Add Uint Support
pytorch/pytorch
Add unsigned integer (uint) type support to PyTorch operators by updating ATDISPATCH macros.
Multi-GPU and distributed training patterns with PyTorch DDP.
Install Distributed Training
$ npx skills add aiming-lab/AutoResearchClaw --skill distributed-training -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aiming-lab/AutoResearchClaw distributed-training --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/researchclaw/skills/builtin/tooling/distributed-training .claude/skills/distributed-training && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "distributed-training" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/tooling/distributed-training into .claude/skills/distributed-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "distributed-training", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/tooling/distributed-trainingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add aiming-lab/AutoResearchClaw --skill distributed-training -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aiming-lab/AutoResearchClaw distributed-training --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/researchclaw/skills/builtin/tooling/distributed-training .agents/skills/distributed-training && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "distributed-training" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/tooling/distributed-training into .agents/skills/distributed-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "distributed-training", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aiming-lab/AutoResearchClaw --skill distributed-training -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aiming-lab/AutoResearchClaw distributed-training --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/researchclaw/skills/builtin/tooling/distributed-training .cursor/skills/distributed-training && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "distributed-training" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/tooling/distributed-training into .cursor/skills/distributed-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "distributed-training", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/aiming-lab/AutoResearchClaw.git --path researchclaw/skills/builtin/tooling/distributed-training--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add aiming-lab/AutoResearchClaw --skill distributed-training -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aiming-lab/AutoResearchClaw distributed-training --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/researchclaw/skills/builtin/tooling/distributed-training .gemini/skills/distributed-training && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "distributed-training" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/tooling/distributed-training into .gemini/skills/distributed-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "distributed-training", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install aiming-lab/AutoResearchClaw distributed-trainingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add aiming-lab/AutoResearchClaw --skill distributed-training -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/researchclaw/skills/builtin/tooling/distributed-training .github/skills/distributed-training && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "distributed-training" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/tooling/distributed-training into .github/skills/distributed-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "distributed-training", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aiming-lab/AutoResearchClaw --skill distributed-training -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aiming-lab/AutoResearchClaw distributed-training --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/researchclaw/skills/builtin/tooling/distributed-training .opencode/skills/distributed-training && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "distributed-training" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/tooling/distributed-training into .opencode/skills/distributed-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "distributed-training", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
distributed-training- GitHub stars
- 15k
- Token cost
- ~216 tokens
- SKILL.md length
- 52 words
- Files
- 1
- Skills in repo
- 34
- Repo updated
- First seen
- Licence
- MIT
At a glance
Multi-GPU and distributed training patterns with PyTorch DDP.
- Works in 7 steps: Use DistributedDataParallel (DDP) over… → Initialize process group:… → Use DistributedSampler for data sharding → …
- Scaling training across GPUs
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Tasks that involve Deep learning
What it does
Distributed Training is an agent skill from aiming-lab/AutoResearchClaw. Multi-GPU and distributed training patterns with PyTorch DDP. Use when scaling training across GPUs.
Its SKILL.md is about 220 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering Deep learning. It works with PyTorch. The repository describes itself as: Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞. The licence is MIT.
When your agent uses it
- Scaling training across GPUs
- Tasks that involve Deep learning
Example prompts
- “/distributed-training”
Workflow steps
7 steps, taken from the first numbered list in SKILL.md.
- Use DistributedDataParallel (DDP) over DataParallel for multi-GPU
- Initialize process group: dist.init_process_group(backend='nccl')
- Use DistributedSampler for data sharding
- Synchronize batch norm: nn.SyncBatchNorm.convert_sync_batchnorm()
- Only save checkpoint on rank 0
- Scale learning rate linearly with world size
- Use gradient accumulation for effectively larger batch sizes
What it can do on your machine
Read from SKILL.md and the folder at commit be4ba47. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Distributed Training loads about 216 tokens when it runs. Until then it costs about 30 tokens; SKILL.md has 52 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from aiming-lab/AutoResearchClaw at commit be4ba47, republished under its MIT licence (© aiming-lab). 52 words, ~216 tokens.
Download SKILL.mdSave it as
.claude/skills/distributed-training/SKILL.md (or your agent's skills folder).- name
- distributed-training
- description
- Multi-GPU and distributed training patterns with PyTorch DDP. Use when scaling training across GPUs.
- metadata.category
- tooling
- metadata.trigger-keywords
- distributed,multi-gpu,parallel,ddp,scale
- metadata.applicable-stages
- 10,12
- metadata.priority
- 7
- metadata.version
- 1.0
- metadata.author
- researchclaw
- metadata.references
- PyTorch DDP Tutorial, pytorch.org; Goyal et al., Accurate Large Minibatch SGD, 2017
Distributed Training Best Practice
- Use DistributedDataParallel (DDP) over DataParallel for multi-GPU
- Initialize process group: dist.init_process_group(backend='nccl')
- Use DistributedSampler for data sharding
- Synchronize batch norm: nn.SyncBatchNorm.convert_sync_batchnorm()
- Only save checkpoint on rank 0
- Scale learning rate linearly with world size
- Use gradient accumulation for effectively larger batch sizes
© aiming-lab, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in researchclaw/skills/builtin/tooling/distributed-training of aiming-lab/AutoResearchClaw.
Open the folder on GitHubat commit be4ba47
Compare with similar skills
Distributed Training next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Distributed Training this skillaiming-lab/AutoResearchClaw | 15k | — | ~216 | Automated safety check: Pass | MIT | |
| Add Uint Supportpytorch/pytorch | 104k | 2 repos | ~2.3k | Automated safety check: Pass | Custom licence | |
| CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Add Torch Shapes Examplefacebook/pyrefly | 7.1k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Interview Cheatsheetwanshuiyin/ARIS-in-AI-Offer | 574 | 1 repos | ~3.4k | Automated safety check: Notes | MIT | |
| Ghstack CIpytorch/pytorch | 104k | — | ~1.4k | Automated safety check: Pass | Custom licence |
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Related
Works with
Categories
Questions about Distributed Training
What does Distributed Training do?
Multi-GPU and distributed training patterns with PyTorch DDP. Distributed Training is an agent skill from aiming-lab/AutoResearchClaw. Multi-GPU and distributed training patterns with PyTorch DDP.
When should I use Distributed Training?
Distributed Training fits situations like: scaling training across GPUs; tasks that involve Deep learning.
How do I install Distributed Training in Claude Code?
Run `npx skills add aiming-lab/AutoResearchClaw --skill distributed-training -a claude-code`. Or copy the skill folder (researchclaw/skills/builtin/tooling/distributed-training in aiming-lab/AutoResearchClaw) into .claude/skills/distributed-training in your project. Claude Code loads it when a task matches its description.
How do I install Distributed Training in Codex?
Run `npx skills add aiming-lab/AutoResearchClaw --skill distributed-training -a codex`. Or copy the skill folder (researchclaw/skills/builtin/tooling/distributed-training in aiming-lab/AutoResearchClaw) into .agents/skills/distributed-training in your project. Codex loads it when a task matches its description.
Can I use Distributed Training in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aiming-lab/AutoResearchClaw --skill distributed-training -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/distributed-training, .gemini/skills/distributed-training, .github/skills/distributed-training and .opencode/skills/distributed-training in your project.
What does Distributed Training need to run?
SKILL.md names no scripts, command-line tools or credentials: Distributed Training is instructions for the agent only.
Does Distributed Training access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Distributed Training safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Distributed Training use?
Distributed Training is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Distributed Training use?
About 216 tokens (SKILL.md is roughly 864 characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Distributed Training?
Skills that share tags, products or a category with Distributed Training: Add Uint Support (pytorch/pytorch, 104k stars), CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars), Add Torch Shapes Example (facebook/pyrefly, 7.1k stars) and Interview Cheatsheet (wanshuiyin/ARIS-in-AI-Offer, 574 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Distributed Training?
aiming-lab (a GitHub organization) maintains it in aiming-lab/AutoResearchClaw, which has 14,587 GitHub stars. The repository holds 34 skills in this directory. The repository was last updated on August 19, 2026.
Source: aiming-lab/AutoResearchClaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.