While we are going to discuss the configuration in details next, the key to getting a huge improvement on a single GPU It provides a smart GPU memory management system, that minimizes memory fragmentation, which again allows you to fit.larger batch size, or enabling a fitting of a very big model which
Leave more GPU resources for model’s needs - e.g. It has a ZeRO-offload feature which can delegate some computations and memory to the host’s CPU and RAM, and thus.Why would you want to use DeepSpeed with just one GPU? The following documentation discusses the launcher options. By default, DeepSpeed deploys all GPUs it can see on the given node. This is almost the same as with multiple-GPUs, but here we tell DeepSpeed explicitly to use just one GPU via dataset_name wmt16 -dataset_config "ro-en" \ do_train -max_train_samples 500 -num_train_epochs 1 \ output_dir output_dir -overwrite_output_dir -fp16 \ model_name_or_path t5-small -per_device_train_batch_size 1 \ deepspeed tests/deepspeed/ds_config_zero2.json \ Trainer Deepspeed IntegrationĬopied deepspeed -num_gpus=1 examples/pytorch/translation/run_translation.py \ There is also DeepSpeed Inference - this is a totally different technology which uses Tensor Parallelism instead of It doesn’t use an optimizer and a lr scheduler and only stage 3 is relevant. It uses the same ZeRO protocol as training, but DeepSpeed ZeRO Inference supports ZeRO stage 3 with ZeRO-Infinity.
#INTEGRATED MASTER SCHEDULER FULL#
DeepSpeed ZeRO training supports the full ZeRO stages 1, 2 and 3 with ZeRO-Infinity (CPU and NVME offload).To tap into this feature read the docs on Parts of DeepSpeed like zero.Init for ZeRO stage 3 and higher. Yourself, core functionality functions like from_pretrained and from_config include integration of essential If you don’t use Trainer and want to use your own Trainer where you integrated DeepSpeed.This document is focused on this feature. Of integration - just supply your custom config file or use our template and you have nothing else to do. Integration of the core DeepSpeed features via Trainer.🤗 Transformers integrates DeepSpeed via 2 options:
Memory Wall for Extreme Scale Deep Learning.ĭeepSpeed ZeRO-2 is primarily used only for training, as its features are of no use to inference.ĭeepSpeed ZeRO-3 can be used for inference as well, since it allows huge models to be loaded on multiple GPUs, which And NVMe-support is described in the paper ZeRO-Infinity: Breaking the GPU ZeRO-Offload has its own dedicated paper: ZeRO-Offload: Democratizing Billion-Scale Model Training.
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