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Artículo: AMZ-B0G1XCC7WY
DEEPSPEED: THE COMPLETE GUIDE TO DISTRIBUTED DEEP LEARNING: Train large models efficiently with ZeRO optimization, pipeline parallelism, and mixed-precision training at scale
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0.55 kg
0.55 kg
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Amazon
Amazon
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USA
Sobre este producto
- Train large transformer models efficiently with DeepSpeed and turn cluster resources into stable throughput.Scaling models is hard when memory pressure, communication overhead, and fragile precision settings stall progress. This guide shows when DeepSpeed is the right tool compared to DDP or FSDP, then walks you through practical configurations that fit bigger models and longer sequences without guesswork.You will learn how to select ZeRO stages, shape batches, and tune overlap so GPUs stay busy. Every chapter focuses on decisions that change outcomes, not theory.decide when DeepSpeed beats plain DDP or FSDP and whyapply ZeRO stages 1 to 3, including offload to cpu and nvmetune reduce scatter and allgather buckets for overlapuse pipeline parallel and tensor parallel in 3d layoutsset mixed precision correctly, bf16 fp16 and fp8 recipesbuild fast data input with webdataset shards and daliconfigure nccl and cluster fabric on aws efa azure ib gcp rocelaunch with deepspeed json, torchrun elastic, slurm, kubernetescheckpoint, resume, and convert zero 3 partitioned savesplan inference at scale, kv cache and long sequence tradeoffsintegrate with hugging face trainer, accelerate, and megatron deepspeedfollow end to end case studies, 13b on 8 gpus and 70b across nodesuse a troubleshooting playbook for hangs oom divergence and slow stepsmeasure tokens per second and tokens per dollar with a repeatable labThis is a code heavy guide with runnable json configs and compact python examples so you can copy, adapt, and ship real training runs.Grab your copy today and make large scale training routine.
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