{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_run","slug":"run","name":"Run","type":"product","url":"https://www.run.ai","page_url":"https://unfragile.ai/run","categories":["automation"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_run__cap_0","uri":"capability://productivity.dynamic.gpu.workload.scheduling","name":"dynamic-gpu-workload-scheduling","description":"Automatically schedules and prioritizes ML training jobs across available GPU resources based on configurable policies, deadlines, and resource constraints. Intelligently queues jobs and allocates GPU time to maximize utilization and minimize idle periods.","intents":["I want to automatically schedule my training jobs without manual resource allocation","I need to prioritize urgent experiments over background jobs","I want to reduce GPU idle time and improve cluster efficiency"],"best_for":["ML teams with 50+ GPUs","enterprise research labs","organizations running multiple concurrent workloads"],"limitations":["requires Kubernetes integration","learning curve for policy configuration","not suitable for single-user or small clusters"],"requires":["Kubernetes cluster","GPU infrastructure","DevOps expertise","job submission via supported frameworks"],"input_types":["job specifications","resource requirements","priority policies"],"output_types":["scheduled job queue","resource allocation decisions","execution timeline"],"categories":["productivity","research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_run__cap_1","uri":"capability://productivity.intelligent.gpu.sharing.and.virtualization","name":"intelligent-gpu-sharing-and-virtualization","description":"Enables multiple workloads to share individual GPUs through intelligent partitioning and time-slicing, allowing concurrent execution of smaller jobs on the same hardware. Prevents resource contention and maximizes throughput on expensive GPU resources.","intents":["I want multiple teams to use the same GPU without waiting for exclusive access","I need to run small inference jobs alongside training without buying more GPUs","I want to reduce infrastructure costs by sharing expensive GPU resources"],"best_for":["multi-team organizations","enterprises with shared GPU clusters","cost-conscious research labs"],"limitations":["performance overhead from context switching","not ideal for latency-sensitive applications","requires careful workload profiling"],"requires":["GPU hardware supporting virtualization","Kubernetes","workload characterization"],"input_types":["workload specifications","performance requirements","sharing policies"],"output_types":["shared GPU allocations","performance metrics","contention alerts"],"categories":["productivity","research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_run__cap_10","uri":"capability://productivity.multi.framework.workload.support","name":"multi-framework-workload-support","description":"Supports orchestration of workloads across multiple ML frameworks and tools including PyTorch, TensorFlow, Horovod, and others. Provides framework-agnostic scheduling and resource management.","intents":["I want to run PyTorch and TensorFlow jobs on the same cluster","I need to support distributed training across different frameworks","I want a single orchestration system for all our ML workloads"],"best_for":["organizations using multiple ML frameworks","enterprises with diverse ML teams","research labs with varied tooling"],"limitations":["requires framework-specific drivers","some frameworks have limited support","performance varies by framework"],"requires":["framework-specific runtime support","GPU drivers","framework knowledge"],"input_types":["framework specifications","job definitions","dependency requirements"],"output_types":["scheduled workloads","resource allocations","execution logs"],"categories":["productivity","research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_run__cap_11","uri":"capability://productivity.resource.quota.and.governance.enforcement","name":"resource-quota-and-governance-enforcement","description":"Enforces resource quotas and governance policies at team, project, and user levels to prevent resource abuse and ensure compliance. Tracks resource consumption against quotas and prevents over-allocation.","intents":["I want to limit how many GPUs each team can use","I need to enforce budget constraints on GPU usage","I want to prevent any single user from monopolizing resources"],"best_for":["enterprises with governance requirements","organizations with multiple teams","cost-conscious organizations"],"limitations":["requires policy definition","quota enforcement may delay jobs","complex to tune for fairness"],"requires":["quota management system","user/team identification","policy enforcement engine"],"input_types":["quota policies","user/team assignments","resource requests"],"output_types":["quota enforcement decisions","usage reports","policy violations"],"categories":["productivity","research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_run__cap_12","uri":"capability://productivity.workload.migration.and.portability","name":"workload-migration-and-portability","description":"Enables seamless migration of workloads between different infrastructure environments (on-premise to cloud, between clouds) without code changes. Abstracts infrastructure differences to provide portable workload definitions.","intents":["I want to move a training job from on-premise to AWS without rewriting it","I need to switch between cloud providers based on cost","I want to avoid vendor lock-in for my ML workloads"],"best_for":["organizations with hybrid infrastructure","enterprises using multiple clouds","teams avoiding vendor lock-in"],"limitations":["requires workload abstraction","some infrastructure-specific features may not be portable","migration has latency"],"requires":["portable workload definitions","infrastructure abstraction layer","multi-environment support"],"input_types":["workload definitions","infrastructure specifications","migration policies"],"output_types":["migrated workloads","infrastructure mappings","compatibility reports"],"categories":["productivity","research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_run__cap_2","uri":"capability://productivity.multi.cloud.and.on.premise.orchestration","name":"multi-cloud-and-on-premise-orchestration","description":"Provides unified workload orchestration across on-premise data centers and multiple cloud providers (AWS, GCP, Azure) through a single control plane. Eliminates vendor lock-in and enables seamless workload migration based on cost and availability.","intents":["I want to use GPUs across multiple cloud providers without managing separate systems","I need to move workloads between on-premise and cloud based on cost or availability","I want to avoid being locked into a single cloud vendor"],"best_for":["enterprises with hybrid infrastructure","organizations using multiple cloud providers","teams needing flexibility in resource sourcing"],"limitations":["requires Kubernetes on all platforms","network latency between clouds may impact performance","complex setup and maintenance"],"requires":["Kubernetes clusters across environments","network connectivity","cloud credentials and access","DevOps expertise"],"input_types":["cloud provider configurations","workload specifications","infrastructure topology"],"output_types":["unified resource pool","workload placement decisions","cost analysis across clouds"],"categories":["productivity","research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_run__cap_3","uri":"capability://productivity.real.time.gpu.utilization.monitoring","name":"real-time-gpu-utilization-monitoring","description":"Provides real-time dashboards and metrics showing GPU utilization rates, memory usage, temperature, and job performance across the entire cluster. Identifies bottlenecks, idle resources, and performance anomalies with granular visibility.","intents":["I want to see which GPUs are idle and why","I need to understand if my cluster is being used efficiently","I want to identify bottlenecks in my ML pipeline"],"best_for":["cluster administrators","ML operations teams","organizations optimizing infrastructure spend"],"limitations":["requires continuous monitoring overhead","historical data retention depends on storage","alerts require configuration"],"requires":["monitoring agents on GPU nodes","time-series database","dashboard infrastructure"],"input_types":["GPU metrics","job telemetry","system performance data"],"output_types":["dashboards","utilization reports","performance metrics","alerts"],"categories":["productivity","research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_run__cap_4","uri":"capability://productivity.granular.job.prioritization.and.fairness","name":"granular-job-prioritization-and-fairness","description":"Implements configurable prioritization policies and fair resource allocation mechanisms to ensure critical workloads get resources while preventing any single user or team from monopolizing the cluster. Supports priority queues, resource quotas, and fair-share scheduling.","intents":["I want to ensure production inference jobs get priority over experimental training","I need to prevent one team from using all GPU resources","I want to implement fair resource allocation across multiple teams"],"best_for":["multi-team organizations","enterprises with competing workload priorities","organizations needing governance"],"limitations":["requires careful policy definition","may delay lower-priority jobs significantly","complex to tune for fairness"],"requires":["policy configuration framework","resource quota management","organizational governance model"],"input_types":["priority policies","resource quotas","job metadata"],"output_types":["prioritized job queue","resource allocations","fairness metrics"],"categories":["productivity","research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_run__cap_5","uri":"capability://productivity.infrastructure.cost.optimization.analysis","name":"infrastructure-cost-optimization-analysis","description":"Analyzes GPU utilization patterns and provides recommendations for cost reduction through better scheduling, resource sharing, and infrastructure decisions. Calculates potential savings from improved utilization and identifies cost-inefficient workloads.","intents":["I want to understand how much money we're wasting on idle GPUs","I need to justify GPU infrastructure investments to leadership","I want to identify opportunities to reduce our compute costs"],"best_for":["finance-conscious enterprises","organizations with large GPU budgets","teams needing ROI justification"],"limitations":["accuracy depends on complete utilization data","doesn't account for non-compute costs","recommendations require implementation effort"],"requires":["historical utilization data","cost data from cloud providers or procurement","infrastructure inventory"],"input_types":["utilization metrics","cost data","infrastructure specifications"],"output_types":["cost analysis reports","optimization recommendations","ROI projections"],"categories":["productivity","research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_run__cap_6","uri":"capability://productivity.kubernetes.native.workload.integration","name":"kubernetes-native-workload-integration","description":"Integrates deeply with Kubernetes to manage GPU workloads as native Kubernetes resources, supporting standard Kubernetes APIs and tools. Enables teams already using Kubernetes to manage GPU orchestration without learning new systems.","intents":["I want to manage GPU workloads using standard Kubernetes tools","I need GPU orchestration to work with my existing Kubernetes infrastructure","I want to use kubectl and Kubernetes manifests for GPU job submission"],"best_for":["Kubernetes-native organizations","teams with existing Kubernetes expertise","enterprises standardized on Kubernetes"],"limitations":["requires Kubernetes knowledge","limited to Kubernetes-compatible workloads","adds complexity to cluster management"],"requires":["Kubernetes cluster","Kubernetes expertise","GPU node drivers","CRD support"],"input_types":["Kubernetes manifests","CRD definitions","resource requests"],"output_types":["scheduled pods","resource allocations","Kubernetes events"],"categories":["productivity","research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_run__cap_7","uri":"capability://productivity.workload.performance.profiling.and.insights","name":"workload-performance-profiling-and-insights","description":"Profiles ML workload performance characteristics including GPU utilization patterns, memory requirements, and execution time. Provides insights into workload behavior to inform scheduling decisions and resource allocation strategies.","intents":["I want to understand how much GPU memory my training job actually needs","I need to know if my job is GPU-bound or CPU-bound","I want to identify which jobs can share GPUs safely"],"best_for":["ML engineers optimizing workloads","cluster administrators","organizations tuning resource allocation"],"limitations":["profiling adds overhead","requires representative workload runs","insights are workload-specific"],"requires":["performance monitoring infrastructure","historical execution data"],"input_types":["job execution traces","GPU metrics","system performance data"],"output_types":["performance profiles","resource requirement estimates","optimization recommendations"],"categories":["productivity","research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_run__cap_8","uri":"capability://productivity.dynamic.resource.scaling.and.elasticity","name":"dynamic-resource-scaling-and-elasticity","description":"Automatically scales GPU resources up or down based on workload demand and configured policies, integrating with cloud providers for on-demand resource provisioning. Reduces costs during low-demand periods while ensuring capacity during peaks.","intents":["I want to automatically add GPUs when demand is high and remove them when it's low","I need to handle variable workload demand without manual intervention","I want to minimize cloud costs by scaling down unused resources"],"best_for":["cloud-based deployments","organizations with variable workload patterns","cost-conscious enterprises"],"limitations":["requires cloud provider integration","scaling decisions have latency","cold-start overhead for new nodes"],"requires":["cloud provider credentials","scaling policies","workload demand forecasting"],"input_types":["workload demand metrics","scaling policies","cost constraints"],"output_types":["scaling decisions","resource provisioning requests","cost projections"],"categories":["productivity","research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_run__cap_9","uri":"capability://productivity.job.preemption.and.checkpointing.support","name":"job-preemption-and-checkpointing-support","description":"Enables preemption of lower-priority jobs to make room for higher-priority workloads, with support for checkpointing to resume interrupted jobs without losing progress. Maximizes resource utilization while minimizing wasted computation.","intents":["I want to interrupt a background job to run an urgent experiment","I need to resume interrupted training jobs from checkpoints","I want to maximize GPU utilization by preempting low-priority work"],"best_for":["organizations with mixed priority workloads","teams using checkpoint-compatible frameworks","enterprises optimizing utilization"],"limitations":["requires checkpoint support in workloads","preemption overhead and latency","not all frameworks support checkpointing"],"requires":["checkpoint-compatible ML frameworks","persistent storage for checkpoints","job metadata tracking"],"input_types":["priority policies","checkpoint specifications","job definitions"],"output_types":["preemption decisions","checkpoint triggers","resume instructions"],"categories":["productivity","research"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["Kubernetes cluster","GPU infrastructure","DevOps expertise","job submission via supported frameworks","GPU hardware supporting virtualization","Kubernetes","workload characterization","framework-specific runtime support","GPU drivers","framework knowledge"],"failure_modes":["requires Kubernetes integration","learning curve for policy configuration","not suitable for single-user or small clusters","performance overhead from context switching","not ideal for latency-sensitive applications","requires careful workload profiling","requires framework-specific drivers","some frameworks have limited support","performance varies by framework","requires policy definition","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.43333333333333335,"quality":0.86,"ecosystem":0.25,"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":"2026-05-24T12:16:33.095Z","last_scraped_at":"2026-04-05T13:23:42.535Z","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=run","compare_url":"https://unfragile.ai/compare?artifact=run"}},"signature":"+LE7/Xu8W6k7rKVHLOEGlW+fe7qMXGZ6z/hSYIN+IXg+CfkSJsbrwvwhFgEbaHBGC1H8n1c06XXZz4a1nxVQCg==","signedAt":"2026-06-20T14:37:23.535Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/run","artifact":"https://unfragile.ai/run","verify":"https://unfragile.ai/api/v1/verify?slug=run","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"}}