Deepspeed
CheckedDeepspeed optimizes large model training by enabling efficient memory usage and faster execution, boosting productivity for AI developers.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
Run this in your project
terminal$ npx skills add davila7/claude-code-templates --skill deepspeed -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
davila7
Taken from the source listing. We haven't verified who controls this account.
Install target
Live repository
Not pinned. Installing fetches whatever the repository holds at the time you run the command.
Automated safety checks
This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.
Declares what it does: passed
SKILL.md parses and declares a name and a description.
SKILL.md exists, its frontmatter parses, and it declares a name and a description.
No undeclared network calls: passed
Makes no network calls.
Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.
No obfuscated payloads: passed
No encoded or obfuscated payloads.
No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.
No credential access: passed
Reads no keys, tokens, keychains or credential files.
Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.
No pipe-to-shell installers: passed
No pipe-to-shell or download-and-run installers.
No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.
Pinned to a commit: passed
Scanned davila7/claude-code-templates at 79182c51fb.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 79182c51fb
- Scanned
- 9 files, 2,468 lines
About this skill
The DeepSpeed skill enables AI agents to optimize large-scale model training through advanced techniques like ZeRO optimization, gradient checkpointing, and mixed precision. It allows agents to manage memory usage efficiently and accelerate training on distributed hardware setups.
When to use it
- Training models with billions of parameters that exceed single-GPU memory limits.
- Reducing communication overhead between multiple GPUs or nodes during parallel processing.
- Implementing ZeRO stages (offload, partition) to minimize redundant data storage in memory.
- Accelerating convergence by enabling mixed precision training without sacrificing model accuracy.
Key capabilities
- ZeRO Optimization: Dynamically partitions optimizer states, gradients, and parameters across devices.
- Gradient Checkpointing: Trades compute for memory to store intermediate activations only when needed.
- Mixed Precision Training: Utilizes FP16 or BF16 arithmetic to speed up computation and save VRAM.
- Distributed Training Support: Facilitates efficient scaling across multi-GPU and multi-node clusters.
Example prompts
- "Configure DeepSpeed ZeRO stage 3 for a transformer model training on four A100 GPUs."
- "Set up mixed precision training with gradient checkpointing to fit a 7B parameter model into limited VRAM."
- "Optimize the DeepSpeed configuration file to reduce communication overhead during distributed fine-tuning."
Tips & gotchas
Ensure your hardware supports the required precision modes (e.g., BF16 support on newer NVIDIA GPUs) before enabling mixed precision. Always validate ZeRO stage compatibility with your specific model architecture to avoid runtime errors.