Distributed Training

Parallelism paradigms (DDP, TP, PP, FSDP) and scaling laws.

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Explanation

A focused 3-minute explanation using the topic's authored material.

Learning goals and prerequisites

After this lesson

Explain major parallelism strategies
Estimate memory and communication costs
Design a resilient training platform

Helpful before starting

Neural-network training and optimizers
GPU memory and matrix operations
Basic networking and distributed systems

Start here

Distributed Training, in plain language

Parallelism paradigms (DDP, TP, PP, FSDP) and scaling laws. Distributed training turns a model algorithm into a coordinated compute system where communication, memory, failure recovery, and reproducibility determine usable scale.

For a small example, two workers see different mini-batches. Compute local gradients, all-reduce their average, and compare the update with one combined batch. This is the mechanism to keep in view as the lesson becomes more technical. Before moving on, identify the input, transformation, output, and one observation that would falsify your conclusion.

Key points

Workers, ranks, process groups, synchronization, and collective operations.
Data parallelism, gradient averaging, global batch, and scaling efficiency.
Model state: parameters, gradients, optimizer states, and activations.

Use it well

When Distributed Training helps—and where it breaks

Parameters no longer fit on one accelerator. Choose data, tensor, or pipeline parallelism, estimate communication, profile utilization, and validate numerical parity. A useful result still depends on checking the assumptions and evidence below rather than treating one successful output as proof.

Key points

Adding GPUs before profiling one worker. Better approach: Establish a stable single-device throughput and memory baseline.
Changing global batch without retuning. Better approach: Preserve optimizer semantics or validate scaling rules.
Writing checkpoints from every rank unsafely. Better approach: Use coordinated sharded checkpoints with atomic completion metadata.

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