playbooks

home / skills / jeremylongshore / claude-code-plugins-plus-skills / pytorch-model-trainer

pytorch-model-trainer skill

/skills/07-ml-training/pytorch-model-trainer

This skill guides PyTorch model trainer workflows, generates production-ready configurations, and validates results to accelerate ML training projects.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill pytorch-model-trainer

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
2.2 KB
---
name: "pytorch-model-trainer"
description: |
  Build pytorch model trainer operations. Auto-activating skill for ML Training.
  Triggers on: pytorch model trainer, pytorch model trainer
  Part of the ML Training skill category. Use when working with pytorch model trainer functionality. Trigger with phrases like "pytorch model trainer", "pytorch trainer", "pytorch".
allowed-tools: "Read, Write, Edit, Bash(python:*), Bash(pip:*)"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# Pytorch Model Trainer

## Overview

This skill provides automated assistance for pytorch model trainer tasks within the ML Training domain.

## When to Use

This skill activates automatically when you:
- Mention "pytorch model trainer" in your request
- Ask about pytorch model trainer patterns or best practices
- Need help with machine learning training skills covering data preparation, model training, hyperparameter tuning, and experiment tracking.

## Instructions

1. Provides step-by-step guidance for pytorch model trainer
2. Follows industry best practices and patterns
3. Generates production-ready code and configurations
4. Validates outputs against common standards

## Examples

**Example: Basic Usage**
Request: "Help me with pytorch model trainer"
Result: Provides step-by-step guidance and generates appropriate configurations


## Prerequisites

- Relevant development environment configured
- Access to necessary tools and services
- Basic understanding of ml training concepts


## Output

- Generated configurations and code
- Best practice recommendations
- Validation results


## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |


## Resources

- Official documentation for related tools
- Best practices guides
- Community examples and tutorials

## Related Skills

Part of the **ML Training** skill category.
Tags: ml, training, pytorch, tensorflow, sklearn

Overview

This skill helps build and run PyTorch model trainer operations for end-to-end ML training workflows. It auto-activates for PyTorch training tasks and generates production-ready code, configs, and validation checks. The goal is to speed development of robust training pipelines with sensible defaults and best-practice guidance.

How this skill works

The skill inspects your training intent and project context, then produces step-by-step trainer code, data pipelines, hyperparameter tuning setups, and experiment logging hooks. It validates generated artifacts against common standards (device handling, reproducibility, checkpointing) and suggests corrections or dependency fixes. Outputs include runnable Python trainer modules, configuration files, and recommended commands for execution and monitoring.

When to use it

  • You need a complete PyTorch training loop or trainer class scaffolded quickly
  • You want production-ready training code with checkpointing, logging, and device-aware behavior
  • You are preparing hyperparameter search or experiment tracking (e.g., weights & biases, TensorBoard)
  • You need best-practice patterns for data loading, augmentation, and batching
  • You want validation of trainer configuration and dependency guidance

Best practices

  • Structure trainer as a reusable class separating model, data, and training logic
  • Handle devices and mixed precision explicitly; include reproducibility seeds and deterministic flags
  • Implement robust checkpointing and resume logic with metadata (epoch, optimizer state, RNG states)
  • Use lightweight config files (YAML/JSON) and a cli entrypoint for reproducible runs
  • Integrate experiment tracking and clear metrics; log both scalar metrics and example predictions

Example use cases

  • Scaffold a PyTorch Trainer class with training/validation/test loops and checkpointing
  • Generate data loader code with transforms, augmentation pipelines, and efficient batching
  • Create hyperparameter sweep configs and launch scripts for local or cloud runs
  • Add mixed-precision training, gradient accumulation, and learning-rate schedulers to an existing project
  • Validate training configs and suggest missing dependencies or recommended runtime flags

FAQ

What prerequisites are required?

A Python environment with PyTorch installed, access to any dataset locations, and optional tracking tools (W&B, TensorBoard). Basic familiarity with PyTorch concepts speeds adoption.

Can it generate code for distributed or multi-GPU training?

Yes. It can produce patterns for DataParallel, DistributedDataParallel, and launcher scripts, and recommends environment setup and process spawning practices.

How does validation work for generated configs?

The skill checks for common pitfalls: missing optimizer state in checkpoints, incorrect device moves, absent seed settings, and incompatible dataloader parameters, then offers fixes.