Distributed Training
A technical paradigm that distributes deep learning model training tasks across multiple GPUs or nodes for parallel execution. It accelerates large-scale model training through strategies such as data parallelism and model parallelism, relying on high-speed interconnect networks for gradient synchronization.
Timeline (last 90 days)
"Hangs" in distributed training are mostly caused by mismatched collective communication calls, such as inconsistent order or number of collectives entered by each rank, or a rank failing to participate due to OOM or an abnormal exit.
To judge whether a training library supports horizontal scaling, the key is whether it has built-in wrappers for distributed communication backends (such as NCCL and Gloo) and whether it supports sharded data loaders and gradient accumulation.
Distributed training refers to splitting a training task across multiple GPUs and machines for parallel execution when a single GPU cannot hold the model or data, with common strategies including data parallelism, tensor parallelism, and pipeline parallelism.
The development of open-source models, model distillation and distributed training technologies is partially offsetting the trend toward AI power concentration.
When saving checkpoints during distributed training, the data loader state and random seeds must also be saved; otherwise, after recovery the data order and augmentation results will be inconsistent, making training irreproducible.
In long-cycle training, even if one-thousandth of samples are misaligned per epoch, after accumulating over tens of thousands of steps it may significantly change the model's convergence path, making experimental results under different resource schedules lose comparability.
All-Reduce is the most core collective communication operation in distributed training; its common implementations include Ring All-Reduce and Tree All-Reduce, and the communication volume grows linearly with the number of model parameters.
In large-scale distributed training, forward propagation, backward propagation, and AllReduce/AllGather communication operations are interleaved; if a communication operation is not complete, downstream compute operators must wait, creating bubbles.
Communication primitives in distributed training (such as AllReduce gradient aggregation) can introduce non-deterministic accumulated errors due to differences in parallelism configuration and network topology.
Hidden failures such as network topology asymmetry, collective communication path anomalies, degraded GPU interconnects, and data loading and storage bottlenecks all appear green on traditional monitoring dashboards and only manifest under real workload pressure.
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All Facts (11)
"Hangs" in distributed training are mostly caused by mismatched collective communication calls, such as inconsistent order or number of collectives entered by each rank, or a rank failing to participate due to OOM or an abnormal exit.
50%UnverifiedTo judge whether a training library supports horizontal scaling, the key is whether it has built-in wrappers for distributed communication backends (such as NCCL and Gloo) and whether it supports sharded data loaders and gradient accumulation.
50%UnverifiedDistributed training refers to splitting a training task across multiple GPUs and machines for parallel execution when a single GPU cannot hold the model or data, with common strategies including data parallelism, tensor parallelism, and pipeline parallelism.
50%UnverifiedWhen saving checkpoints during distributed training, the data loader state and random seeds must also be saved; otherwise, after recovery the data order and augmentation results will be inconsistent, making training irreproducible.
50%UnverifiedIn long-cycle training, even if one-thousandth of samples are misaligned per epoch, after accumulating over tens of thousands of steps it may significantly change the model's convergence path, making experimental results under different resource schedules lose comparability.
50%UnverifiedAll-Reduce is the most core collective communication operation in distributed training; its common implementations include Ring All-Reduce and Tree All-Reduce, and the communication volume grows linearly with the number of model parameters.
50%UnverifiedIn large-scale distributed training, forward propagation, backward propagation, and AllReduce/AllGather communication operations are interleaved; if a communication operation is not complete, downstream compute operators must wait, creating bubbles.
50%UnverifiedCommunication primitives in distributed training (such as AllReduce gradient aggregation) can introduce non-deterministic accumulated errors due to differences in parallelism configuration and network topology.
50%UnverifiedHidden failures such as network topology asymmetry, collective communication path anomalies, degraded GPU interconnects, and data loading and storage bottlenecks all appear green on traditional monitoring dashboards and only manifest under real workload pressure.
50%UnverifiedIn distributed training, a small number of nodes whose kernel parameters are inconsistent with the rest can drag down the throughput of the entire training job, creating bottleneck nodes.
50%UnverifiedThe development of open-source models, model distillation and distributed training technologies is partially offsetting the trend toward AI power concentration.
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