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home / skills / jeremylongshore / claude-code-plugins-plus-skills / torchscript-exporter

torchscript-exporter skill

/skills/08-ml-deployment/torchscript-exporter

This skill provides production-ready guidance and code for torchscript exporter tasks, optimizing deployment and validation across ML serving pipelines.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill torchscript-exporter

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

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SKILL.md
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---
name: "torchscript-exporter"
description: |
  Export torchscript exporter operations. Auto-activating skill for ML Deployment.
  Triggers on: torchscript exporter, torchscript exporter
  Part of the ML Deployment skill category. Use when working with torchscript exporter functionality. Trigger with phrases like "torchscript exporter", "torchscript exporter", "torchscript".
allowed-tools: "Read, Write, Edit, Bash(cmd:*), Grep"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# Torchscript Exporter

## Overview

This skill provides automated assistance for torchscript exporter tasks within the ML Deployment domain.

## When to Use

This skill activates automatically when you:
- Mention "torchscript exporter" in your request
- Ask about torchscript exporter patterns or best practices
- Need help with machine learning deployment skills covering model serving, mlops pipelines, monitoring, and production optimization.

## Instructions

1. Provides step-by-step guidance for torchscript exporter
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 torchscript exporter"
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 deployment 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 Deployment** skill category.
Tags: mlops, serving, inference, monitoring, production

Overview

This skill automates TorchScript exporter tasks to prepare PyTorch models for production deployment. It provides step-by-step guidance, generates export code and configuration, and validates outputs against common deployment standards. The skill auto-activates when you reference TorchScript exporter functionality and is focused on ML deployment workflows.

How this skill works

The skill inspects your model code, dependencies, and target runtime constraints, then generates TorchScript-compatible export code and configuration snippets. It recommends tracing vs scripting approaches, produces example export commands, and verifies exported artifacts for common issues like missing buffers or unsupported ops. It can also output minimal CI/CD steps and runtime checks to integrate the artifact into serving pipelines.

When to use it

  • You need to export a PyTorch model to TorchScript for inference or edge deployment.
  • You want guidance choosing tracing vs scripting or resolving unsupported ops.
  • You are preparing models for model server integration or mobile/embedded targets.
  • You need export code, validation checks, and deployable configuration snippets.
  • You want to add TorchScript export steps into CI/CD or MLOps pipelines.

Best practices

  • Prefer scripting for models with dynamic control flow and tracing for stable, static graphs.
  • Validate exported artifacts by running sanity inputs, shape checks, and unit inference tests.
  • Strip training-only components and register buffers/parameters before export.
  • Record and pin dependency versions and runtime environment in export metadata.
  • Add automated export and validation steps to CI to catch regressions early.

Example use cases

  • Convert a trained PyTorch model to a TorchScript module for low-latency serving.
  • Generate export code and a lightweight validation script to include in CI pipelines.
  • Resolve unsupported operator errors by suggesting alternative implementations or custom ops.
  • Produce a compact artifact for mobile deployment and guidance on runtime constraints.
  • Create reproducible export commands and environment metadata for production handoffs.

FAQ

Should I use torch.jit.trace or torch.jit.script?

Use torch.jit.script for models with Python control flow or data-dependent branches; use torch.jit.trace for stable, pure-tensor graphs where a representative input captures behavior.

How do I validate a TorchScript export?

Run inference on representative inputs, compare outputs to the original model within tolerances, check saved metadata, and run shape and dtype sanity checks.