Distributed & Parallel Training

30+

Scale training from single GPU to multi-node clusters

Scale from a single GPU to hundreds with data parallelism (DDP, FSDP), model parallelism (tensor, pipeline), and memory optimization (ZeRO, gradient checkpointing). Train billion-parameter models efficiently across GPUs and nodes, all in pure C#.

Large Model Training Foundation Models Multi-GPU Clusters Cloud Training LLM Pre-training Vision Model Training Research Labs Enterprise AI

Data Parallelism

Replicate models across GPUs, each processing different data batches.

DDP

Distributed Data Parallel with AllReduce gradient synchronization.

FSDP

Fully Sharded Data Parallel sharding model parameters across GPUs.

ZeRO Stage 1/2/3

Zero Redundancy Optimizer progressively sharding states, gradients, and parameters.

Gradient Accumulation

Simulate larger batches by accumulating gradients across micro-batches.

Mixed Precision

FP16/BF16 training with loss scaling for 2x speed and 50% memory reduction.

Model Parallelism

Split large models across multiple GPUs when they exceed single GPU memory.

Tensor Parallelism

Split individual layers across GPUs for intra-layer parallelism.

Pipeline Parallelism

Split model into stages processed by different GPUs with micro-batching.

Expert Parallelism

Distribute Mixture-of-Experts layers across GPUs.

Sequence Parallelism

Split long sequences across GPUs for attention computation.

Memory Optimization

Reduce memory footprint to train larger models on existing hardware.

Gradient Checkpointing

Recompute activations during backward pass saving 60-70% memory.

Activation Recomputation

Selective activation recomputation for optimal memory-compute tradeoff.

Communication Overlap

Overlap gradient communication with computation for higher throughput.

Distributed training with AiModelBuilder

using AiDotNet;

// Distributed training with AiModelBuilder
var result = await new AiModelBuilder<float, float[], float>()
    .ConfigureModel(new NeuralNetwork<float>(
        inputSize: 784, hiddenSize: 512, outputSize: 10))
    .ConfigureDistributedTraining(new FSDPConfig(
        shardingStrategy: ShardingStrategy.FullShard,
        mixedPrecision: MixedPrecisionPolicy.BFloat16,
        gradientCheckpointing: true))
    .ConfigureOptimizer(new AdamWOptimizer<float>(
        learningRate: 1e-3f, weightDecay: 0.01f))
    .BuildAsync(features, labels);

var prediction = result.Predict(newSample);

Start building with Distributed & Parallel Training

All 30+ implementations are included free under Apache 2.0.

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