{"categories":[{"label":"Distributed Computing","url":"https://skillfed.io/packages/category/system-distributed-computing/3"}],"enrichment":{"capability":"Provides decorators for Metaflow workflows to save and resume checkpoints, manage models, and load external models from Hugging Face Hub, enabling long-running jobs to recover from failures without losing progress.","skillfed_tags":["metaflow-extension","checkpoint-recovery","ml-workflow"],"use_cases":["Resume a multi-day machine learning training job after a hardware failure or timeout without restarting from scratch.","Cache and version Hugging Face models across a team to avoid redundant downloads and ensure everyone uses the same model snapshot.","Save intermediate data checkpoints in a data processing pipeline so failed steps can retry from the last known good state.","Isolate checkpoints per user in a shared Metaflow environment to prevent accidental overwrites during concurrent runs.","Track model lineage and versions automatically as part of a Metaflow flow without manual checkpoint file management."],"what_it_does":"Metaflow Checkpoint is an experimental extension for Metaflow that adds three decorators\u2014`@checkpoint`, `@model`, and `@huggingface_hub`\u2014to simplify state management and model handling in long-running data processing and machine learning workflows. The `@checkpoint` decorator saves and restores step state, allowing jobs to resume from the last checkpoint after failures without manual intervention. The `@model` decorator provides a straightforward way to save and load models created within a flow, while `@huggingface_hub` enables efficient loading and caching of large models from Hugging Face Hub. All three decorators handle user isolation and version control in multi-user environments, preventing conflicts and ensuring reproducibility.\n\nThe package has no runtime dependencies and installs with low friction. It is actively maintained but carries an experimental label, meaning the API and behavior may still evolve. The license treatment is unclear, so users should verify licensing terms before adoption. The package is positioned for workflows where interruptions are costly\u2014training jobs that run for hours or days, data pipelines that process large datasets, and scenarios where collaboration requires clear model and checkpoint versioning.","worth_installing":"Yes, if you run long-running Metaflow workflows and need checkpoint recovery and model versioning. The low install friction and active maintenance are favorable, but verify the license before use and be aware of the experimental status\u2014test thoroughly in non-critical workflows first. Not necessary if your jobs are short-lived or you already have external checkpoint infrastructure."},"id":"metaflow-checkpoint","links":{"html":"https://skillfed.io/packages/metaflow-checkpoint","md":"https://skillfed.io/packages/metaflow-checkpoint.md","pypi":"https://pypi.org/project/metaflow-checkpoint/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":null,"license_treatment":"unclear","name":"metaflow-checkpoint","python_support":"unspecified","summary":"An EXPERIMENTAL checkpoint decorator for Metaflow"},"popularity":{"monthly_downloads":96244,"position":13222,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.2.11"}