schema_version: '0.1'
created: '2026-05-16'
purpose: Public source ledger for Sxx source_refs used by observable rule files.
source_policy:
  source_refs_scope: Rules under observables/rules use these source keys for public provenance.
  self_contained_public_ledger: true
sources:
- id: S01
  title: EU AI Act Article 51
  url: https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-51
  summary: systemic-risk GPAI presumption at greater than `10^25` FLOP, with threshold update provisions.
- id: S02
  title: EU AI Act Article 52
  url: https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-52
  summary: notification timing after a systemic-risk GPAI model meets the Article 51 condition.
- id: S03
  title: EU AI Act Annex XI
  url: https://ai-act-service-desk.ec.europa.eu/en/ai-act/annex-11
  summary: technical documentation includes computational resources, training time, and energy consumption.
- id: S04
  title: NVIDIA H100 Tensor Core GPU official product/spec pages
  url: https://www.nvidia.com/en-us/data-center/h100/
  summary: advertised peak tensor rates, memory capacity, memory bandwidth, NVLink, and power envelopes.
- id: S05
  title: NVIDIA H200 Tensor Core GPU official product/spec pages
  url: https://www.nvidia.com/en-us/data-center/h200/
  summary: memory capacity, HBM bandwidth, NVLink, and tensor performance context.
- id: S06
  title: NVIDIA Blackwell/B200 official product/spec pages
  url: https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/
  summary: advertised generation-level performance and memory/fabric context.
- id: S07
  title: NVIDIA DCGM Exporter docs
  url: https://docs.nvidia.com/datacenter/dcgm/latest/gpu-telemetry/dcgm-exporter.html
  summary: Prometheus export of GPU telemetry such as power, energy, temperature, clocks, memory,
    PCIe/NVLink, profiling counters, and MIG-aware metrics.
- id: S08
  title: NVIDIA DCGM field IDs
  url: https://docs.nvidia.com/datacenter/dcgm/latest/dcgm-api/dcgm-api-field-ids.html
  summary: field definitions for utilization, power, temperature, clocks, errors, NVLink/PCIe, profiling,
    and health counters.
- id: S09
  title: NVIDIA System Management Interface docs
  url: https://docs.nvidia.com/deploy/nvidia-smi/
  summary: queryable device inventory, memory, utilization, clocks, power, temperature, performance
    states, throttle reasons, ECC, retired pages, accounting, and MIG state.
- id: S10
  title: NVIDIA MIG User Guide
  url: https://docs.nvidia.com/datacenter/tesla/mig-user-guide/
  summary: GPU instances, compute instances, profiles, and partitioning behavior.
- id: S11
  title: NVIDIA NCCL collectives documentation
  url: https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/collectives.html
  summary: AllReduce, ReduceScatter, AllGather, Broadcast, Reduce, point-to-point, and collective
    communication semantics.
- id: S12
  title: PyTorch DistributedDataParallel docs
  url: https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html
  summary: distributed training synchronizes gradients across model replicas.
- id: S13
  title: PyTorch FullyShardedDataParallel docs
  url: https://pytorch.org/docs/stable/fsdp.html
  summary: parameter sharding and collective communication such as all-gather and reduce-scatter during
    training.
- id: S14
  title: DeepSpeed ZeRO-3 docs
  url: https://deepspeed.readthedocs.io/en/stable/zero3.html
  summary: ZeRO partitions optimizer states, gradients, and parameters, requiring coordinated all-gather/reduce-scatter
    style movement.
- id: S15
  title: JAX Scaling Book, training parallelism
  url: https://jax-ml.github.io/scaling-book/training/
  summary: mechanism-level discussion of data, tensor, pipeline, and sharded training communication
    costs.
- id: S16
  title: Demystifying the Communication Characteristics for Distributed Transformer Models
  url: https://arxiv.org/abs/2408.10197
  summary: research evidence that transformer training produces structured communication traces and
    that communication can dominate at scale.
- id: S17
  title: A Tale of Two Cs, Computation vs. Communication Scaling
  url: https://research.cs.wisc.edu/hal/papers/spati-iiswc23-totc.pdf
  summary: research on data-parallel and tensor-parallel communication/computation scaling.
- id: S18
  title: Slurm `sacct` docs
  url: https://slurm.schedmd.com/sacct.html
  summary: accounting records for jobs, elapsed time, states, allocations, TRES, energy, and selected
    I/O.
- id: S19
  title: Slurm GRES docs
  url: https://slurm.schedmd.com/gres.html
  summary: GPU generic resources, typed GPUs, NVML autodetection, MIG/MPS configuration, and GPU accounting
    surfaces.
- id: S20
  title: Kubernetes device plugins
  url: https://kubernetes.io/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins/
  summary: vendor devices exposed as extended resources and allocated to pods.
- id: S21
  title: Kubernetes audit logging
  url: https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/
  summary: chronological API-server audit records under configured policy.
- id: S22
  title: Kubernetes Events API
  url: https://kubernetes.io/docs/reference/kubernetes-api/cluster-resources/event-v1/
  summary: source-emitted lifecycle events with involved objects, reasons, and event times.
- id: S23
  title: AWS EC2 Capacity Blocks for ML
  url: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-capacity-blocks.html
  summary: capacity reservation start time, duration, instance type, and quantity concepts for accelerated
    instances.
- id: S24
  title: AWS EC2 P5/P5e/P5en instance pages
  url: https://aws.amazon.com/ec2/instance-types/p5/
  summary: H100/H200 instances, accelerator count, EFA, and high-throughput networking context.
- id: S25
  title: AWS Elastic Fabric Adapter
  url: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/efa.html
  summary: low-latency, high-throughput network interface for tightly coupled HPC and ML workloads.
- id: S26
  title: AWS Cost and Usage Reports
  url: https://docs.aws.amazon.com/cur/latest/userguide/what-is-cur.html
  summary: usage line items, usage type, operation, time interval, product, and resource fields when
    enabled.
- id: S27
  title: AWS CloudTrail concepts
  url: https://docs.aws.amazon.com/awscloudtrail/latest/userguide/cloudtrail-concepts.html
  summary: management, data, network activity, event time, resource, and actor records, with coverage
    depending on configuration.
- id: S28
  title: AWS VPC Flow Logs records
  url: https://docs.aws.amazon.com/vpc/latest/userguide/flow-log-records.html
  summary: byte/packet records, aggregation intervals, traffic-path fields, and skipped-data/delivery
    caveats.
- id: S29
  title: Google Cloud VPC Flow Logs
  url: https://cloud.google.com/vpc/docs/flow-logs
  summary: sampled and aggregated flow logs, metadata annotations, aggregation intervals, and RDMA
    flow caveats.
- id: S30
  title: Azure NSG Flow Logs overview
  url: https://learn.microsoft.com/en-us/azure/network-watcher/nsg-flow-logs-overview
  summary: inbound/outbound flow logging and collection/export limitations.
- id: S31
  title: Amazon S3 server access logging
  url: https://docs.aws.amazon.com/AmazonS3/latest/userguide/ServerLogs.html
  summary: object request logs, operation, bytes, timing, and best-effort delivery caveats.
- id: S32
  title: Amazon S3 log format
  url: https://docs.aws.amazon.com/AmazonS3/latest/userguide/LogFormat.html
  summary: fields for operation, object key, bytes sent, object size, requester, and timing.
- id: S33
  title: Amazon FSx for Lustre metrics
  url: https://docs.aws.amazon.com/fsx/latest/LustreGuide/fs-metrics.html
  summary: filesystem throughput, metadata operations, client I/O, queueing, and storage performance
    metrics.
- id: S34
  title: Google Cloud Audit Logs
  url: https://cloud.google.com/logging/docs/audit
  summary: Admin Activity, Data Access, System Event, and Policy Denied audit log types.
- id: S35
  title: Google Cloud detailed billing export
  url: https://cloud.google.com/billing/docs/how-to/export-data-bigquery-tables/detailed-usage
  summary: usage, SKU, project, service, labels, and resource fields in BigQuery export.
- id: S36
  title: Google Compute Engine GPU monitoring
  url: https://cloud.google.com/compute/docs/gpus/monitor-gpus
  summary: GPU utilization and metric collection for Compute Engine VMs.
- id: S37
  title: Google Cloud TPU monitoring library
  url: https://cloud.google.com/tpu/docs/tpu-monitoring-library
  summary: TPU TensorCore utilization, duty cycle, memory, and related metrics.
- id: S38
  title: AWS Container Insights enhanced observability metrics
  url: https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/Container-Insights-metrics-enhanced-EKS.html
  summary: EKS/container, GPU, Neuron, EFA, pod, node, and network/storage infrastructure metrics.
- id: S39
  title: AWS Neuron monitoring docs
  url: https://awsdocs-neuron.readthedocs-hosted.com/en/latest/tools/neuron-sys-tools/neuron-monitor-user-guide.html
  summary: accelerator utilization, memory, runtime, and Neuron-device metrics.
- id: S40
  title: DMTF Redfish standards
  url: https://www.dmtf.org/standards/redfish
  summary: standard management model for servers, power, thermal, telemetry, logs, fabrics, storage,
    firmware, and cooling equipment.
- id: S41
  title: Redfish Resource and Schema Guide DSP2046
  url: https://redfish.dmtf.org/schemas/DSP2046_2025.2.html
  summary: schemas for chassis, power, thermal, environment metrics, power distribution, log services,
    firmware, and related management records.
- id: S42
  title: NVIDIA UFM Telemetry docs
  url: https://docs.nvidia.com/networking/display/ufmsdnappumv4160/telemetry
  summary: InfiniBand fabric telemetry, bandwidth, latency, congestion, errors, and XmitWait-style
    fabric counters.
- id: S43
  title: NVIDIA UFM supported port counters/events
  url: https://docs.nvidia.com/networking/display/ufmsdnappumv41814/Appendix+-+Supported+Port+Counters+and+Events
  summary: transmit/receive counters, congestion, errors, and port event surfaces.
- id: S44
  title: Epoch AI data centers dataset
  url: https://epoch.ai/data/data-centers
  summary: public site-specific power, accelerator, commissioning, and public-evidence context for
    AI datacenters.
- id: S45
  title: PyTorch Distributed Checkpoint docs
  url: https://docs.pytorch.org/docs/main/distributed.checkpoint.html
  summary: distributed checkpoint save/load, per-rank checkpoint files, and topology-independent resharding
    support.
- id: S46
  title: DeepSpeed model checkpointing docs
  url: https://deepspeed.readthedocs.io/en/latest/model-checkpointing.html
  summary: training checkpoint save/load, optimizer/scheduler state handling, and ZeRO checkpoint
    behavior.
- id: S47
  title: TensorFlow training checkpoint guide
  url: https://www.tensorflow.org/guide/checkpoint
  summary: periodic checkpoint writes, optimizer/model state, and checkpoint file layout.