pytorch/ignite

Checkpoint stateful handlers and metrics

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#966 opened on Apr 22, 2020

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enhancementhelp wantedmodule: metrics

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Description

🚀 Feature

Things that are attached to the Engine might have state that would ideally be checkpointed and restored using as part of the Engine's state_dict.

An example is a Checkpoint handler when it has a score_function. Currently, the priorities the Checkpoint class stores is not saved anywhere. It is not able to recover gracefully from failure without manual intervention to parse the checkpoint path names, and directly setting the internals of the class.

Handlers and Metrics should have state_dict and load_state_dict methods (empty by default), and I think it should be possible for these to automatically make it into/restored from the Engine's state_dict when it's attached to an Engine.

Contributor guide

Research direction
Implement `state dict` and `load state dict` methods for handlers and metrics, then integrate them into `Engine`'s `state dict` so that handler state is preserved across checkpoints.
Tech stack
pythonpytorch
Domain
machine learningbackend
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonPyTorchGit
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
60