Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). I
Install
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill accelerate -a cursor -y
Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
name: huggingface-accelerate
description: Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Distributed Training, HuggingFace, Accelerate, DeepSpeed, FSDP, Mixed Precision, PyTorch, DDP, Unified API, Simple]
dependencies: [accelerate, torch, transformers]
HuggingFace Accelerate - Unified Distributed Training
Quick start
Accelerate simplifies distributed training to 4 lines of code.
Installation:
pip install accelerate
Convert PyTorch script (4 lines):
import torch
+ from accelerate import Accelerator
+ accelerator = Accelerator()
model = torch.nn.Transformer()
optimizer = torch.optim.Adam(model.parameters())
dataloader = torch.utils.data.DataLoader(dataset)
+ model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)
for batch in dataloader:
optimizer.zero_grad()
loss = model(batch)
- loss.backward()
+ accelerator.backward(loss)
optimizer.step()
Run (single command):
accelerate launch train.py
Common workflows
Workflow 1: From single GPU to multi-GPU
Original script:
# train.py
import torch
model = torch.nn.Linear(10, 2).to('cuda')
optimizer = torch.optim.Adam(model.parameters())
dataloader = torch.utils.data.DataLoader(dataset, batch_size=32)
for epoch in range(10):
for batch in dataloader:
batch = batch.to('cuda')
optimizer.zero_grad()
loss = model(batch).mean()
loss.backward()
optimizer.step()
With Accelerate (4 lines added):
# train.py
import torch
from accelerate import Accelerator # +1
accelerator = Accelerator() # +2
model = torch.nn.Linear(10, 2)
optimizer = torch.optim.Adam(model.parameters())
dataloader = torch.utils.data.DataLoader(dataset, batch_size=32)
model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader) # +3
for epoch in range(10):
for batch in dataloader:
# No .to('cuda') needed - automatic!
optimizer.zero_grad()
loss = model(batch).mean()
accelerator.backward(loss) # +4
optimizer.step()
PyTorch Lightning: Need callbacks, high-level abstractions
Ray Train: Multi-node orchestration, hyperparameter tuning
DeepSpeed: Direct API control, advanced features
Raw DDP: Maximum control, minimal abstraction
Common issues
Issue: Wrong device placement
Don't manually move to device:
# WRONG
batch = batch.to('cuda')
# CORRECT
# Accelerate handles it automatically after prepare()
Issue: Gradient accumulation not working
Use context manager:
# CORRECT
with accelerator.accumulate(model):
optimizer.zero_grad()
accelerator.backward(loss)
optimizer.step()
Issue: Checkpointing in distributed
Use accelerator methods:
# Save only on main process
if accelerator.is_main_process:
accelerator.save_state('checkpoint/')
# Load on all processes
accelerator.load_state('checkpoint/')
Issue: Different results with FSDP
Ensure same random seed:
from accelerate.utils import set_seed
set_seed(42)
Advanced topics
Megatron integration: See references/megatron-integration.md for tensor parallelism, pipeline parallelism, and sequence parallelism setup.
Custom plugins: See references/custom-plugins.md for creating custom distributed plugins and advanced configuration.
Performance tuning: See references/performance.md for profiling, memory optimization, and best practices.
Used by: HuggingFace Transformers, TRL, PEFT, all HF libraries
How do I install huggingface-accelerate?
Download 08-distributed-training/accelerate/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does huggingface-accelerate live?
The publisher keeps it at https://github.com/Orchestra-Research/AI-Research-SKILLs.
Which agents can use huggingface-accelerate?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.