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huggingface-accelerate

Automate distributed training setup across CPU, GPU, and TPU environments.

Updated May 3, 2026
Search Tags:#pytorch#distributed-training#fsdp#deepspeed#accelerate#megatron-lm
One-click install
npx skills add https://github.com/Yangel-hide/video-production-planner-agent --skill huggingface-accelerate-yangel-hide
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: huggingface-accelerate
Source: https://github.com/Yangel-hide/video-production-planner-agent/tree/main/optional-skills/mlops/accelerate
Command: npx skills add https://github.com/Yangel-hide/video-production-planner-agent --skill huggingface-accelerate-yangel-hide

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Accelerate simplifies distributed training by unifying the setup across CPU, single-GPU, multi-GPU, and TPU environments, dramatically reducing boilerplate.

Core Features & Use Cases

  • Unified API and one-launch workflow for DDP, DeepSpeed, FSDP, and Megatron-LM with automatic device placement and mixed-precision support.
  • Interactive configuration and single-command launch to scale training across diverse hardware.
  • Performance optimizations and best practices such as gradient accumulation, gradient bucketing, and checkpointing across backends.

Quick Start

Configure Accelerate with accelerate config and start training with accelerate launch.

Frequently Asked Questions about huggingface-accelerate

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up distributed training across multi-GPU and TPU environments?▼

To set up distributed training across multi-GPU and TPU environments, you can use a unified API that automates device placement and mixed-precision configurations with a single code path.

What is the best way to switch between DeepSpeed and FSDP without rewriting PyTorch code?▼

The best way to switch between DeepSpeed and FSDP is using a unified API that applies automatic device placement and mixed-precision support, allowing you to change backends via interactive configuration.

How do I launch PyTorch distributed training after configuring accelerate?▼

To launch PyTorch distributed training, first run an interactive configuration to set hardware backends, then use a single launch command to start the training workflow across your configured devices.

Does this distributed training approach work with Megatron-LM and mixed-precision?▼

Yes, this distributed training approach works with Megatron-LM and mixed-precision, applying automatic device placement and unified workflow configurations across CPU, single-GPU, multi-GPU, and TPU setups.

Can I apply gradient accumulation and checkpointing across different distributed backends?▼

Yes, you can apply gradient accumulation, gradient bucketing, and checkpointing across different distributed backends using performance optimizations and best practices built into the unified training API.

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