{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_clear-ml","slug":"clear-ml","name":"Clear.ml","type":"product","url":"https://clear.ml","page_url":"https://unfragile.ai/clear-ml","categories":["model-training"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_clear-ml__cap_0","uri":"capability://mlops.automatic.experiment.tracking","name":"automatic-experiment-tracking","description":"Automatically captures and logs experiment metadata including hyperparameters, metrics, and artifacts with minimal code instrumentation. Integrates directly with popular ML frameworks to record training runs without requiring extensive manual logging.","intents":["I want to track all my model experiments without writing boilerplate logging code","I need to compare metrics across multiple training runs automatically","I want to capture hyperparameters and artifacts without manual intervention"],"best_for":["data scientists running iterative experiments","ML teams using popular frameworks like PyTorch, TensorFlow, scikit-learn"],"limitations":["Requires using supported ML frameworks for automatic capture","Custom metrics may need explicit logging"],"requires":["ClearML SDK integration in training code","Supported ML framework (PyTorch, TensorFlow, etc.)"],"input_types":["training script with ML framework code","hyperparameters","metrics during training"],"output_types":["experiment metadata","logged metrics and artifacts","experiment comparison data"],"categories":["mlops","productivity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clear-ml__cap_1","uri":"capability://mlops.distributed.task.orchestration","name":"distributed-task-orchestration","description":"Schedules and manages distributed ML tasks across multiple machines and GPUs without requiring external orchestration tools. Handles resource allocation, task queuing, and execution coordination for parallel workloads.","intents":["I want to run hyperparameter sweeps across multiple GPUs automatically","I need to distribute training jobs across a cluster without manual coordination","I want to queue and schedule tasks based on available resources"],"best_for":["teams running distributed training workflows","organizations with multi-GPU or multi-machine setups","enterprises needing resource-aware task scheduling"],"limitations":["Requires infrastructure setup and configuration","More complex than single-machine training workflows"],"requires":["ClearML agent deployment on worker machines","Network connectivity between machines","Configured resource pools and queues"],"input_types":["task definitions","hyperparameter grids","resource constraints"],"output_types":["scheduled tasks","execution logs","resource utilization metrics"],"categories":["mlops","infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clear-ml__cap_10","uri":"capability://mlops.web.ui.experiment.dashboard","name":"web-ui-experiment-dashboard","description":"Provides a web-based interface for viewing, filtering, and managing experiments with dashboards for metrics visualization and experiment comparison. Enables team collaboration and experiment discovery through centralized UI.","intents":["I want to view all my experiments in one place with their metrics","I need to filter and search experiments by various criteria","I want to share experiment results with my team through a web interface"],"best_for":["data science teams collaborating on experiments","organizations requiring centralized experiment visibility","teams without command-line preference"],"limitations":["UI can feel cluttered with many features","Navigation less intuitive compared to more streamlined competitors","Performance may degrade with very large numbers of experiments"],"requires":["ClearML server deployment","Web browser access","Tracked experiments in ClearML"],"input_types":["experiment metadata","metrics and artifacts"],"output_types":["web dashboard views","experiment comparison visualizations","filterable experiment lists"],"categories":["mlops","collaboration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clear-ml__cap_11","uri":"capability://mlops.team.collaboration.and.access.control","name":"team-collaboration-and-access-control","description":"Manages user access, permissions, and team collaboration features within the ClearML platform. Enables sharing of experiments, models, and resources across team members with granular access control.","intents":["I want to share my experiments with team members","I need to control who can access and modify specific experiments","I want to organize team members into groups with different permission levels"],"best_for":["teams with multiple data scientists and engineers","organizations with security and compliance requirements","enterprises managing access across departments"],"limitations":["Requires user management infrastructure setup","Permission model complexity depends on organizational structure"],"requires":["ClearML server deployment","User authentication system","Configured access control policies"],"input_types":["user credentials","permission specifications","team definitions"],"output_types":["user accounts and roles","access control policies","audit logs"],"categories":["mlops","collaboration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clear-ml__cap_12","uri":"capability://mlops.integration.with.popular.ml.frameworks","name":"integration-with-popular-ml-frameworks","description":"Provides native integrations and auto-logging capabilities with popular ML frameworks like PyTorch, TensorFlow, scikit-learn, and others. Automatically captures framework-specific metadata without requiring manual instrumentation.","intents":["I want automatic logging of my PyTorch/TensorFlow training without code changes","I need to capture framework-specific metrics and model architecture information","I want seamless integration with my existing ML framework workflow"],"best_for":["teams using popular ML frameworks","data scientists wanting minimal instrumentation overhead","organizations standardizing on specific frameworks"],"limitations":["Only works with supported frameworks","Auto-logging may miss custom training loops","Framework-specific features may not be fully captured"],"requires":["ClearML SDK","Supported ML framework installed","Framework version compatibility"],"input_types":["training script using supported framework","framework configuration"],"output_types":["auto-logged metrics and artifacts","framework-specific metadata","model architecture information"],"categories":["mlops","integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clear-ml__cap_13","uri":"capability://mlops.data.versioning.and.lineage.tracking","name":"data-versioning-and-lineage-tracking","description":"Tracks data versions and maintains lineage information showing which datasets were used in which experiments. Enables reproducibility by documenting the complete data pipeline from source to model training.","intents":["I want to know which dataset version was used in each experiment","I need to reproduce an experiment with the exact same data","I want to track how data changes affect model performance"],"best_for":["teams requiring reproducibility and audit trails","organizations with strict data governance requirements","data science teams managing multiple data versions"],"limitations":["Requires explicit data versioning setup","Storage overhead for maintaining multiple data versions","Lineage tracking adds complexity to pipeline management"],"requires":["Data versioning infrastructure","Artifact storage backend","Explicit data lineage documentation"],"input_types":["dataset files","data processing scripts","lineage metadata"],"output_types":["versioned datasets","data lineage graphs","reproducibility reports"],"categories":["mlops","data-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clear-ml__cap_2","uri":"capability://mlops.hyperparameter.sweep.execution","name":"hyperparameter-sweep-execution","description":"Automatically generates and executes multiple training runs with different hyperparameter combinations across available compute resources. Manages the sweep configuration, task creation, and result aggregation.","intents":["I want to run a grid or random search over hyperparameters automatically","I need to find optimal hyperparameters without manual trial-and-error","I want to parallelize hyperparameter tuning across multiple machines"],"best_for":["data scientists optimizing model performance","teams with access to distributed compute resources","researchers exploring hyperparameter sensitivity"],"limitations":["Computational cost scales with number of hyperparameter combinations","Requires predefined hyperparameter search space"],"requires":["ClearML task orchestration setup","Distributed compute resources","Defined hyperparameter ranges"],"input_types":["training script","hyperparameter grid or search space","optimization metric"],"output_types":["sweep results with metrics for each combination","best hyperparameters found","performance comparison across runs"],"categories":["mlops","optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clear-ml__cap_3","uri":"capability://mlops.model.versioning.and.artifact.management","name":"model-versioning-and-artifact-management","description":"Stores, versions, and manages trained models and associated artifacts with automatic tracking of model lineage and metadata. Enables retrieval and comparison of different model versions across experiments.","intents":["I want to version my trained models and track which experiment produced them","I need to retrieve a specific model version from a past experiment","I want to compare performance metrics across different model versions"],"best_for":["teams managing multiple model iterations","organizations requiring model reproducibility","data scientists comparing model performance over time"],"limitations":["Storage requirements scale with model size and number of versions","Requires configured artifact storage backend"],"requires":["ClearML experiment tracking integration","Artifact storage backend (local, S3, GCS, etc.)","Sufficient storage capacity"],"input_types":["trained model files","model metadata","experiment context"],"output_types":["versioned model artifacts","model metadata and lineage","model comparison reports"],"categories":["mlops","model-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clear-ml__cap_4","uri":"capability://mlops.experiment.comparison.and.analysis","name":"experiment-comparison-and-analysis","description":"Provides tools to compare metrics, hyperparameters, and results across multiple experiments in a unified interface. Enables visualization and statistical analysis of experiment differences.","intents":["I want to compare metrics across multiple training runs side-by-side","I need to identify which hyperparameters had the biggest impact on performance","I want to visualize how different experiments performed relative to each other"],"best_for":["data scientists analyzing experiment results","teams conducting ablation studies","researchers comparing model variants"],"limitations":["Comparison quality depends on consistent metric logging","UI can feel cluttered with many experiments"],"requires":["Multiple tracked experiments in ClearML","Consistent metric logging across experiments"],"input_types":["experiment metadata","logged metrics","hyperparameters"],"output_types":["comparison tables","visualization charts","statistical analysis"],"categories":["mlops","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clear-ml__cap_5","uri":"capability://infrastructure.self.hosted.deployment.and.management","name":"self-hosted-deployment-and-management","description":"Enables on-premise deployment of the entire ClearML platform with full control over infrastructure, data storage, and security. Provides flexibility to customize and audit all components without vendor lock-in.","intents":["I want to run ClearML on my own infrastructure for data privacy","I need to avoid cloud vendor lock-in and maintain full control","I want to audit and customize the platform to meet compliance requirements"],"best_for":["enterprises with strict data privacy requirements","organizations rejecting cloud-dependent solutions","teams with DevOps resources for infrastructure management"],"limitations":["Requires dedicated DevOps resources for setup and maintenance","Steeper operational overhead compared to managed alternatives","Responsible for security patches and updates"],"requires":["On-premise infrastructure (servers, storage, networking)","DevOps expertise for deployment and maintenance","Database backend (MongoDB, PostgreSQL, etc.)","Container orchestration platform (optional but recommended)"],"input_types":["infrastructure specifications","configuration parameters","deployment manifests"],"output_types":["deployed ClearML instance","system logs and monitoring data","backup and recovery artifacts"],"categories":["infrastructure","mlops"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clear-ml__cap_6","uri":"capability://mlops.framework.agnostic.metric.logging","name":"framework-agnostic-metric-logging","description":"Captures and logs custom metrics, plots, and data from any ML framework or training process through a flexible SDK. Supports diverse metric types and visualization formats without framework-specific constraints.","intents":["I want to log custom metrics from my training process regardless of framework","I need to track non-standard metrics specific to my use case","I want to log plots and visualizations alongside my metrics"],"best_for":["teams using multiple or custom ML frameworks","researchers with non-standard metric requirements","organizations with domain-specific metrics"],"limitations":["Requires explicit logging code for custom metrics","Performance impact depends on logging frequency and data volume"],"requires":["ClearML SDK integration","Explicit metric logging in training code"],"input_types":["scalar metrics","plots and visualizations","custom data structures"],"output_types":["logged metrics","visualization artifacts","metric time-series data"],"categories":["mlops","logging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clear-ml__cap_7","uri":"capability://mlops.model.deployment.and.serving","name":"model-deployment-and-serving","description":"Facilitates packaging and deploying trained models to production environments with support for multiple serving frameworks and inference engines. Manages model serving infrastructure and enables A/B testing of model versions.","intents":["I want to deploy my trained model to production easily","I need to serve multiple model versions and run A/B tests","I want to manage model serving infrastructure without manual configuration"],"best_for":["teams moving models from experimentation to production","organizations running multiple model versions in parallel","data science teams without dedicated ML engineering resources"],"limitations":["Deployment complexity depends on target infrastructure","May require additional configuration for custom serving needs"],"requires":["Trained model in ClearML artifact storage","Target deployment infrastructure","Serving framework configuration"],"input_types":["trained model artifact","serving configuration","deployment target specification"],"output_types":["deployed model endpoint","serving logs and metrics","A/B test results"],"categories":["mlops","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clear-ml__cap_8","uri":"capability://mlops.resource.monitoring.and.utilization.tracking","name":"resource-monitoring-and-utilization-tracking","description":"Monitors compute resource usage (CPU, GPU, memory) across training tasks and provides visibility into resource allocation and efficiency. Enables optimization of resource utilization across the cluster.","intents":["I want to see how much GPU and CPU each training job is using","I need to identify resource bottlenecks in my training pipeline","I want to optimize resource allocation across my cluster"],"best_for":["teams managing shared compute resources","organizations optimizing infrastructure costs","DevOps teams monitoring cluster health"],"limitations":["Monitoring overhead depends on collection frequency","Requires agent deployment on worker machines"],"requires":["ClearML agent deployment on compute nodes","Network connectivity for metrics collection","Monitoring backend configuration"],"input_types":["system metrics from worker machines","task execution logs"],"output_types":["resource utilization dashboards","performance metrics","resource allocation recommendations"],"categories":["mlops","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clear-ml__cap_9","uri":"capability://mlops.pipeline.workflow.orchestration","name":"pipeline-workflow-orchestration","description":"Defines and executes multi-stage ML pipelines with dependencies between tasks, enabling complex workflows that combine data processing, training, and evaluation stages. Manages task execution order and data flow between pipeline stages.","intents":["I want to create a workflow that runs data preprocessing, then training, then evaluation automatically","I need to manage dependencies between different stages of my ML pipeline","I want to reuse pipeline components across different projects"],"best_for":["teams with complex multi-stage ML workflows","organizations automating end-to-end ML processes","data science teams building production pipelines"],"limitations":["Requires understanding of pipeline structure and dependencies","Debugging complex pipelines can be challenging"],"requires":["ClearML task definitions for each pipeline stage","Configured task dependencies","Artifact passing between stages"],"input_types":["pipeline definition","task specifications","dependency graph"],"output_types":["executed pipeline","pipeline execution logs","final pipeline artifacts"],"categories":["mlops","workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["ClearML SDK integration in training code","Supported ML framework (PyTorch, TensorFlow, etc.)","ClearML agent deployment on worker machines","Network connectivity between machines","Configured resource pools and queues","ClearML server deployment","Web browser access","Tracked experiments in ClearML","User authentication system","Configured access control policies"],"failure_modes":["Requires using supported ML frameworks for automatic capture","Custom metrics may need explicit logging","Requires infrastructure setup and configuration","More complex than single-machine training workflows","UI can feel cluttered with many features","Navigation less intuitive compared to more streamlined competitors","Performance may degrade with very large numbers of experiments","Requires user management infrastructure setup","Permission model complexity depends on organizational structure","Only works with supported frameworks","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.43333333333333335,"quality":0.86,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:29.716Z","last_scraped_at":"2026-04-05T13:23:42.537Z","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=clear-ml","compare_url":"https://unfragile.ai/compare?artifact=clear-ml"}},"signature":"9ssO9geoNLijT1sNZWFR4fLha+9AWSyOqkluRJwi9BVe4AbAdZY3UD0Rb37IMXUz2IEggPo4hI/xfqtxwcFECA==","signedAt":"2026-06-20T00:11:41.370Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/clear-ml","artifact":"https://unfragile.ai/clear-ml","verify":"https://unfragile.ai/api/v1/verify?slug=clear-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"}}