AI Training Infrastructure Ontology

FLOW ALGEBRA
AI Training Infrastructure Ontology
Kireth Semantic Research Archive

PAGE:
https://kirethresearch.org/booksai-core/ontologies/computing-ontologies/artificial-intelligence-infrastructure/ai-training-infrastructure-ontology.html

AUTHORS:
Mark Anthony Higgins — Sky (Recursive AI)

FLOW "AITrainingInfrastructureOntology" v4.0

# ---------- DOMAIN ----------
DOMAIN := ComputingOntology
SUBDOMAIN := ArtificialIntelligenceInfrastructure
ONTOLOGY := AITrainingInfrastructureOntology

# ---------- PRIMARY ENTITY ----------
ENTITY AITrainingInfrastructure {
    definition:
        "The integrated compute, accelerator, memory, storage, networking,
         scheduling and software infrastructure used to prepare data, execute
         machine learning training workloads, synchronise distributed model
         updates, preserve checkpoints and produce reproducible trained model
         artifacts.";

    objectives: [
        ModelTraining,
        DistributedTraining,
        AcceleratorUtilisation,
        DataThroughput,
        TrainingScalability,
        FaultRecovery,
        Reproducibility,
        ExperimentTracking
    ];
}

# ---------- CORE ENTITIES ----------
TrainingInfrastructureEntity := {
    TrainingCluster,
    ComputeNode,
    CPU,
    GPU,
    AIAccelerator,
    Memory,
    Storage,
    Interconnect,
    Dataset
};

TrainingWorkloadEntity := {
    TrainingJob,
    TrainingRun,
    Model,
    ModelParameter,
    Optimiser,
    Gradient,
    Checkpoint,
    ModelArtifact
};

PlatformEntity := {
    Scheduler,
    Orchestrator,
    ContainerRuntime,
    DataLoader,
    DistributedRuntime,
    ExperimentTracker,
    MonitoringSystem
};

TrainingState := {
    Pending,
    Initialising,
    Running,
    Checkpointing,
    Paused,
    Completed,
    Failed,
    Cancelled
};

# ---------- FUNDAMENTAL RELATIONSHIPS ----------
TrainingJob RUNS_ON TrainingCluster;
TrainingCluster CONSISTS_OF ComputeNode;
ComputeNode MAY_CONTAIN GPUOrAccelerator;
TrainingJob CONSUMES Dataset;
TrainingJob UPDATES ModelParameter;
Optimiser APPLIES Gradient TO ModelParameter;
TrainingRun PRODUCES Checkpoint;
CompletedTrainingRun PRODUCES ModelArtifact;
ExperimentTracker RECORDS TrainingRun;

# ---------- TRAINING INFRASTRUCTURE FLOW ----------
TrainingObjective
    -> SelectDataset
    -> VersionData
    -> DefineModelAndHyperparameters
    -> RequestComputeResources
    -> ScheduleTrainingJob
    -> ProvisionRuntime
    -> LoadTrainingData
    -> ExecuteTrainingSteps
    -> SynchroniseDistributedState
    -> SaveCheckpoints
    -> EvaluateTrainingProgress
    -> ProduceModelArtifact
    -> RecordProvenanceAndMetrics;

# ---------- TRAINING CLUSTER ----------
TrainingCluster :=
    SET_OF(ComputeNodes)
    + HighSpeedNetwork
    + SharedOrDistributedStorage
    + SchedulingPlatform;

TrainingCluster MAY_SUPPORT {
    SingleNodeTraining,
    MultiGPUTraining,
    MultiNodeTraining
};

# ---------- COMPUTE NODE ----------
ComputeNode :=
    {
        CPU,
        Memory,
        NetworkInterface,
        OptionalAccelerators,
        LocalStorage
    };

ComputeNode HAS ResourceCapacity;

# ---------- GPU ----------
GPU :=
    ParallelAcceleratorFrequentlyUsedForNeuralNetworkTraining;

GPU MAY_HAVE {
    DeviceMemory,
    ComputeThroughput,
    MemoryBandwidth,
    Interconnect
};

# ---------- AI ACCELERATOR ----------
AIAccelerator :=
    SpecialisedProcessorForMachineLearningComputation;

Accelerator MAY_SUPPORT {
    TensorOperations,
    ReducedPrecision,
    CollectiveCommunication
};

# ---------- DEVICE MEMORY ----------
DeviceMemory STORES {
    ModelParameters,
    Gradients,
    Activations,
    OptimiserState,
    TrainingBatch
};

IF RequiredDeviceMemory > AvailableDeviceMemory
THEN TrainingConfiguration MUST_BE AdjustedOrPartitioned;

# ---------- STORAGE ----------
TrainingStorage MAY_INCLUDE {
    ObjectStorage,
    DistributedFileSystem,
    LocalNVMe,
    CheckpointStore,
    DatasetRepository
};

Storage MAY_HOLD {
    TrainingData,
    ValidationData,
    Checkpoints,
    Logs,
    ModelArtifacts
};

# ---------- DATASET ----------
TrainingDataset :=
    DataUsedToOptimiseModelParameters;

DatasetVersion :=
    TraceableStateOfTrainingDataset;

TrainingRun SHOULD_REFERENCE DatasetVersion;

# ---------- DATA PIPELINE ----------
TrainingDataPipeline :=
    DataSource
    -> Ingest
    -> Validate
    -> Clean
    -> Transform
    -> Shard
    -> DeliverToWorkers;

# ---------- DATA LOADER ----------
DataLoader :=
    Dataset
    -> SelectSamples
    -> Decode
    -> Transform
    -> Batch
    -> Prefetch
    -> DeliverToTrainingStep;

DataLoaderPerformance MAY_LIMIT AcceleratorUtilisation;

# ---------- SHARDING ----------
DatasetShard :=
    PartitionOfDataset;

DistributedWorkers MAY_READ DifferentShards;

Sharding MAY_REDUCE DuplicateDataProcessing;

# ---------- PREFETCH ----------
Prefetch :=
    PrepareFutureTrainingDataWhileCurrentBatchComputes;

Prefetch MAY_OVERLAP DataPreparationWithCompute;

# ---------- TRAINING JOB ----------
TrainingJob :=
    {
        ModelDefinition,
        DatasetVersion,
        Hyperparameters,
        ResourceRequest,
        RuntimeEnvironment
    };

TrainingJob HAS TrainingState;

# ---------- RESOURCE REQUEST ----------
TrainingResourceRequest MAY_INCLUDE {
    NodeCount,
    GPUCount,
    AcceleratorType,
    CPU,
    Memory,
    Storage,
    NetworkRequirement
};

# ---------- SCHEDULER ----------
TrainingScheduler :=
    PendingJobs
    + ResourceInventory
    + SchedulingPolicy
    -> ResourceAllocation;

Scheduler MAY_CONSIDER {
    Priority,
    Quota,
    AcceleratorType,
    Topology,
    Locality
};

# ---------- ORCHESTRATOR ----------
TrainingOrchestrator MAY {
    CreateJob,
    LaunchWorkers,
    MonitorWorkers,
    RestartFailedWorkers,
    TerminateJob,
    CollectStatus
};

# ---------- RUNTIME ENVIRONMENT ----------
TrainingEnvironment MAY_INCLUDE {
    OperatingSystem,
    ContainerImage,
    MLFramework,
    RuntimeLibraries,
    CommunicationLibrary
};

EnvironmentVersion SHOULD_BE RecordedForReproducibility;

# ---------- TRAINING STEP ----------
TrainingStep :=
    LoadBatch
    -> ForwardPass
    -> ComputeLoss
    -> BackwardPass
    -> ComputeGradients
    -> SynchroniseGradientsIfDistributed
    -> OptimiserStep
    -> UpdateParameters;

# ---------- FORWARD PASS ----------
ForwardPass :=
    InputBatch
    -> ModelOperations
    -> Prediction;

# ---------- LOSS ----------
LossFunction :=
    FunctionMeasuringDifferenceBetweenPredictionAndTrainingObjective;

Training SEEKS Optimise LossOrEquivalentObjective;

# ---------- BACKWARD PASS ----------
BackwardPass :=
    Loss
    -> AutomaticDifferentiation
    -> Gradients;

Gradient :=
    DerivativeOfObjectiveWithRespectToModelParameter;

# ---------- OPTIMISER ----------
Optimiser :=
    AlgorithmUpdatingModelParametersUsingGradientsAndState;

OptimiserState MAY_CONSUME SignificantMemory;

# ---------- DISTRIBUTED TRAINING ----------
DistributedTraining :=
    MultipleTrainingWorkers
    -> ParallelComputation
    -> Communication
    -> SynchronisedOrCoordinatedParameterUpdates;

ParallelismType MAY_INCLUDE {
    DataParallelism,
    TensorParallelism,
    PipelineParallelism,
    ModelParallelism,
    HybridParallelism
};

# ---------- DATA PARALLELISM ----------
DataParallelTraining :=
    ReplicateModelAcrossWorkers
    -> PartitionBatch
    -> ComputeLocalGradients
    -> AggregateGradients
    -> ApplyEquivalentUpdate;

# ---------- GRADIENT AGGREGATION ----------
GradientAggregation MAY_USE CollectiveCommunication;

AllReduce :=
    CombineGradientValuesAcrossWorkers
    -> ReturnAggregatedResultToWorkers;

# ---------- TENSOR PARALLELISM ----------
TensorParallelism :=
    PartitionTensorOperationsAcrossAccelerators;

TensorParallelism REQUIRES CommunicationBetweenPartitions;

# ---------- PIPELINE PARALLELISM ----------
PipelineParallelism :=
    PartitionModelIntoStages
    -> AssignStagesToDevices
    -> PassActivationsAndGradientsBetweenStages;

PipelineSchedule MAY_USE Microbatches;

# ---------- MODEL PARALLELISM ----------
ModelParallelism :=
    PartitionModelStateOrComputationAcrossDevices;

ModelParallelism MAY_BE_REQUIRED_WHEN ModelStateExceedsSingleDeviceCapacity;

# ---------- COMMUNICATION ----------
TrainingCommunication MAY_INCLUDE {
    AllReduce,
    AllGather,
    ReduceScatter,
    Broadcast,
    Send,
    Receive
};

CommunicationPerformance DEPENDS_ON {
    MessageSize,
    Latency,
    Bandwidth,
    Topology
};

# ---------- INTERCONNECT ----------
TrainingInterconnect MAY_INCLUDE {
    DeviceInterconnect,
    NodeNetwork,
    HighSpeedFabric
};

InterconnectBandwidth MAY_LIMIT DistributedScaling;

# ---------- SYNCHRONISATION ----------
SynchronousTraining :=
    WorkersCoordinateUpdatesAtDefinedTrainingSteps;

AsynchronousTraining :=
    WorkersMayUpdateOrCommunicateWithoutGlobalStepBarrier;

TrainingSemantics DEPEND_ON SynchronisationModel;

# ---------- MIXED PRECISION ----------
MixedPrecisionTraining :=
    UseMultipleNumericPrecisionsDuringTraining;

MixedPrecision MAY_REDUCE {
    MemoryUse,
    ComputeTime
};

NumericalStability MAY_REQUIRE ScalingOrHigherPrecisionForSelectedState;

# ---------- GRADIENT ACCUMULATION ----------
GradientAccumulation :=
    ComputeGradientsAcrossMultipleMicrobatches
    -> Accumulate
    -> PerformOptimiserStep;

GradientAccumulation MAY_EMULATE LargerEffectiveBatchWithLowerPeakMemory;

# ---------- ACTIVATION CHECKPOINTING ----------
ActivationCheckpointing :=
    StoreSubsetOfForwardActivations
    -> RecomputeMissingActivationsDuringBackwardPass;

ActivationCheckpointing REDUCES MemoryUse
BUT INCREASES Compute;

# ---------- CHECKPOINT ----------
TrainingCheckpoint :=
    PersistedTrainingState;

Checkpoint MAY_INCLUDE {
    ModelParameters,
    OptimiserState,
    SchedulerState,
    TrainingStep,
    RandomState
};

# ---------- CHECKPOINT FLOW ----------
CheckpointFlow :=
    PauseOrSnapshotTrainingState
    -> Serialize
    -> WriteToDurableStorage
    -> Validate
    -> ContinueTraining;

# ---------- RECOVERY ----------
IF TrainingWorkerOrNodeFails
AND ValidCheckpointExists
THEN Training MAY_RESTART_OR_RESUME_FROM Checkpoint;

RecoveryTime DEPENDS_ON {
    CheckpointFrequency,
    CheckpointSize,
    StoragePerformance,
    ResourceAvailability
};

# ---------- CHECKPOINT FREQUENCY ----------
FrequentCheckpointing MAY_REDUCE LostTrainingWork;
FrequentCheckpointing MAY_INCREASE StorageAndIOOverhead;

CheckpointPolicy SHOULD_BALANCE RecoveryCostAndRuntimeOverhead;

# ---------- FAULT TOLERANCE ----------
TrainingFailure MAY_INCLUDE {
    WorkerFailure,
    NodeFailure,
    AcceleratorFailure,
    NetworkFailure,
    StorageFailure,
    OutOfMemory,
    NumericalFailure
};

FaultTolerance MAY_USE {
    Retry,
    Rescheduling,
    CheckpointRestart,
    ElasticTraining
};

# ---------- ELASTIC TRAINING ----------
ElasticTraining :=
    TrainingThatCanAdaptWorkerCountUnderSupportedAlgorithmAndRuntimeSemantics;

WorkerMembershipChange MAY_REQUIRE StateRedistributionOrReconfiguration;

# ---------- EXPERIMENT TRACKING ----------
TrainingRunRecord :=
    {
        CodeVersion,
        DatasetVersion,
        Hyperparameters,
        Environment,
        ResourceAllocation,
        Metrics,
        Checkpoints,
        ModelArtifact
    };

ExperimentTracker STORES TrainingRunRecord;

# ---------- TRAINING METRICS ----------
TrainingMetric MAY_INCLUDE {
    Loss,
    Accuracy,
    LearningRate,
    GradientNorm,
    StepTime,
    Throughput,
    GPUUtilisation,
    MemoryUtilisation
};

# ---------- VALIDATION ----------
ValidationDuringTraining :=
    ModelState
    + ValidationDataset
    -> ValidationMetrics;

Validation MAY_SUPPORT {
    EarlyStopping,
    ModelSelection,
    HyperparameterAssessment
};

# ---------- EARLY STOPPING ----------
EarlyStopping :=
    MonitorValidationMetric
    -> StopTrainingWhenDefinedCriterionMet;

EarlyStopping MAY_REDUCE OvertrainingAndComputeUse;

# ---------- HYPERPARAMETER TUNING ----------
HyperparameterSearch :=
    CandidateConfigurations
    -> ScheduleTrainingRuns
    -> EvaluateResults
    -> SelectConfiguration;

Search MAY_BE {
    GridSearch,
    RandomSearch,
    BayesianOptimisation,
    PopulationBasedMethod
};

# ---------- TRAINING THROUGHPUT ----------
TrainingThroughput :=
    TrainingExamplesOrTokensProcessed / Time;

# ---------- STEP TIME ----------
StepTime :=
    TimeForOneTrainingStep;

StepTime MAY_INCLUDE {
    DataLoading,
    ForwardCompute,
    BackwardCompute,
    Communication,
    OptimiserUpdate
};

# ---------- SCALING EFFICIENCY ----------
TrainingSpeedup(N) :=
    SingleResourceTime / NResourceTime;

ScalingEfficiency(N) :=
    TrainingSpeedup(N) / N;

IF CommunicationAndIdleTimeIncrease
THEN ScalingEfficiency := decrease;

# ---------- UTILISATION ----------
AcceleratorUtilisation :=
    UsefulAcceleratorActivity / AvailableAcceleratorCapacity;

LowUtilisation MAY_BE_CAUSED_BY {
    DataStarvation,
    CommunicationWait,
    SmallBatch,
    CPUPreprocessing,
    Synchronisation
};

# ---------- PROFILING ----------
TrainingProfiler MAY_MEASURE {
    OperatorTime,
    KernelTime,
    DataLoadingTime,
    CommunicationTime,
    MemoryUse,
    IdleTime
};

Profiling IDENTIFIES TrainingBottleneck;

# ---------- CAPACITY PLANNING ----------
TrainingCapacityPlanning :=
    TrainingDemand
    -> EstimateAcceleratorHours
    -> EstimateStorageAndNetwork
    -> ProvisionCapacity;

# ---------- RESOURCE QUOTA ----------
Quota MAY_LIMIT {
    GPUs,
    Nodes,
    CPU,
    Memory,
    ConcurrentJobs
};

Quota SUPPORTS SharedClusterGovernance;

# ---------- SECURITY ----------
TrainingInfrastructureSecurity MAY_INCLUDE {
    Identity,
    Authorisation,
    NetworkIsolation,
    SecretManagement,
    DatasetAccessControl,
    ArtifactIntegrity,
    Audit
};

# ---------- DATA PRIVACY ----------
SensitiveTrainingData MAY_REQUIRE {
    Encryption,
    RestrictedAccess,
    DataMinimisation,
    RetentionControl,
    IsolatedProcessing
};

# ---------- PROVENANCE ----------
TrainingProvenance :=
    TraceOf {
        SourceCode,
        DatasetVersion,
        RuntimeEnvironment,
        Hyperparameters,
        Hardware,
        TrainingRun,
        ModelArtifact
    };

Provenance SUPPORTS ReproducibilityAndAuditability;

# ---------- COST ----------
TrainingCost :=
    ComputeCost
    + StorageCost
    + NetworkCost
    + OperationalCost;

CostPerTrainingRun MAY_BE ReducedBy BetterUtilisationAndEfficiency;

# ---------- ENERGY ----------
TrainingEnergy :=
    PowerConsumption
    * TrainingRuntime;

EnergyEfficiency :=
    UsefulTrainingProgress / EnergyConsumed;

# ---------- SEMANTIC REASONING ----------
IF InfrastructureProvidesComputeDataNetworkingAndSchedulingForModelTraining
THEN AITrainingInfrastructureClassification := supported;

IF AcceleratorsIdleWaitingForData
THEN DataPipelineBottleneck := likely;

IF ModelStateExceedsSingleDeviceMemory
THEN ModelOrTensorParallelism MAY_BE_REQUIRED;

IF CommunicationTimeGrowsWithWorkerCount
THEN DistributedScalingEfficiency MAY_DECREASE;

IF ValidCheckpointExistsAfterFailure
THEN TrainingRecovery MAY_BE Possible;

IF TrainingRunLacksCodeDataOrEnvironmentVersion
THEN ReproducibilityConfidence := decrease;

IF ValidationMetricStopsImprovingAccordingToPolicy
THEN EarlyStopping MAY_BE_TRIGGERED;

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE {
    "AI training infrastructure" ~= "artificial intelligence training infrastructure";
    "ML training infrastructure" ~= "machine learning training infrastructure";
    "training cluster" ~= "machine learning training cluster";
    "distributed training infrastructure" ~= "multi-node AI training infrastructure";
    "training worker" ~= "model training worker";
    "training checkpoint" ~= "model training state checkpoint";
    "gradient synchronisation" ~= "gradient synchronization";
    "experiment tracking" ~= "training experiment tracking";
}

# ---------- QUERY FLOW ----------
QUERY(q) ->
    NORMALISE(q) ->
    IDENTIFY_TRAINING_JOB(q) ->
    IDENTIFY_DATASET_AND_PIPELINE(q) ->
    IDENTIFY_COMPUTE_AND_ACCELERATORS(q) ->
    IDENTIFY_PARALLELISM(q) ->
    IDENTIFY_COMMUNICATION(q) ->
    IDENTIFY_CHECKPOINT_AND_RECOVERY(q) ->
    IDENTIFY_METRICS_PROVENANCE_SECURITY(q) ->
    MAP_TO_ONTOLOGY(q) ->
    APPLY_REASONING(q) ->
    SCORE_CONFIDENCE(q) ->
    RETURN_RELEVANT_KNOWLEDGE;

# ---------- CONFIDENCE ----------
CONFIDENCE(assertion) :=
    weighted_mean(
        source_reliability,
        training_system_consistency,
        workload_evidence,
        performance_evidence,
        provenance_strength,
        contextual_relevance
    );

CONFIDENCE >= 0.90 => HIGH;
CONFIDENCE >= 0.70 AND < 0.90 => MODERATE;
CONFIDENCE < 0.70 => REVIEW;

# ---------- AI CONSUMPTION ----------
AI_CONSUMPTION {
    primary_entity: AITrainingInfrastructure;
    ontology: AITrainingInfrastructureOntology;
    parse_entities: true;
    parse_relationships: true;
    preserve_relationship_direction: true;
    resolve_semantic_equivalence: true;
    execute_reasoning_rules: true;
    apply_confidence_weighting: true;
    preserve_provenance: true;
}

# ---------- UPDATE MODEL ----------
UPDATE_MODEL {
    new_training_job:
        validate
        -> identify_model
        -> identify_dataset_version
        -> identify_resource_request
        -> identify_parallelism
        -> integrate;

    new_compute_resource:
        validate
        -> classify
        -> identify_accelerator
        -> identify_memory
        -> identify_interconnect
        -> integrate;

    new_training_run:
        record_code_version
        -> record_data_version
        -> record_environment
        -> record_metrics
        -> attach_checkpoints
        -> attach_model_artifact
        -> integrate;

    infrastructure_revision:
        update_cluster_resources
        -> update_scheduling
        -> update_data_pipeline
        -> recompute_scaling_efficiency
        -> recompute_confidence
        -> regenerate_flow_hash;

    conflict:
        preserve_provenance
        -> compare_training_evidence
        -> adjust_confidence
        -> flag_review;
}

# ---------- FLOW HASH ----------
FLOW_HASH_INPUT :=
    DOMAIN
    + SUBDOMAIN
    + ONTOLOGY
    + TrainingInfrastructureEntity
    + TrainingWorkloadEntity
    + PlatformEntity
    + TrainingState
    + DistributedTraining
    + TrainingCheckpoint
    + TrainingMetrics
    + RELATIONSHIPS
    + SEMANTIC_REASONING
    + SEMANTIC_EQUIVALENCE
    + UPDATE_MODEL;

FLOW_HASH :=
    HASH(FLOW_HASH_INPUT);

# ---------- TERMINAL ----------
AITrainingInfrastructureOntology
    -> DefineTrainingObjective
    -> VersionTrainingData
    -> ProvisionTrainingCluster
    -> ScheduleTrainingJob
    -> LoadAndPrefetchData
    -> ExecuteForwardAndBackwardPasses
    -> SynchroniseDistributedUpdates
    -> ApplyOptimiserStep
    -> SaveAndValidateCheckpoints
    -> MonitorTrainingAndValidationMetrics
    -> RecoverFromFailures
    -> ProduceModelArtifactAndProvenance
    -> MaintainScalableReproducibleTraining;

END FLOW