From 607c534dea781354e944b08940465442b1eac728 Mon Sep 17 00:00:00 2001 From: Dave Berenbaum Date: Wed, 16 Aug 2023 14:02:25 -0400 Subject: [PATCH] dvclive: copy best lightning checkpoint (#4768) --- .../dvclive/ml-frameworks/pytorch-lightning.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/docs/dvclive/ml-frameworks/pytorch-lightning.md b/content/docs/dvclive/ml-frameworks/pytorch-lightning.md index af2fa5e08a..29ba5949c4 100644 --- a/content/docs/dvclive/ml-frameworks/pytorch-lightning.md +++ b/content/docs/dvclive/ml-frameworks/pytorch-lightning.md @@ -66,13 +66,12 @@ checkpointing at all as described in the created by [`ModelCheckpoint`]. See [Log model checkpoints](#log-model-checkpoints). - - if `log_model == 'all'`, checkpoints are logged during training. + - if `log_model == False` (default), no checkpoint is logged. - if `log_model == True`, checkpoints are logged at the end of training, - except when `save_top_k == -1` which also logs every checkpoint during - training. + except when `save_top_k == -1` which logs every checkpoint during training. - - if `log_model == False` (default), no checkpoint is logged. + - if `log_model == 'all'`, checkpoints are logged during training. - `experiment` - (`None` by default) - [`Live`](/doc/dvclive/live) object to be used instead of initializing a new one. @@ -85,9 +84,10 @@ checkpointing at all as described in the ### Log model checkpoints Use `log_model` to save the checkpoints (it will use `Live.log_artifact()` -internally to save those). At the end of training, DVCLive will annotate the -[`best_model_path`][`ModelCheckpoint`] with name `best` (for example, to be -consumed in [Studio model registry] or automation scenarios). +internally to save those). At the end of training, DVCLive will copy the +[`best_model_path`][`ModelCheckpoint`] to the `dvclive/artifacts` directory and +annotate it with name `best` (for example, to be consumed in [Studio model +registry] or automation scenarios). - Save updates to the checkpoints directory at the end of training: