distributed-llm-pretraining-torchtitan

name	distributed-llm-pretraining-torchtitan
description	Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
version	1.0.0
author	Orchestra Research
license	MIT
dependencies	["torch>=2.6.0","torchtitan>=0.2.0","torchao>=0.5.0"]
platforms	["linux","macos"]
metadata	{"mangaba":{"tags":["Model Architecture","Distributed Training","TorchTitan","FSDP2","Tensor Parallel","Pipeline Parallel","Context Parallel","Float8","Llama","Pretraining"]}}

TorchTitan - PyTorch Native Distributed LLM Pretraining

Quick start

TorchTitan is PyTorch's official platform for large-scale LLM pretraining with composable 4D parallelism (FSDP2, TP, PP, CP), achieving 65%+ speedups over baselines on H100 GPUs.

Installation:

# From PyPI (stable)
pip install torchtitan

# From source (latest features, requires PyTorch nightly)
git clone https://github.com/pytorch/torchtitan
cd torchtitan
pip install -r requirements.txt

Download tokenizer:

# Get HF token from https://huggingface.co/settings/tokens
python scripts/download_hf_assets.py --repo_id meta-llama/Llama-3.1-8B --assets tokenizer --hf_token=...

Start training on 8 GPUs:

CONFIG_FILE="./torchtitan/models/llama3/train_configs/llama3_8b.toml" ./run_train.sh

Common workflows

Workflow 1: Pretrain Llama 3.1 8B on single node

Copy this checklist:

Single Node Pretraining:
- [ ] Step 1: Download tokenizer
- [ ] Step 2: Configure training
- [ ] Step 3: Launch training
- [ ] Step 4: Monitor and checkpoint

Step 1: Download tokenizer

python scripts/download_hf_assets.py \
  --repo_id meta-llama/Llama-3.1-8B \
  --assets tokenizer \
  --hf_token=YOUR_HF_TOKEN

Step 2: Configure training

Edit or create a TOML config file:

# llama3_8b_custom.toml
[job]
dump_folder = "./outputs"
description = "Llama 3.1 8B training"

[model]
name = "llama3"
flavor = "8B"
hf_assets_path = "./assets/hf/Llama-3.1-8B"

[optimizer]
name = "AdamW"
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