Distributed Training

Distributed training uses multiple cooperating workers to optimize one machine learning model. Workers can run on several GPUs within one machine or across multiple cloud instances. The main objectives are to reduce elapsed training time and make models or training workloads feasible when a single device lacks sufficient memory. Unlike hyperparameter tuning, where independent jobs evaluate different configurations, distributed training coordinates workers within the same training run.

Data parallelism gives each worker a model replica and a different portion of the training data. In synchronous implementations, workers exchange and aggregate gradients before applying consistent updates, often using an all-reduce collective operation. For example, eight GPUs can process eight different mini-batches concurrently. If each GPU processes 32 examples per optimizer step, the global batch size is 256 without gradient accumulation. Increasing this batch size changes optimization behavior, so learning-rate schedules, warmup, and validation results may need adjustment. Adding workers without changing the training code or sampler does not automatically produce correct distributed execution.

Model parallelism distributes a model across devices when replication is impractical or individual operations exceed device memory. Tensor parallelism splits computations within layers, while pipeline parallelism places groups of layers on different devices and passes intermediate results between them. Sharded training approaches distribute parameters, gradients, or optimizer state to reduce per-device memory requirements. These strategies can be combined with data parallelism, but introduce additional communication and configuration complexity.

Cloud implementation starts with a framework and container that support the selected distribution strategy. Configure instance type and count, processes per host, worker ranks, rendezvous settings, accelerator libraries, and communication paths. A distributed-aware data loader should assign appropriate samples to each worker and avoid unintended duplication. Ensure that workers execute compatible numbers of training steps so collective operations do not hang. Fast storage, parallel data reads, and high-bandwidth, low-latency networking help prevent expensive accelerators from waiting for data or gradient exchange.

For example, an image classifier that fits on one GPU may benefit from synchronous data parallelism across several GPU instances. A large language model that does not fit on one GPU may instead require state sharding or model parallelism before additional data-parallel replicas are useful. A small tabular workload might train faster and cost less on one machine because provisioning, synchronization, and communication consume more time than parallel execution saves.

In an automated MLOps pipeline, define the training configuration as a versioned artifact alongside the code, container image, dataset references, and hyperparameters. The orchestrator submits the job, waits for completion, evaluates the resulting model, and permits registration or deployment only after quality checks pass. Configure resource limits, runtime limits, permissions, networking, logs, metrics, and checkpoint storage explicitly. Training and inference have separate resource requirements: a model trained across many workers does not necessarily require distributed inference, although a sufficiently large model may still need multiple devices for serving.

Checkpointing should preserve the model, optimizer, scheduler, and other state needed to resume consistently. All workers must coordinate checkpoint creation and recovery; sharded implementations may need multiple checkpoint files. Interruptible instances can reduce compute charges, but recovery requires compatible framework and job support. Coordinate early stopping across workers so one process does not exit while others wait in a collective operation.

Distributed training does not guarantee linear speedup, lower total cost, or better accuracy. Measure examples processed per second, accelerator utilization, communication overhead, validation quality, and total cost to reach the target quality. Benchmark progressively larger configurations: stragglers, network saturation, uneven data partitions, and insufficient input throughput can erase the benefit of additional workers.