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name: "mixed-precision-trainer" description: | Manage mixed precision trainer operations. Auto-activating skill for ML Training. Triggers on: mixed precision trainer, mixed precision trainer Part of the ML Training skill category. Use when working with mixed precision trainer functionality. Trigger with phrases like "mixed precision trainer", "mixed trainer", "mixed". allowed-tools: "Read, Write, Edit, Bash(python:), Bash(pip:)" version: 1.0.0 license: MIT author: "Jeremy Longshore jeremy@intentsolutions.io"

Mixed Precision Trainer

Overview

This skill provides automated assistance for mixed precision trainer tasks within the ML Training domain.

When to Use

This skill activates automatically when you:

• Mention "mixed precision trainer" in your request
• Ask about mixed precision 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 mixed precision 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 mixed precision 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