Distributed Training
Distributed training distributes ML training across multiple GPUs or machines – necessary for models that don't fit on a single GPU.
Distributed training distributes ML training across many GPUs – data parallel, model parallel, and pipeline parallel enable training of billion-parameter models.
Explanation
Strategies: Data parallel (same model copy, different data), model parallel (model split), pipeline parallel (layers distributed). Tools: DeepSpeed, FSDP, Megatron-LM. For LLM training, thousands of GPUs are combined.
Marketing Relevance
Without distributed training, no LLM training would be possible – GPT-4 used an estimated 10,000+ GPUs.
Origin & History
Data parallel training became popular with MapReduce approaches. Horovod (Uber, 2018) simplified multi-GPU training. DeepSpeed (Microsoft, 2020) brought ZeRO optimization for memory efficiency. FSDP (PyTorch, 2022) integrated sharding natively. Megatron-LM (NVIDIA) combines all parallelism strategies for maximum scaling.
Comparisons & Differences
Distributed Training vs. Data Parallel vs Model Parallel
Data parallel: model on every GPU, data split (simple). Model parallel: model split (needed when model > 1 GPU).