Metaflow Checkpoint

Imagine running a machine learning training job or any data processing task that takes hours or even days to complete. In such scenarios, you don't want failures or collaboration complexities to force you to start over and lose all the progress made. This is where Metaflow's new decorators—@checkpoint, @model, and @huggingface_hub—come into play. These decorators are specifically designed to address these challenges by simplifying checkpointing, model management, and efficient loading of external models, ensuring that your long-running jobs can be resumed seamlessly after a failure and that models and checkpoints are properly versioned in multi-user environments.

This repository introduces three new decorators for Metaflow that address these challenges:

  • @checkpoint: Simplifies saving and reloading checkpoints within your Metaflow flows.
  • @huggingface_hub: Enables efficient loading and caching of large models from Hugging Face Hub.
  • @model: Allows for easy saving and loading of models created during your Metaflow flows.

Examples for these decorators can be found in this repository.

Features

@checkpoint Decorator

The @checkpoint decorator alleviates the pain points associated with saving and reloading the state of your program (a Metaflow @step) in Metaflow flows. It also handles version control in multi-user settings by isolating checkpoints per user and run. Whether it's a checkpoint created by a machine learning model or intermediate data required in case of crashes, this decorator simplifies state management and failure recovery.

  • Checkpointing: Save the state of your @step at designated points.
  • Seamless Recovery: Restart your job from the last checkpoint upon retries without any manual intervention.
  • User Isolation: Checkpoints are managed per user to prevent overwriting in collaborative environments.
  • Ease of Use: Minimal code changes required to implement checkpointing.

@huggingface_hub Decorator

The @huggingface_hub decorator allows you to load large models from Hugging Face Hub and cache them for increased performance benefits. It also ensures that models are versioned and managed appropriately in multi-user environments.

  • Efficient Model Loading: Load models on-the-fly from Hugging Face Hub.
  • Caching Mechanism: Cache models locally to avoid redundant downloads.
  • Version Control: Manages different versions of models to prevent conflicts.
  • Integration with Metaflow: Easily incorporate models across your Metaflow flows.

@model Decorator

The @model decorator provides a trivial way to save and load models/checkpoints created as part of your Metaflow flow.

  • Simplified Model Loading: Automatically load models based on references and identifiers created by decorators such as @model/@checkpoint/@huggingface_hub.
  • Model Identity: Associates a uniquie identity to models so that there is clear distinction between different versions making it easy to track their lineage.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.2.13)

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for oavdeev from gravatar.com oavdeev Avatar for valay-ob from gravatar.com valay-ob

Credits

Author: Valay Dave

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

metaflow_checkpoint-0.2.12.tar.gz (88.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

metaflow_checkpoint-0.2.12-py2.py3-none-any.whl (113.2 kB view details)

Uploaded Python 2Python 3

File details

Details for the file metaflow_checkpoint-0.2.12.tar.gz.

File metadata

  • Download URL: metaflow_checkpoint-0.2.12.tar.gz
  • Upload date:
  • Size: 88.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for metaflow_checkpoint-0.2.12.tar.gz
Algorithm Hash digest
SHA256 92af36359eb2db41482c921ad5b3e690cb629b1f80d2ad43e19b68697d0dc467
MD5 45aba7cbd497ae43c9df3d3a2d08e410
BLAKE2b-256 87b7ad13cf7cbb968707a7c32830b6dce6baa96ace569cf560e924bb40ac9636

See more details on using hashes here.

Provenance

The following attestation bundles were made for metaflow_checkpoint-0.2.12.tar.gz:

Publisher: publish.yaml on outerbounds/metaflow-checkpoint

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file metaflow_checkpoint-0.2.12-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for metaflow_checkpoint-0.2.12-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 7b78bb6a85b4fa00c6ca9aa2a73ed4375872297a09451009f909350250437402
MD5 b7853d50b1a9facc16dae0f4b9c71c6d
BLAKE2b-256 817620df04bf7e33d063b8aa60a961b6424a0e17fac32b78412419bc3cd22049

See more details on using hashes here.

Provenance

The following attestation bundles were made for metaflow_checkpoint-0.2.12-py2.py3-none-any.whl:

Publisher: publish.yaml on outerbounds/metaflow-checkpoint

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.2.13

2 files

This release

0.2.12 This release

2 files

0.2.11

2 files

0.2.10

2 files

0.2.8

2 files

0.2.7

2 files

0.2.6

2 files

0.2.5

2 files

0.2.4

2 files

0.2.3

2 files

0.2.2

2 files

0.2.1

2 files

0.2.0

2 files

0.1.9

2 files

Pre-release

0.1.8.1rc0

2 files

0.1.8

2 files

0.1.7

2 files

0.1.6

2 files

0.1.5

2 files

0.1.4

2 files

0.1.3

2 files

0.1.2

2 files

0.1.1

2 files

0.1.0

2 files

0.0.13

2 files

0.0.12

2 files

0.0.11

2 files

0.0.10

2 files

0.0.9

2 files

0.0.8

2 files

0.0.7

2 files

0.0.6

2 files

0.0.5

2 files

0.0.4

2 files

0.0.3

2 files

0.0.2

2 files

0.0.1

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page