Accelerate Distributed Train

Name: Accelerate Distributed Train
Price: 5 USD
Availability: InStock
Author: Agensi

Add multi-GPU and multi-node training to any PyTorch script with Hugging Face Accelerate in a few lines. Use when a training run must scale from one GPU to seve

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See it in action

You say

Convert my single-GPU train.py script to support multi-GPU training using Accelerate. I need to use BF16 precision and FSDP for a large model.

Your agent does

I will instrument your train.py.

Import and init Accelerator.
Wrap your model, optimizer, and loader with accelerator.prepare().
Replace loss.backward() with accelerator.backward(loss).
Guard your print statements with if accelerator.is_main_process. Run via: accelerate launch --mixed_precision bf16 train.py

What you get

Enable DDP or FSDP training on multiple GPUs without rewriting loops.Implement mixed precision training to save VRAM and increase throughput.Manage distributed checkpoints and logging across multiple nodes.Switch between DeepSpeed and standard DDP via simple configuration flags.

About this skill

The problem

Rewriting PyTorch training loops for multi-GPU support is error-prone and tedious. Manually managing device placement, gradient synchronization, and mixed precision often leads to boilerplate-heavy code that is difficult to scale or port between different hardware setups.

What it does

Instruments standard PyTorch scripts with the Hugging Face Accelerate wrapper to enable distributed execution.
Configures hardware backends including DDP, FSDP, and DeepSpeed via external config or flags rather than code changes.
Automates device placement and gradient accumulation to handle larger batch sizes and models.
Implements main-process guards and state saving/loading to ensure consistent logging and checkpointing across ranks.

Frameworks & tools

PyTorch, Hugging Face Accelerate, DeepSpeed, FSDP, and CUDA.

Why this beats prompting it yourself

Generic prompts often fail to handle the specific "contract" of distributed training, such as ensuring logging only happens on rank 0 or correctly replacing loss.backward(). This skill provides the exact touchpoints needed to port a script without breaking the training logic or causing deadlocks.

Use cases

Scale a local PyTorch training script to a 4-GPU workstation or cloud cluster.
Enable BF16 or FP16 mixed precision to reduce VRAM usage and speed up training.
Transition large models to FSDP sharding when they no longer fit on a single GPU.
Standardize checkpointing and logging across multi-node environments.