reference/generated/torchrl.checkpoint.resume_config

Run in Google Colab

Colab

Download Notebook

Notebook

View on GitHub

GitHub

resume_config

torchrl.checkpoint.resume_config(cfg: DictConfig, checkpoint_path: str | Path, *, overrides: Sequence[str] | None = None) → DictConfig

Return the configuration of a run resumed from checkpoint_path.

The configuration saved with the checkpoint under its config component is the base and overrides are applied on top, so resume=<path> alone rebuilds the original run while resume=<path> collector.total_frames=... extends it. Interpolations survive because recipes save the configuration unresolved. Config-group overrides such as logger@logger=csv, deletions (~key) and bare flags cannot be applied to a saved configuration and are ignored with a warning. When the checkpoint holds no config component, cfg is returned unchanged with a warning.

Parameters:

• cfg (DictConfig) – the configuration composed for the current run.

• checkpoint_path (str or Path) – the checkpoint being resumed.

• overrides (Sequence[str], optional) – key=value overrides applied over the saved configuration. Defaults to the task overrides of the current Hydra run, or none outside a Hydra application.

Returns:

The configuration to run.

Examples

>>> import tempfile
>>> from omegaconf import OmegaConf  
>>> from torchrl.checkpoint import Checkpoint, resume_config
>>> saved = {"budget": 100, "trainer": {"total_frames": "${budget}"}}
>>> with tempfile.TemporaryDirectory() as tmpdir:  
...     path = Checkpoint(config=saved).save(f"{tmpdir}/checkpoint")
...     cfg = resume_config(
...         OmegaConf.create({"budget": 5}), path, overrides=["budget=200"]
...     )
>>> cfg.trainer.total_frames  
200