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AI Ecosystem · In Development

Accelerator Clusters And The Cloud/Software Bridge

See how many AI computers work together and how cloud services make them available.

About This Course

From installed equipment to a developer's API call

Thousands of accelerators can be installed in powered racks and still be a long way from a useful service. Software has to run the model, networks have to connect related work, storage has to supply information, and schedulers have to allocate resources. Identity, metering, monitoring, and recovery are part of the developer's experience too.

The project, 10,000 Accelerators to One API Call, follows those steps. You will trace a training job separately from an inference request and learn to recognize what someone might mean by 'more compute.' Modules 2–5 supply the groundwork on workloads, facilities, and the supply chain. We will explain the software terms before using them.

The scenarios are conceptual and the numbers hypothetical. Use them to decide what to measure and where a constraint might sit. A production implementation needs documentation for its exact framework, drivers, hardware, network, service version, and configuration.

Planned Lessons

  1. The Software Stack Translates Intentions Into ExecutionFollow a developer’s program through the software that tells AI hardware what to do.
  2. CUDA, ROCm, XLA, And Neuron Are Different Kinds Of EcosystemCompare the tools that help developers run AI on different types of chips.
  3. Graph Compilation And Kernel FusionSee how combining calculations can reduce the work of moving information around.
  4. Scale-Up And Scale-Out CommunicationCompare exchanging information inside one AI machine with sending it between machines.
  5. NICs, RDMA, InfiniBand, And EthernetExplore the network equipment and connection methods used in large AI systems.
  6. Data Parallelism Replicates The ModelSee how several copies of a model can learn from different examples at the same time.
  7. Tensor, Pipeline, And Expert Parallelism Divide Different ThingsCompare ways to divide a large model’s work across several chips.
  8. Scaling Efficiency Measures The Benefit Of More ResourcesCalculate whether adding computers gives an AI task a worthwhile increase in speed.
  9. Containers Package Software For Defined EnvironmentsUnderstand how a container bundles software so it can run in a suitable environment.
  10. Kubernetes, Orchestration, And SchedulingSee how software assigns AI jobs to available computers and keeps those jobs running.
  11. Storage Feeds Training And Preserves StateFind out how training systems read examples and save work so it can be recovered.
  12. Fault Tolerance Includes Detection And Correct RecoveryFollow what happens when an AI job fails and the system needs to restart valid work.
  13. Inference Serving Turns Weights Into ResponsesSee the software needed to turn a stored AI model into a service people can use.
  14. Tail Latency, Service Targets, And GoodputMeasure how long people wait and how much useful work a service completes.
  15. Observability Connects Symptoms To StagesUse service records to work out which stage is causing an AI application to slow down.
  16. Hyperscalers, Neoclouds, And Responsibility BoundariesCompare computing providers by what they manage and what remains the customer’s job.