Skip to content
Back to skills

Training Eval Cli

ASecurity

"Use this repo skill to build and troubleshoot Swin-Transformer

  • 247 stars
  • 0 votes
  • 0 copies
  • 2 views
  • Added September 9, 2026
toolsapi

Works with

  • cli
  • api

Security analysis

A100/100

Pro scans all 5 files and shows the line behind each finding

Scanned September 9, 2026

npx -y skills add VectorSpaceLab/AREX-Skill --skill training-eval-cli --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Training Eval Cli?

Add the live security badge to your README. It updates with every re-scan.

Security grade badge for Training Eval Cli
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/vectorspacelab-training-eval-cli/badge)](https://www.skillsdirectory.com/skills/vectorspacelab-training-eval-cli)

More formats (shields.io, HTML) on the badges page. Keep it an A: scan every change in CI with Pro.

Download with Pro
SKILL.md
---
name: training-eval-cli
description: "Use this repo skill to build and troubleshoot Swin-Transformer
  supervised ImageNet training, fine-tuning, evaluation, throughput, DDP, AMP,
  and config override commands."
disable-model-invocation: true
metadata:
  disco-role: operating
license: MIT
---

# training-eval-cli

Use this sub-skill when a task asks for supervised `main.py` workflows: training from scratch, evaluation, fine-tuning from pretrained weights, throughput measurement, command-line flags, distributed launchers, memory levers, or config overrides.

## Covers

- `main.py` train/eval/fine-tune/throughput workflow shape.
- `--cfg`, `--data-path`, `--zip`, `--cache-mode`, `--pretrained`, `--resume`, `--accumulation-steps`, `--use-checkpoint`, AMP and fused flags.
- `torchrun`/distributed-launch command templates and `LOCAL_RANK` behavior.
- Supervised config families under Swin V1, Swin V2, and Swin-MLP.
- Safe command validation without running training.

## Routes elsewhere

- Data root or checkpoint-schema diagnosis: `data-and-checkpoints`.
- Model constructor/API questions: `core-models`.
- SimMIM pretraining/fine-tuning: `simmim-workflows`.
- Swin-MoE/Tutel/fused CUDA extension: `moe-and-acceleration`.

## Workflow

1. Pick a config family using `../../references/model-zoo-and-configs.md`.
2. Validate data/checkpoint assumptions with `data-and-checkpoints`.
3. Compose a command using `../../scripts/swin_cli_command_builder.py` or the recipes in `references/supervised-workflows.md`.
4. Validate the command shape with `scripts/validate_swin_command.py --repo-root <checkout> -- <command>`. The validator never launches training.
5. If the command still fails, read `references/troubleshooting.md` for DDP, memory, config, AMP, and optional dependency issues.

## Safety warning

Actual `main.py` training/evaluation is GPU/data/checkpoint heavy. Do not run full native training as a quick check. Use CPU model construction and command validation first.

## Linked files

- `references/supervised-workflows.md` - recipes for train, eval, fine-tune, and throughput.
- `references/cli-reference.md` - flag meanings and config side effects.
- `references/troubleshooting.md` - launcher, memory, and flag mistakes.
- `scripts/validate_swin_command.py` - safe command-shape validator.

Files in this skill

  • SKILL.md2.3 KB
  • references/cli-reference.md1.3 KB
  • references/supervised-workflows.md1.6 KB
  • references/troubleshooting.md1.3 KB
  • scripts/validate_swin_command.py3.9 KB

Attribution

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments

Loading comments…

Newsletter

Get the weekly list of safe new skills, and the ones we pulled.