Covers designing and operating machine learning systems to handle growth in data volume, model complexity, and traffic. Topics include distributed training strategies such as data parallelism, model parallelism, and pipeline parallelism; coordination and orchestration approaches like parameter servers, gradient aggregation, and framework tools such as PyTorch distributed, Horovod, and TensorFlow strategies; data pipeline and I O considerations including sharding, efficient formats, preprocessing bottlenecks, streaming and batch ingestion; serving and inference scaling including model sharding, batching for throughput, autoscaling, request routing, caching, and latency versus throughput tradeoffs. Also includes monitoring, profiling, checkpointing and recovery, reproducibility, cost and resource optimization, and common bottleneck analysis across network, storage, CPU preprocessing, and accelerator utilization.
HardTechnical
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Discuss consistency and staleness trade-offs between parameter-server architectures with asynchronous updates and synchronous ring-allreduce training. Address convergence properties under staleness, communication efficiency, fault tolerance, and practical heuristics such as bounded staleness or elastic averaging.
HardTechnical
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Explain challenges and mitigation strategies for achieving deterministic distributed training: nondeterministic kernels, reduction order variability across devices, atomic ops, mixed-precision nondeterminism, and OS-level thread scheduling. Propose practical steps to maximize determinism and discuss residual sources you may not be able to eliminate.
MediumTechnical
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Compare the practical trade-offs between using spot/preemptible instances versus on-demand instances for large-scale training. Discuss checkpointing frequency, job scheduling strategies, cost savings versus risk, use of mixed instance pools, and recommendations for job types with varying tolerance to preemption.
MediumSystem Design
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Propose a model sharding strategy to train a transformer with 300B parameters across hundreds of GPUs. Discuss tensor parallelism, pipeline parallelism, ZeRO optimizer partitioning stages, activation checkpointing, micro-batching, and the communication patterns needed to keep devices utilized.
HardTechnical
0 practiced
Provide Python-like pseudocode demonstrating the communication pattern for a simple model-parallel linear layer split across two GPUs. Show the forward pass where activations are sent/received and the backward pass where gradients are exchanged. Include synchronization points and buffer management to avoid deadlocks.
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