Distributed training runs model training across multiple CPU or GPU devices so larger datasets and models can be trained faster. A training service treats distributed code as a workload to schedule: it provisions a worker group, supplies framework-specific communication settings, starts the processes, and returns the trained model. This keeps execution infrastructure separate from model development. section 4.0 section 4.2.4 section 3.6
Choose the parallelism strategy according to the model and operational cost. Data parallelism partitions data among devices, has standardized conversion patterns, and usually needs only small changes to single-device code. Model parallelism splits a network into sequential subnetworks on different devices; pipeline parallelism further divides batches into microbatches so devices can overlap work rather than sit idle. Model and pipeline approaches require architecture-specific changes and are harder to debug. section 4.1 section 4.5
A production-oriented distributed run has distinct responsibilities:
- Data scientists adapt training code for the selected framework and request a worker-group size. section 4.2.4 section 4.3.1
- The training service allocates workers, assigns addresses and task indexes, initializes distributed libraries, and launches the processes. section 4.2.4
- Kubernetes manages cluster resources, pod execution, interworker communication, and job status for distributed jobs. section 4.3.1
- The master worker can report synchronized progress and persist models or checkpoints, avoiding duplicate output from every worker. section 4.5
TensorFlow worker configuration
Each TensorFlow worker receives a tf_config environment value describing the worker cluster and its own task type and index. A service generates these values automatically when dynamically provisioning workers, rather than requiring users to determine IP addresses and indexes themselves. section 4.2.4
tf_config = {
'cluster': {
'worker': ['192.168.4.53:12345', '192.168.4.55:23456']
},
# A 'task' provides information of the current task
# and is different for each worker. It specifies
# the 'type' and 'index' of that worker.
'task': {'type': 'worker', 'index': 0}
}
The configuration shows two worker endpoints and identifies the current worker by its task index, enabling distributed processes to locate one another. section 4.2.4
Use a unified training API across local Docker execution and Kubernetes-backed distributed execution. Containerized code and a clear protocol let the service run single-device or distributed workloads without interpreting model algorithms. TensorFlow, PyTorch, and Horovod differ in APIs and configuration, but their worker-based execution workflow can be supported through one service approach; Horovod can additionally provide one execution method across several frameworks. section 4.3.1 section 3.6 section 4.5
Distributed execution introduces operational concerns beyond speed: availability, resilience, and failure recovery matter for worker groups. When a model does not fit on one GPU, memory-saving techniques such as gradient accumulation or memory swapping are easier to implement but slow training; model parallelism is the alternative strategy. section 4.5
