Optimizing Large-Scale Model Training with Microsoft DeepSpeed and ZeRO Architecture
Memory Allocation Dynamics in Distributed Training
Training massive neural networks introduces severe GPU VRAM bottlenecks that traditional data parallelism cannot resolve. In a standard multi-GPU setup, memory consumption is dominated by two distinct categories: model states and residual memory. Model states encompass trainable parameters, gra ...
Posted on Mon, 28 Sep 2026 16:24:46 +0000 by snapy
Multi-Node Multi-GPU Training: Comparing Accelerate and DeepSpeed for Large Model Pre-training
Initial weight synchronization: ```
ls -l /data/xxx/gpu008/MoeRemake/train/etuning/LLaMA-Factory2/models/xxx-Base-10B-200k-Llama
Environment synchronization: ```
./scp_batch.sh "/data/xxx/miniconda3/envs/etuning4/" "/data/vayu/miniconda3/envs/etuning4/" gpu004 gpu006
./scp_batch.sh "/data/xxx/train/etuning/LLaMA-Fact ...
Posted on Sun, 09 Aug 2026 16:54:29 +0000 by ddemore
DeepSpeed Launcher Runner Implementation for Multi-node Multi-GPU Training
from copy import deepcopy
def filter_host_resources(host_info, include_list="", exclude_list=""):
'''Process inclusion or exclusion specifications for host resource filtering.
Format is NODE_SPEC[@NODE_SPEC ...], where
NODE_SPEC = NAME[:SLOT[,SLOT ...]].
Omitting :SLOT includes/excludes all slots on tha ...
Posted on Tue, 04 Aug 2026 16:15:09 +0000 by g.grillo