Accelerate — Run PyTorch training across GPUs with minimal changes
Accelerate — Run PyTorch training across GPUs with minimal changes
Start with meaning, then move to detail.
This lesson explains Accelerate — Run PyTorch training across GPUs with minimal changes as part of extending Hermes and connecting external tools. You will learn what it does, when it matters, and the smallest safe test that proves it works.
If you are new, do not memorize names. Focus on three questions: what problem does this solve, what access does it need, and how can you verify the result?
For practice, inspect the first example, identify its effects, run it on test data, and compare the result with the source claim.
For advanced readers, inspect Skill metadata, Reference: full SKILL.md, Quick start, then verify failure modes and version compatibility.
Complete installation and one successful task before adding new capabilities.
A clear outcome before you read.
- Understand Accelerate — Run PyTorch training across GPUs with minimal changes without assumed prior knowledge.
- Separate the source description from what still needs testing in your environment.
- Read the first command and identify its inputs and outputs before copying it.
Short definitions before the details.
- Skill
- An instruction bundle that teaches Hermes a repeatable workflow without necessarily adding an external service.
Run PyTorch training across GPUs with minimal changes
What does the source say, and in what order?
- 01Skill metadata
Start here to understand the core idea or structure.
- 02Reference: full SKILL.md
Read this after the foundation, then connect it to the previous step.
- 03Quick start
Read this after the foundation, then connect it to the previous step.
- 04Common workflows
Read this after the foundation, then connect it to the previous step.
- 05Workflow 1: From single GPU to multi-GPU
Read this after the foundation, then connect it to the previous step.
- 06Workflow 2: Mixed precision training
Read this after the foundation, then connect it to the previous step.
- 07Workflow 3: DeepSpeed ZeRO integration
Read this after the foundation, then connect it to the previous step.
- 08Workflow 4: FSDP (Fully Sharded Data Parallel)
Read this after the foundation, then connect it to the previous step.
- 09Workflow 5: Gradient accumulation
Read this after the foundation, then connect it to the previous step.
- 10When to use vs alternatives
Finish here to verify the result and special cases.
Copy only after you understand the effect.
pip install accelerate**Run** (single command):## Common workflows
### Workflow 1: From single GPU to multi-GPU
**Original script**:Read the first command and identify its inputs and outputs before copying it.
Match every command to your installed Hermes version, review the files and accounts it can reach, and use non-sensitive data for the first test. If this explanation differs from the source, the official source wins.