agentleFS
Sign inStart free
Red-Hat-AI-Innovation-TeamRed-Hat-AI-Innovation-Team/training_hub/.claude/skills/memory-estimation/SKILL.md

memory-estimation

Skill104 starsChanged 5 months agoApache-2.0View on GitHub

Use when the user wants to estimate GPU memory (VRAM) requirements for a training configuration, check if a model will fit on their GPUs, or plan GPU allocation for training.

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 2 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    If library=missing, tell the user to install training_hub first via the setup-guide skill.

    line 17
  2. Distinctive:

    Add more GPUs for data parallelism

    line 37
  3. Distinctive:

    If no method is specified, the script infers it from the configured algorithm.

    line 48

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

GPU Memory Estimation

Estimate GPU VRAM requirements before committing to a training run.

Step 1: Check Environment

"${CLAUDE_PLUGIN_ROOT}/scripts/th_detect.sh"

If library=missing, tell the user to install training_hub first via the setup-guide skill.

Step 2: Run Estimation

Execute the estimation script with user-provided parameters or config defaults:

"${CLAUDE_PLUGIN_ROOT}/scripts/th_estimate.sh" $ARGUMENTS

Step 3: Present Results

Parse the JSON output and present clearly:

  1. Memory estimates — Show low/mid/high VRAM estimates in GB
  2. GPU fit — Report whether the configuration fits on the available GPU(s)
  3. Recommendations — If memory is tight, suggest:
    • Reduce max_seq_len (e.g., 4096 -> 2048)
    • Reduce effective_batch_size
    • Switch to LoRA or QLoRA for lower memory
    • Add more GPUs for data parallelism

Estimation Methods

MethodForEstimator
basicSFT, GRPOBasicEstimator
osftOSFTOSFTEstimator
loraLoRA-SFT, LoRA-GRPOLoRAEstimator
qloraQuantized LoRAQLoRAEstimator

If no method is specified, the script infers it from the configured algorithm.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free