playbooks

home / skills / jeremylongshore / claude-code-plugins-plus-skills / torchserve-config-generator

torchserve-config-generator skill

/skills/08-ml-deployment/torchserve-config-generator

This skill helps you generate production-ready TorchServe configurations and best-practice deployment guidelines, accelerating ML model serving and deployment

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill torchserve-config-generator

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

Files (1)
SKILL.md
2.2 KB
---
name: "torchserve-config-generator"
description: |
  Generate torchserve config generator operations. Auto-activating skill for ML Deployment.
  Triggers on: torchserve config generator, torchserve config generator
  Part of the ML Deployment skill category. Use when configuring systems or services. Trigger with phrases like "torchserve config generator", "torchserve generator", "torchserve".
allowed-tools: "Read, Write, Edit, Bash(cmd:*), Grep"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# Torchserve Config Generator

## Overview

This skill provides automated assistance for torchserve config generator tasks within the ML Deployment domain.

## When to Use

This skill activates automatically when you:
- Mention "torchserve config generator" in your request
- Ask about torchserve config generator 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 torchserve config generator
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 torchserve config generator"
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 generation of TorchServe configurations and deployment operations for model serving. It produces production-ready config files, step-by-step guidance, and validation checks to help you deploy PyTorch models with TorchServe reliably. Use it to speed up model packaging, configuration tuning, and environment validation for inference services.

How this skill works

The skill inspects your model metadata, desired inference options (batching, worker count, autoscaling hints), and environment constraints to generate TorchServe config files and related artifacts. It outputs model-archive commands, config.properties, logging and metrics settings, and validation results against common TorchServe patterns. It also provides deployment steps and troubleshooting tips tailored to the inputs you provide.

When to use it

  • Preparing config.properties and model-archive commands for TorchServe deployments
  • Automating resource and worker tuning for inference throughput and latency
  • Validating TorchServe configs against common errors before production rollout
  • Generating monitoring, logging, and metrics settings for observability
  • Adapting configs for different environments (single node, container, or k8s)

Best practices

  • Start with accurate model metadata (handler, version, requirements) to ensure correct .mar creation
  • Tune batch_size and number_of_netty_threads based on real load tests, not just defaults
  • Enable model-level metrics and structured logging for observability in production
  • Use environment-specific overrides (dev/staging/prod) rather than editing a single config in place
  • Validate generated configs with a dry run to catch missing fields or incompatible settings

Example use cases

  • Generate config.properties and .mar packaging commands for a new ResNet model
  • Create a TorchServe profile optimized for low-latency real-time inference
  • Produce Kubernetes-ready TorchServe configurations and container run commands
  • Validate a submitted config to detect missing handler or incorrect checkpoint paths
  • Recommend worker and batch settings based on target QPS and latency SLOs

FAQ

What inputs do you need to generate a config?

Provide model metadata (handler, model file path, requirements), target throughput/latency goals, and environment type (local, container, k8s).

Can this skill validate configs before deployment?

Yes — it checks for common errors like missing fields, invalid paths, and incompatible settings, and provides corrective suggestions.