_types":["Command stdout/stderr","Exit codes","File system changes"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-vscode-ai-remote__cap_5","uri":"capability://automation.workflow.remote.git.repository.management.with.workspace.integration","name":"remote-git-repository-management-with-workspace-integration","description":"Enables git operations (clone, pull, push, branch management) on remote Compute Instance through VS Code's source control UI, with automatic integration to workspace-mounted repositories. Implements git command proxying through the remote shell, supporting both HTTPS and SSH-based authentication. Provides visual diff and merge conflict resolution in VS Code's UI while maintaining repository state on the Compute Instance.","intents":["Clone training code repositories directly to Compute Instance without local download","Push experiment results and model artifacts to git without manual file transfer","Manage branches and merge code changes while working on remote compute"],"best_for":["ML teams using git for experiment tracking and code versioning","Data scientists collaborating on shared Compute Instances","DevOps engineers automating code deployment to compute"],"limitations":["Git authentication requires pre-configured credentials on Compute Instance (SSH keys or git credentials)","No built-in git LFS support for large model files","Merge conflicts require manual resolution in VS Code UI; no automated conflict resolution","No integration with Azure Repos or GitHub Actions for CI/CD","Repository size is limited by Compute Instance storage; no automatic cleanup"],"requires":["Remote Compute Instance connection established","Git installed on Compute Instance","Git credentials or SSH keys configured on Compute Instance","Network access to git repository (GitHub, Azure Repos, etc.)"],"input_types":["Repository URL (HTTPS or SSH)","Branch names","Commit messages","File changes for staging/committing"],"output_types":["Repository state (branches, commits, diffs)","Merge conflict markers","Push/pull status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-vscode-ai-remote__cap_6","uri":"capability://tool.use.integration.remote.filesystem.traversal.and.file.access","name":"remote-filesystem-traversal-and-file-access","description":"Provides read/write access to the remote Compute Instance's filesystem through VS Code's file explorer, enabling browsing, opening, editing, and deleting files on the instance. Implements file synchronization between local VS Code editor and remote filesystem, with automatic conflict detection if files are modified externally. Supports access to mounted Azure fileshares and datasets through the Compute Instance's filesystem mount points.","intents":["Browse and edit training scripts, configuration files, and notebooks on remote compute","Access large datasets mounted to Compute Instance without downloading to local machine","Manage experiment outputs and model artifacts stored on remote compute"],"best_for":["Data scientists working with large datasets that can't fit locally","ML engineers managing training code and configuration files on shared compute","Teams collaborating on shared Compute Instances with centralized file storage"],"limitations":["File transfer speed depends on network bandwidth; large file operations may be slow","No built-in file compression or delta sync—entire files transferred on edit","No file locking mechanism; concurrent edits by multiple users can cause conflicts","File size limits unknown; very large files may timeout or fail to transfer","No automatic backup or version history for edited files (relies on git)"],"requires":["Remote Compute Instance connection established","Read/write permissions on Compute Instance filesystem","Sufficient network bandwidth for file transfers"],"input_types":["File paths","File content (for editing)","File operations (create, delete, rename)"],"output_types":["File content (for viewing/editing)","Directory listings","File metadata (size, modification time)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-vscode-ai-remote__cap_7","uri":"capability://tool.use.integration.azure.ml.studio.ui.integration.with.one.click.connection","name":"azure-ml-studio-ui-integration-with-one-click-connection","description":"Integrates with Azure Machine Learning Studio web UI through click-out links and 'Edit in VS Code' buttons, enabling one-click connection to Compute Instances from Notebook and Compute tabs. Implements deep linking from Azure ML Studio to VS Code with automatic connection establishment, eliminating manual workspace/instance selection. Provides inline VS Code launch button on Compute Instance cards in Azure ML Studio UI.","intents":["Launch VS Code development session directly from Azure ML Studio without manual connection steps","Switch between Azure ML Studio UI and VS Code editor without context switching","Discover available Compute Instances in Azure ML Studio and connect to them with one click"],"best_for":["Azure ML Studio users wanting to transition to VS Code for development","Teams standardizing on Azure ML for compute management and discovery","Non-technical users who prefer UI-driven connection over command-line setup"],"limitations":["Requires Azure ML extension to be installed and configured","One-click connection only works from Azure ML Studio web UI; no programmatic API","No support for connecting to Compute Instances outside the current workspace","Deep linking may fail if VS Code is not installed or extension is disabled"],"requires":["Azure Machine Learning extension installed in VS Code","Active Azure ML workspace with Compute Instances","Access to Azure ML Studio web UI (ml.azure.com)","VS Code installed and registered as default handler for vscode:// protocol"],"input_types":["Click-out link from Azure ML Studio","'Edit in VS Code' button","Compute Instance card inline button"],"output_types":["Authenticated VS Code connection to Compute Instance","File explorer showing remote filesystem"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-vscode-ai-remote__cap_8","uri":"capability://automation.workflow.compute.instance.lifecycle.troubleshooting.with.server.kill.commands","name":"compute-instance-lifecycle-troubleshooting-with-server-kill-commands","description":"Provides manual kill-switch commands to terminate hung or unresponsive VS Code Server and WebSocket servers on remote Compute Instance, enabling recovery from connection failures without restarting the entire instance. Implements server process termination through Azure ML backend APIs, with automatic server restart on next connection attempt. Supports troubleshooting scenarios where servers become unresponsive due to network issues or resource exhaustion.","intents":["Recover from hung VS Code Server without restarting Compute Instance","Clear stuck WebSocket connections that prevent new sessions from connecting","Troubleshoot connection issues by forcing server restart"],"best_for":["DevOps engineers managing Compute Instance infrastructure","Support teams troubleshooting user connection issues","Users experiencing hung connections who need quick recovery"],"limitations":["Killing servers terminates all active sessions; no graceful shutdown","No automatic detection of hung servers; requires manual intervention","Server restart may take 30+ seconds; no progress indication","No logging of server termination events for audit purposes","Repeated server kills may indicate underlying infrastructure issues (not addressed by this command)"],"requires":["Remote Compute Instance connection established (or Azure ML workspace access)","Permission to manage Compute Instance servers"],"input_types":["Command: 'Azure ML: Kill VS Code server on Compute Instance'","Command: 'Azure ML: Kill Azure ML websocket server on Compute Instance'"],"output_types":["Server termination confirmation","Status message indicating server restart"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-vscode-ai-remote__cap_9","uri":"capability://safety.moderation.telemetry.collection.with.opt.out.control","name":"telemetry-collection-with-opt-out-control","description":"Collects usage telemetry data on extension interactions (connection events, command execution, errors) and sends to Microsoft servers for product improvement analysis. Implements opt-out mechanism through VS Code's standard `telemetry.enableTelemetry` setting, allowing users to disable telemetry collection globally. Respects user privacy preferences without requiring extension-specific configuration.","intents":["Allow Microsoft to collect usage data for product improvement and feature prioritization","Disable telemetry collection for privacy-sensitive environments (healthcare, finance)","Comply with organizational data governance policies restricting cloud telemetry"],"best_for":["Microsoft product teams analyzing feature usage and adoption","Enterprise organizations with strict data governance requirements","Privacy-conscious users wanting to minimize data collection"],"limitations":["Telemetry opt-out is global (affects all VS Code extensions, not just this one)","No granular control over which telemetry events are collected","No visibility into what data is collected or how it's used","Telemetry data may be retained by Microsoft indefinitely","No local telemetry logging for audit purposes"],"requires":["VS Code telemetry setting accessible (may be restricted in managed environments)"],"input_types":["VS Code setting: telemetry.enableTelemetry (true/false)"],"output_types":["Telemetry data sent to Microsoft (content unknown)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["Visual Studio Code (minimum version unknown)","Azure Machine Learning extension version 0.6.17 or newer","Active Azure subscription with Azure Machine Learning workspace","At least one provisioned Compute Instance in the workspace","Network access to Azure ML websocket endpoints","Remote Compute Instance connection established and active","Python environment configured on Compute Instance","Script file accessible in remote filesystem or local file synced to compute","Remote Compute Instance connection established","Jupyter installed on Compute Instance"],"failure_modes":["Requires active Azure ML Compute Instance running (incurs compute costs)","No fallback behavior documented if WebSocket connection drops mid-session","Cannot connect to compute instances outside the current Azure ML workspace scope","Depends entirely on Azure infrastructure availability and network connectivity","No built-in job queuing or scheduling—executes immediately on compute instance","No automatic output capture to Azure ML experiment tracking (requires manual logging)","Cannot specify compute resource constraints (GPU/CPU allocation) per script","Execution blocks VS Code terminal until script completes; no background job support","No timeout enforcement documented—long-running scripts can consume compute indefinitely","Notebook state is ephemeral—kernel restarts lose all variable state (no persistence layer)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.88,"quality":0.3,"ecosystem":0.3,"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":"2026-05-03T15:20:29.937Z","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=azure-machine-learning-remote","compare_url":"https://unfragile.ai/compare?artifact=azure-machine-learning-remote"}},"signature":"ag4Sw/DqkCWq0IWVjBQ5Zb7hM5XvBn5N+caFENw+ucI9DBV5XTSsmUI4lsau1fVPehmRDw46v1sC3iLk95UWCg==","signedAt":"2026-06-21T06:15:58.513Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/azure-machine-learning-remote","artifact":"https://unfragile.ai/azure-machine-learning-remote","verify":"https://unfragile.ai/api/v1/verify?slug=azure-machine-learning-remote","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"}}