Unified computing interface for ai/ml workloads
OwnerGaurav Vij, Saurabh Kumar Vijinventors
Life of this patent
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Abstract
A distributed computing system for artificial intelligence workloads, comprising: a core orchestration server configured to manage a network of heterogeneous computing devices; a node registration module configured to detect capabilities of computing devices and register validated devices as network nodes; a security layer implementing authentication and workload isolation protocols; a workload scaling module configured to match job requirements with available node capabilities and distribute tasks across the network nodes; a smart container orchestrator configured to deploy and monitor containerized AI/ML workloads across the network nodes; wherein said system enables distributed processing of AI/ML workloads across heterogeneous computing devices.
Claims12
A distributed computing system for artificial intelligence workloads, comprising:
- a core orchestration server configured to manage a distributed network of heterogeneous computing devices;
- a node registration module configured to:
- detect capabilities of computing devices requesting to join the network;
- perform automated benchmarking of said computing devices;
- and
- register validated devices as network nodes;
- a security layer implementing:
- TLS certification;
- node authentication protocols;
- and
- workload isolation protocols;
- a workload scaling module configured to:
- analyze job requirements;
- match said requirements with available node capabilities;
- and
- distribute tasks across the network nodes;
- a smart container orchestrator configured to:
- deploy containerized AI/ML workloads;
- monitor workload execution;
- and
- maintain workload operations across the network nodes;
- wherein said system enables distributed processing of AI/ML workloads across heterogeneous computing devices.
A method for managing distributed AI computing resources, comprising:
- receiving registration requests from heterogeneous computing devices;
- performing automated capability detection and benchmarking of said devices;
- registering qualified devices as network nodes;
- receiving AI workload submissions through an API or visual interface;
- analyzing workload requirements and available node capabilities;
- distributing workloads across appropriate network nodes;
- monitoring workload execution and system performance;
- and
- implementing automatic failure recovery procedures as needed.
A system for automated environment management in distributed AI computing, comprising:
- a configuration management module configured to:
- analyze runtime requirements for AI workloads;
- verify GPU driver compatibility across nodes;
- manage software dependencies;
- and
- implement automatic environment configuration;
- wherein said system enables consistent workload execution across heterogeneous computing devices.
Dependencies are inferred from the claim text.