Migrating from MrMTgui to ModelPie: Step-by-Step Transition Guide

Migrating from MrMTgui to ModelPie: Step-by-Step Transition GuideMigrating from MrMTgui to ModelPie involves more than a simple name change — ModelPie brings interface updates, workflow improvements, and compatibility adjustments that can affect how you run, manage, and interpret your models. This guide walks you through a complete, practical migration: assessing readiness, preparing your environment, converting projects and configs, testing and validating outputs, and adopting ModelPie’s newer features and best practices.

Why migrate to ModelPie?

• Improved stability and performance: ModelPie includes optimizations and bug fixes not present in MrMTgui.
• Modernized UI and UX: Cleaner layout, streamlined workflows, and accessibility improvements reduce friction.
• Enhanced extensibility: New plugin hooks and configuration options make it easier to customize pipelines.
• Active maintenance and feature roadmap: Continued updates and community support mean long-term viability.

Pre-migration planning

1. Inventory your current usage

   • List all MrMTgui projects, datasets, and scripts you rely on.
   • Note dependencies (Python versions, libraries, custom plugins).
   • Record any custom configurations, presets, or patched behavior.

2. Check ModelPie compatibility

   • Review ModelPie release notes and compatibility docs for breaking changes.
   • Identify deprecated features in MrMTgui and their ModelPie replacements.
   • Verify supported runtime (Python versions, OS, GPU drivers).

3. Prepare stakeholders and backup

   • Notify team members about the migration timeline and expected downtime.
   • Back up projects, configuration files, datasets, trained models, and logs.
   • Create a rollback plan in case critical issues arise.

Setup ModelPie environment

1. Create an isolated environment
   • Use virtualenv, venv, or conda to create a clean Python environment to avoid dependency conflicts.
   • Pin Python version to one supported by ModelPie.

Example (venv):

python3 -m venv modelpie-env source modelpie-env/bin/activate pip install --upgrade pip 

1. Install ModelPie and core dependencies

   • Install ModelPie via pip or the project’s recommended installer.

     
     pip install modelpie 

   • Install any optional extras you need (GPU support, visualization, plugin SDK).

2. Install and test required libraries

   • Reinstall libraries used by your MrMTgui projects into the new environment, respecting version constraints.

     
     pip install numpy pandas torch torchvision 

   • Verify GPU/tooling availability (CUDA/cuDNN versions as needed).

Migrate projects and configurations

1. Map configuration and file structure

   • Compare MrMTgui config schema to ModelPie’s. Create a mapping document of keys that changed, were removed, or were added.
   • Example mapping entries:
     • mrmtgui.config.model_path -> modelpie.model.file
     • mrmtgui.config.batch_size -> modelpie.runtime.batch

2. Convert configuration files

   • For simple key renames, use scripted transforms (sed, Python scripts). For complex schema changes, write a small migration script that reads old configs and outputs new ones.

     # simple example: convert old YAML keys to new ModelPie keys import yaml, sys with open('mrmtgui_config.yml') as f: old = yaml.safe_load(f) new = {} new['modelpie'] = {'model': {'file': old.get('model_path')},            'runtime': {'batch': old.get('batch_size', 1)}} with open('modelpie_config.yml', 'w') as f: yaml.safe_dump(new, f) 

3. Move project assets

   • Copy datasets, pretrained model files, and auxiliary assets into the new project layout expected by ModelPie.
   • Preserve file permissions and paths referenced in configs.

4. Update custom plugins and scripts

   • Review custom MrMTgui plugins and scripts. Replace deprecated APIs with ModelPie equivalents.
   • Recompile or refactor any native extensions if the ABI changed.
   • If ModelPie provides a plugin SDK, wrap your logic into the new plugin scaffold.

5. Handle checkpoints and model formats

   • Confirm that existing model checkpoints are supported. If not, export models to a supported format (ONNX, TorchScript, etc.) and re-import.
   • Validate that serialization (optimizers, epoch counters) remains intact or note what must be reinitialized.

Test migration with a controlled run

1. Create a minimal test case

   • Start with a small dataset and a simple model to validate end-to-end behavior.
   • Run the same seed/inputs in both MrMTgui and ModelPie and compare outputs numerically (within acceptable tolerances).

2. Verify performance and resource usage

   • Compare runtimes, memory consumption, and GPU utilization.
   • Look for regressions in throughput or latency.

3. Compare evaluation metrics

   • Ensure accuracy, loss curves, and other metrics remain consistent.
   • If results diverge, enable verbose logging and trace differences to configuration, preprocessing, or numerical precision changes.

4. Log and fix issues iteratively

   • Maintain a migration issues tracker (bug, severity, fix).
   • Triage configuration mismatches, API errors, and model loading problems.

Validate and certify migrated projects

1. Re-run full training/inference workflows
   • For mission-critical projects, run full-length training or large-scale inference to validate stability and performance.
2. Create acceptance criteria
   • Define pass/fail thresholds (e.g., within X% of baseline metrics, stable for Y hours).
3. Security and compliance checks
   • Ensure dependencies meet your security policies; run vulnerability scanners and license checks.
4. Peer review
   • Have team members review converted configs, scripts, and results.

Cutover and deployment

1. Staged rollout

   • Start with a canary deployment using ModelPie for a subset of traffic or jobs.
   • Monitor logs, metrics, and user feedback closely.

2. Automation and CI/CD updates

   • Update build scripts, CI pipelines, and deployment manifests to use the ModelPie environment and commands.
   • Add automated tests that validate the new setups in CI.

3. Update documentation and runbooks

   • Replace references to MrMTgui with ModelPie in internal docs, READMEs, and operational runbooks.
   • Document known differences and troubleshooting tips.

4. Decommission MrMTgui artifacts

   • After successful cutover and sufficient validation, archive or remove old MrMTgui environments and artifacts per your retention policy.

Post-migration: adopt ModelPie features and best practices

• Use ModelPie’s new plugin hooks to modularize custom logic.
• Migrate monitoring to ModelPie-native telemetry or integrate with your observability stack.
• Take advantage of improved experiment tracking and metadata features.
• Regularly update ModelPie and follow the project’s release notes for incremental improvements.

Troubleshooting common issues

• Model fails to load: check format compatibility; try exporting to ONNX or TorchScript and re-importing.
• Different numeric results: confirm identical preprocessing, seeds, and floating-point precision settings.
• Performance regressions: profile and compare GPU kernels, batch sizes, and mixed-precision settings.
• Missing plugin API: refactor using ModelPie’s plugin SDK; reach out to community plugins for examples.

Example quick migration checklist (summary)

• Backup MrMTgui projects, configs, and models
• Create isolated ModelPie environment
• Install ModelPie and dependencies
• Map and convert configuration files
• Migrate datasets and model files
• Update/refactor custom plugins
• Run controlled tests and compare outputs
• Run full validation and security scans
• Staged rollout and CI updates
• Update docs and decommission old artifacts

Migrating from MrMTgui to ModelPie is a manageable process with planning, testing, and staged rollout. With careful mapping of configuration, validation of model behavior, and adoption of ModelPie’s newer features, you can minimize disruption and gain the benefits of improved stability, extensibility, and ongoing support.