Background & research
Fundamentals of distributed training
Compute, memory, and communication across training workers.
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Distributed training notes
Educational notes on parallel training and the trust boundaries of AI infrastructure.
Training efficiency and infrastructure trustworthiness meet at worker, communication, storage, and runtime boundaries. These notes provide background; they do not describe a released AGIACC training product.
Read fundamentals, data parallelism, model parallelism, pipeline parallelism, and hybrid parallelism, then see framework references and security questions.
Background & research
Data parallelism
Replicated computation and sharded state across training workers.
Read noteBackground & research
Model parallelism
Splitting computation within model operations and layers.
Read noteBackground & research
Pipeline parallelism
Stage boundaries, microbatches, and training schedules.
Read noteBackground & research
Hybrid parallelism
Combining data, tensor, and pipeline dimensions with explicit assumptions.
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Frameworks and tools
Primary implementation references for distributed AI workloads.
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Security challenges in distributed training
Worker admission, artifact integrity, credentials, and recovery.
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