Distributed Training Setup
When a single GPU can't train your model fast enough — or at all — you need distributed training. We configure multi-GPU and multi-node training with proper parallelism strategies, communication backends, and fault tolerance so your training scales linearly, not logarithmically.
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Parallelism Strategy Selection
Data parallelism (DDP) works for models that fit on one GPU — replicate the model, split the data. Model parallelism splits large models across GPUs — necessary for 70B+ parameter models. Pipeline parallelism overlaps computation across stages. We analyze your model architecture and pick the strategy (or combination) that maximizes throughput. DeepSpeed ZeRO stages reduce memory per GPU for data-parallel training.
Communication Backend
NCCL handles GPU-to-GPU communication with proper topology awareness. We verify NVLink connectivity, configure NCCL environment variables for multi-node setups, and tune buffer sizes. For multi-node training, InfiniBand or high-bandwidth Ethernet gets configured with proper MTU and flow control. A misconfigured NCCL setup can make 8-GPU training slower than 1-GPU.
Kubernetes Training Operator
PyTorchJob, TFJob, or MPIJob custom resources launch distributed training on Kubernetes. We configure gang scheduling so all workers start simultaneously, shared volumes for data access, and proper resource requests for GPU and memory. Pod anti-affinity spreads workers across nodes for fault tolerance. Job queuing via Kueue manages training job priority and fair scheduling.
Fault Tolerance & Checkpointing
Elastic training (torch.distributed.elastic) handles worker failures by restarting from the latest checkpoint without losing the entire job. Checkpoints save to shared storage (S3, NFS) every N steps. Spot instance preemption triggers a graceful checkpoint before termination. We configure checkpoint frequency based on your training cost — a 48-hour training run needs more frequent saves than a 2-hour one.
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