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Data Loading Pipelines for Training

Trace batches from storage to GPU readiness, budget workers and prefetch memory, and resume from the input position saved with training state. An executable example shows how a read-ahead cursor can silently skip samples.

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RELATED CONCEPTS

Parallel Filesystems vs Object Storage GPUDirect RDMA and GPUDirect Storage MFU and HFU NVLink, NVSwitch and PCIe Evaluation and Data Pipeline Infrastructure Checkpoint I/O

LESSONS THAT TEACH THIS

Training at ScaleA training step follows a critical path through compute, exchange and update18 min→Fleet and ReliabilitySize storage for bytes, operations, concurrency and durability14 min→

PRACTICE THIS IN REAL QUESTIONS

Networking, Interconnects & StorageThe GPUs are idle between steps and the profiler says the data loader. Find the actual constraint and fix it.→Networking, Interconnects & StorageYou have 60 terabytes of filtered text and need 15 trillion training tokens. Design the tokenization and sharding stage.→Networking, Interconnects & StorageWhy does object storage behave badly for random reads, and what does a training pipeline do about it?→AI Infrastructure System DesignDesign the pipeline that produces 15 trillion training tokens: ingest, dedup, tokenize, shard, serve. Throughput per stage.→Napkin Math, Cost & CapacityWhat storage throughput does a 16k-GPU training cluster need?→CUDA, Triton & Kernel EngineeringA training step runs at 20 percent model FLOPs utilization. Profile it and find where the missing time goes.→