deepspeed

DeepSpeed: Large-Scale Distributed Training

Overview

DeepSpeed is Microsoft's deep learning optimization library that enables efficient training of large models through ZeRO (Zero Redundancy Optimizer) optimization, pipeline parallelism, and mixed precision training. Apply this skill for memory-efficient training, large model fine-tuning, multi-GPU optimization, and reducing training costs.

When to Use This Skill

This skill should be used when:

• Training models with billions of parameters
• Memory optimization with ZeRO stages
• Multi-GPU distributed training
• Mixed precision training (FP16/BF16)
• Pipeline parallelism for model parallelism
• DeepSpeed integration with Hugging Face
• Optimizing training costs
• Large batch training

Quick Start

Basic Import and Setup

import deepspeed
import torch
import torch.nn as nn

Simple DeepSpeed Training

import deepspeed

# Model
model = nn.Linear(10, 10)

# Initialize DeepSpeed
model_engine, optimizer, _, _ = deepspeed.initialize(
    model=model,
    optimizer=torch.optim.Adam(model.parameters()),
    config={
        "train_batch_size": 8,
        "fp16": {"enabled": True},
        "zero_optimization": {"stage": 1},
    }
)

# Training loop
for batch in dataloader:
    batch = batch.to(model_engine.device)
    loss = model_engine(batch)
    model_engine.backward(loss)
    model_engine.step()

With Hugging Face Trainer

from transformers import Trainer, TrainingArguments
import deepspeed

# Training arguments with DeepSpeed
training_args = TrainingArguments(
    output_dir="./output",
    deepspeed="ds_config.json",
    num_train_epochs=3,
    per_device_train_batch_size=4,
)

# Trainer with DeepSpeed
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

trainer.train()

ZeRO Optimization

ZeRO Stages

Stage	Description	Memory Savings
Stage 1	Optimizer state partitioning	~4x
Stage 2	+ Gradient partitioning	~8x
Stage 3	+ Parameter partitioning	~N x