{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-pipedreamin-pipedream-ml","slug":"pipedream-ml","name":"Pipedream ML","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=PipedreamIN.pipedream-ml","page_url":"https://unfragile.ai/pipedream-ml","categories":["model-training"],"tags":["aws","deep-learning","machine-learning","ml","pytorch","sagemaker","tensorflow","training"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-pipedreamin-pipedream-ml__cap_0","uri":"capability://automation.workflow.aws.sagemaker.training.job.orchestration.from.vs.code","name":"aws sagemaker training job orchestration from vs code","description":"Submits ML training jobs to AWS SageMaker backend via REST API calls triggered from VS Code sidebar or command palette, handling job lifecycle management (creation, monitoring, termination) without local execution. The extension acts as a thin client that serializes project configuration and hyperparameters into SageMaker API requests, polling the backend for status updates and streaming live training logs back to the editor via WebSocket or HTTP long-polling.","intents":["I want to train my ML model on AWS compute without leaving VS Code","I need to offload training to cloud resources while keeping my local machine free","I want to submit multiple training jobs and monitor them in parallel from the editor"],"best_for":["ML engineers developing locally but training on AWS SageMaker","Teams prototyping models in VS Code and scaling to cloud infrastructure","Developers who want integrated training without CLI context switching"],"limitations":["All training execution happens remotely — no local training fallback if AWS is unavailable","Compute resource selection is opaque and auto-configured by backend; no manual instance type selection documented","Training job submission requires internet connectivity and active Pipedream API backend","No support for distributed training configuration or custom SageMaker job parameters beyond framework defaults"],"requires":["VS Code 1.85.0 or higher","Active Pipedream account with API key configured","AWS account with SageMaker permissions (managed server-side by Pipedream)","Internet connection for API communication"],"input_types":["Python training script (train.py)","Project folder structure with framework detection","Hyperparameter configuration from UI form"],"output_types":["SageMaker training job ID","Real-time training logs streamed to VS Code output panel","Job status (running, completed, failed)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-pipedreamin-pipedream-ml__cap_1","uri":"capability://code.generation.editing.automatic.ml.framework.detection.and.project.scaffolding","name":"automatic ml framework detection and project scaffolding","description":"Scans the current VS Code project folder to identify installed ML frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost, HuggingFace) by analyzing imports in Python files, requirements.txt, or setup.py. When no framework is detected, offers template scaffolding that generates a starter train.py with framework-specific boilerplate code and a default hyperparameter configuration suitable for SageMaker execution.","intents":["I want to quickly set up a new ML project with framework-specific templates","I need the extension to understand what framework I'm already using without manual configuration","I want to convert an existing Python project into a SageMaker-compatible training setup"],"best_for":["ML practitioners starting new projects who want framework-specific boilerplate","Teams onboarding to SageMaker training who need standardized project structure","Developers migrating existing ML code to cloud training"],"limitations":["Framework detection is static (reads files at extension activation time; doesn't watch for dynamic imports)","Scaffolding templates are limited to 5 pre-defined frameworks — no custom framework support","Generated train.py templates may require manual editing for domain-specific logic","No detection of framework versions — assumes latest compatible version for SageMaker"],"requires":["VS Code 1.85.0 or higher","Python project folder with readable file system access","Optional: requirements.txt or setup.py for dependency detection"],"input_types":["Project folder structure","Python source files with import statements","requirements.txt or setup.py (optional)"],"output_types":["Framework identification (PyTorch, TensorFlow, scikit-learn, XGBoost, HuggingFace)","Generated train.py template file","Default hyperparameter configuration JSON"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-pipedreamin-pipedream-ml__cap_2","uri":"capability://automation.workflow.real.time.training.log.streaming.and.job.monitoring","name":"real-time training log streaming and job monitoring","description":"Polls the Pipedream backend at configurable intervals (default unknown, configurable via pipedream.autoRefreshInterval setting) to fetch live training logs from SageMaker jobs and streams them to a VS Code output panel. Displays job status (running, completed, failed) and allows users to view logs without switching to AWS console. Implements auto-refresh with configurable polling frequency to balance responsiveness and API call overhead.","intents":["I want to watch my training progress in real-time without opening AWS console","I need to debug training failures by reading live logs in my editor","I want to monitor multiple training jobs simultaneously from VS Code"],"best_for":["ML engineers iterating on models who need fast feedback loops","Teams debugging training failures without AWS console context switching","Developers monitoring long-running training jobs while coding"],"limitations":["Log streaming is polling-based, not event-driven — introduces latency between log generation and display (polling interval + network round-trip)","No filtering or search functionality for logs — users must scroll through full output","Polling frequency is global; cannot set per-job refresh rates","Log history is not persisted — closing the output panel loses previous logs"],"requires":["VS Code 1.85.0 or higher","Active training job running on SageMaker","Internet connectivity to Pipedream API","Pipedream API key configured"],"input_types":["SageMaker training job ID","Polling interval configuration (seconds)"],"output_types":["Live training logs (text stream)","Job status updates (running/completed/failed)","Timestamps for log entries"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-pipedreamin-pipedream-ml__cap_3","uri":"capability://data.processing.analysis.dataset.upload.and.download.management.for.sagemaker","name":"dataset upload and download management for sagemaker","description":"Provides UI commands to upload local dataset files to SageMaker-compatible storage (likely S3 via Pipedream backend) and download trained model artifacts back to the local project folder. Handles file serialization and transfer via REST API calls to the Pipedream orchestrator, which manages AWS credentials and S3 bucket configuration server-side. Users select local files or folders and the extension batches them for upload without manual S3 configuration.","intents":["I want to upload my training dataset to SageMaker without configuring S3 buckets","I need to download trained models and artifacts after training completes","I want to manage datasets for multiple training jobs from the editor"],"best_for":["ML practitioners who want simplified dataset management without AWS S3 knowledge","Teams training models on SageMaker who need integrated artifact retrieval","Developers iterating on models and needing quick dataset/artifact cycles"],"limitations":["No progress indicators or resumable uploads for large datasets — single-shot transfer","No dataset versioning or metadata management — files uploaded with basic naming","Upload/download speed depends on internet bandwidth and Pipedream backend capacity","No built-in dataset validation or format checking before upload","File size limits unknown — may have quota restrictions per Pipedream plan"],"requires":["VS Code 1.85.0 or higher","Local dataset files or folders in project directory","Internet connectivity for file transfer","Pipedream API key and active account"],"input_types":["Local file paths (CSV, Parquet, images, etc.)","Folder paths for batch upload"],"output_types":["S3 URI or storage path for uploaded dataset","Downloaded model artifacts (pickle, .pt, .h5, etc.)","Upload/download status confirmation"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-pipedreamin-pipedream-ml__cap_4","uri":"capability://automation.workflow.hyperparameter.configuration.ui.and.job.submission","name":"hyperparameter configuration ui and job submission","description":"Provides a form-based UI in the VS Code sidebar for setting training hyperparameters (learning rate, batch size, epochs, optimizer, etc.) with framework-specific defaults. Serializes user-configured hyperparameters into JSON and submits them alongside the training script to the Pipedream backend, which passes them to SageMaker as environment variables or job configuration. The extension validates basic parameter types (numeric ranges, enum selections) before submission.","intents":["I want to configure hyperparameters without editing train.py or using CLI flags","I need framework-specific parameter suggestions and defaults","I want to submit training jobs with different hyperparameter sets quickly"],"best_for":["ML practitioners experimenting with hyperparameters who want UI-driven configuration","Teams running hyperparameter sweeps with different parameter combinations","Developers who prefer visual configuration over CLI or code editing"],"limitations":["Hyperparameter schema is not documented — unclear which parameters are supported per framework","No hyperparameter validation beyond basic type checking — invalid ranges may fail at SageMaker runtime","No hyperparameter history or comparison — cannot track which parameters were used for past jobs","No built-in hyperparameter optimization (Bayesian search, grid search) — only manual parameter entry","Parameter UI is static — cannot dynamically add custom parameters not in the predefined schema"],"requires":["VS Code 1.85.0 or higher","Pipedream API key configured","Training script (train.py) that accepts hyperparameters via environment variables or config files"],"input_types":["Numeric values (learning rate, batch size, epochs)","Enum selections (optimizer, activation functions)","Boolean flags (dropout, batch normalization)"],"output_types":["Hyperparameter JSON configuration","SageMaker training job submission with parameters","Job ID for tracking"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-pipedreamin-pipedream-ml__cap_5","uri":"capability://automation.workflow.training.job.lifecycle.management.start.stop.status.tracking","name":"training job lifecycle management (start, stop, status tracking)","description":"Implements commands to start training jobs (Run Training), terminate active jobs (Stop Training), and poll job status from SageMaker backend. Maintains in-memory state of active jobs and displays status in sidebar or status bar. Uses REST API calls to Pipedream backend to submit job termination requests and fetch current job state. Provides visual indicators (icons, status text) for job states (queued, running, completed, failed).","intents":["I want to start a training job and monitor its status without AWS console","I need to cancel a training job that's taking too long or using too many resources","I want to see the status of all my active training jobs in one place"],"best_for":["ML engineers managing multiple concurrent training jobs","Teams needing quick job control without AWS console access","Developers iterating rapidly and needing to cancel failed experiments"],"limitations":["Job state polling introduces latency — status may be stale by 5-30 seconds depending on polling interval","No job history or past job listing — only current active jobs tracked","Job termination is asynchronous — extension doesn't confirm termination completion","No job queuing or scheduling — jobs start immediately or fail if resources unavailable","No job dependencies or conditional workflows — each job is independent"],"requires":["VS Code 1.85.0 or higher","Pipedream API key configured","Training script and hyperparameter configuration ready","Internet connectivity for API calls"],"input_types":["Training job configuration (script, hyperparameters, framework)","SageMaker training job ID (for stop/status commands)"],"output_types":["Training job ID","Job status (queued, running, completed, failed)","Timestamp of last status update"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-pipedreamin-pipedream-ml__cap_6","uri":"capability://automation.workflow.aws.cost.tracking.and.quota.monitoring","name":"aws cost tracking and quota monitoring","description":"Displays estimated or actual AWS spending for training jobs and monitors usage against Pipedream plan quotas (job count, compute hours, storage). Fetches cost data from Pipedream backend (which aggregates SageMaker billing) and displays in sidebar or status bar. Implements quota checking before job submission to prevent overage. Cost tracking is updated periodically or on-demand via Check Quota command.","intents":["I want to monitor my AWS spending for training jobs without logging into billing console","I need to know my remaining quota before submitting expensive training jobs","I want to track cost per training job to optimize compute resource usage"],"best_for":["Teams with limited ML budgets who need cost visibility","Individual developers tracking personal AWS spending","Organizations enforcing quota limits on training resource usage"],"limitations":["Cost data is aggregated by Pipedream backend — no per-job cost breakdown visible in extension","Quota limits are plan-based and not customizable — cannot set custom spending caps","Cost estimates may not match final AWS billing due to rounding or additional charges","No cost optimization recommendations or alerts for expensive jobs","Cost history is not persisted — only current quota status displayed"],"requires":["VS Code 1.85.0 or higher","Pipedream account with active billing configured","Internet connectivity to fetch cost data"],"input_types":["Pipedream plan type (determines quota limits)"],"output_types":["Current spending (USD or currency)","Remaining quota (job count, compute hours, storage GB)","Quota status (OK, warning, exceeded)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-pipedreamin-pipedream-ml__cap_7","uri":"capability://safety.moderation.api.key.configuration.and.authentication.management","name":"api key configuration and authentication management","description":"Implements secure API key storage and configuration via VS Code Secrets API (or similar secure storage mechanism). Users run 'Pipedream: Configure API Key' command, which opens a prompt to enter/update their Pipedream API key. The extension stores the key securely in VS Code's credential storage and uses it for all subsequent API calls to the Pipedream backend. Supports key rotation and validation on first use.","intents":["I want to securely authenticate with Pipedream without hardcoding credentials","I need to update my API key when it expires or is compromised","I want the extension to automatically use my credentials for all API calls"],"best_for":["Individual developers setting up Pipedream ML for the first time","Teams managing shared VS Code instances with credential rotation","Security-conscious organizations requiring secure credential storage"],"limitations":["API key storage mechanism is not documented — unclear if using VS Code Secrets API or local file encryption","No key expiration or rotation reminders — users must manually manage key lifecycle","No multi-account support — only one API key per VS Code instance","No API key validation until first API call — invalid keys fail silently at runtime","Credentials are tied to VS Code profile — not portable across machines or editors"],"requires":["VS Code 1.85.0 or higher","Active Pipedream account with API key generated at pipedream.in/api-keys","Internet connectivity to validate key on first use"],"input_types":["Pipedream API key (string, 32+ characters)"],"output_types":["Authentication status (valid/invalid)","Confirmation message"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-pipedreamin-pipedream-ml__cap_8","uri":"capability://automation.workflow.extension.settings.and.configuration.management","name":"extension settings and configuration management","description":"Exposes configurable settings via VS Code settings.json (or UI settings panel) for API endpoint URL, default framework, auto-refresh polling interval, and notification preferences. Settings are stored in VS Code's configuration system and read at extension activation. Allows users to customize behavior without modifying extension code. Includes validation for setting values (e.g., polling interval must be positive integer).","intents":["I want to change the default ML framework for new projects","I need to adjust log polling frequency to balance responsiveness and API load","I want to disable notifications for training job completion"],"best_for":["Advanced users customizing extension behavior","Teams with custom Pipedream deployments using non-default API endpoints","Developers managing notification preferences"],"limitations":["Settings changes require VS Code reload to take effect — no hot-reload","No validation UI for setting values — invalid values may cause runtime errors","Limited settings scope — only 6 documented settings, no advanced configuration","No settings profiles or per-workspace configuration — settings are global","No settings migration when extension updates — users must manually update config"],"requires":["VS Code 1.85.0 or higher","Access to VS Code settings.json or settings UI"],"input_types":["String (API URL, default framework)","Integer (polling interval in seconds)","Boolean (notification toggles)"],"output_types":["Updated extension behavior","Validation errors for invalid settings"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-pipedreamin-pipedream-ml__cap_9","uri":"capability://code.generation.editing.framework.specific.training.script.templates.and.boilerplate.generation","name":"framework-specific training script templates and boilerplate generation","description":"Generates framework-specific train.py templates with boilerplate code for PyTorch, TensorFlow, scikit-learn, XGBoost, and HuggingFace. Templates include standard training loops, loss functions, optimizer setup, and hyperparameter loading from environment variables. Generated scripts are SageMaker-compatible and accept hyperparameters via environment variables or config files. Users can customize templates after generation.","intents":["I want a starting point for a new training script without writing boilerplate","I need SageMaker-compatible training code that reads hyperparameters from environment","I want framework-specific best practices baked into generated code"],"best_for":["ML practitioners new to SageMaker who need training script templates","Teams standardizing on framework-specific training patterns","Developers prototyping models quickly without manual boilerplate"],"limitations":["Generated templates are generic — require customization for domain-specific logic","No support for multi-GPU or distributed training in templates","Templates assume standard supervised learning — no support for RL, unsupervised, or custom training loops","No template versioning — users get latest template version, may break if framework APIs change","Limited customization options — cannot specify template parameters like model architecture"],"requires":["VS Code 1.85.0 or higher","Pipedream ML extension installed"],"input_types":["Framework selection (PyTorch, TensorFlow, scikit-learn, XGBoost, HuggingFace)"],"output_types":["Generated train.py file with framework-specific boilerplate","Example hyperparameter configuration","Comments explaining key sections"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["VS Code 1.85.0 or higher","Active Pipedream account with API key configured","AWS account with SageMaker permissions (managed server-side by Pipedream)","Internet connection for API communication","Python project folder with readable file system access","Optional: requirements.txt or setup.py for dependency detection","Active training job running on SageMaker","Internet connectivity to Pipedream API","Pipedream API key configured","Local dataset files or folders in project directory"],"failure_modes":["All training execution happens remotely — no local training fallback if AWS is unavailable","Compute resource selection is opaque and auto-configured by backend; no manual instance type selection documented","Training job submission requires internet connectivity and active Pipedream API backend","No support for distributed training configuration or custom SageMaker job parameters beyond framework defaults","Framework detection is static (reads files at extension activation time; doesn't watch for dynamic imports)","Scaffolding templates are limited to 5 pre-defined frameworks — no custom framework support","Generated train.py templates may require manual editing for domain-specific logic","No detection of framework versions — assumes latest compatible version for SageMaker","Log streaming is polling-based, not event-driven — introduces latency between log generation and display (polling interval + network round-trip)","No filtering or search functionality for logs — users must scroll through full output","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.32,"quality":0.45,"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":"2026-05-03T15:20:36.253Z","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=pipedream-ml","compare_url":"https://unfragile.ai/compare?artifact=pipedream-ml"}},"signature":"N87Edwc9MGuuGw8dL6wPNULjCx1JQJHsto6/g1MdPPor9fNW8eHdL6Zc+lqdSBa1G0kQKuYwXo2M74Tjsu5MDQ==","signedAt":"2026-06-21T03:19:05.410Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pipedream-ml","artifact":"https://unfragile.ai/pipedream-ml","verify":"https://unfragile.ai/api/v1/verify?slug=pipedream-ml","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"}}