Distributed Training, Mixed Precision, and Accelerator Systems
Scaling beyond a single device
Parallelism at the systems level
Distributed training uses data parallelism, model parallelism, or hybrid strategies to reduce wall-clock time and fit larger models. TensorFlow supports these patterns through distribution strategies, collective communication, and careful placement of variables and computations.
Scaling strategies
| Strategy | Best for | Tradeoff |
|---|---|---|
| Data parallelism | Many examples, moderate model size | Communication overhead for gradients |
| Model parallelism | Models too large for one device | Complex partitioning and synchronization |
| Pipeline parallelism | Very deep models | Bubbles and scheduling complexity |
| Hybrid parallelism | Frontier-scale systems | Highest engineering complexity |
What is the main idea of data parallel training?
Each worker processes different mini-batches with the same model replica, then synchronizes gradients or weights.
Correct answer: Split the dataset across examples and replicate the model
Mixed precision training
Mixed precision uses lower-precision formats such as or for many computations while preserving critical accumulators and master weights where needed. This can increase throughput and reduce memory use, but numerical stability must be monitored carefully.
Numerical stability
When using mixed precision, watch for overflow, underflow, and loss scaling issues that can silently degrade training.
Distributed training sanity checks
-
1
Verify each worker sees a unique data shard.
-
2
Confirm that global batch size matches the intended optimization regime.
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3
Check that metric aggregation is consistent across replicas.
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4
Profile communication bottlenecks relative to compute time.
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5
Validate determinism assumptions only where the platform truly supports them.
Why is communication overhead a concern in distributed training?
If devices spend too much time exchanging gradients, scaling efficiency drops.
Correct answer: Because synchronization can reduce the speedup gained from parallelism.
Which low-precision format is often favored for better dynamic range on modern accelerators?
bfloat16 preserves exponent range better than float16, which can help with training stability.
Correct answer: bfloat16
Name one benefit of mixed precision training.
Lower-precision activations and intermediates can reduce memory footprint and increase throughput.
Correct answer: Reduced memory usage