From da70fbe2fa0df813f84c2fc03a85a6e3a3f88063 Mon Sep 17 00:00:00 2001 From: Toshihiko Yanase Date: Fri, 10 Nov 2023 15:02:48 +0900 Subject: [PATCH] Update URL. --- docs/tutorials/preferential-optimization.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/tutorials/preferential-optimization.rst b/docs/tutorials/preferential-optimization.rst index ae6623831..ab7ec6540 100644 --- a/docs/tutorials/preferential-optimization.rst +++ b/docs/tutorials/preferential-optimization.rst @@ -99,7 +99,7 @@ enabling the Optuna Dashboard to display images on the evaluation feedback page. register_preference_feedback_component(study, "artifact", user_attr_key) Following this, we create a loop that continuously checks if new trials should be generated, awaiting human evaluation if not. -Within the while loop, new trials are generated if the condition :meth:`~optuna_dashboard.preferential.PreferentialStudy.should_generate` returns ``True``. +Within the while loop, new trials are generated if the condition :meth:`~optuna_dashboard.preferential.PreferentialStudy.should_generate` returns ``True``. For each trial, RGB values are sampled, an image is generated with these values, saved temporarily. Then the image is uploaded to the artifact store, and finally, the ``artifact_id`` is stored to the key, which is specified via :func:`~optuna_dashboard.register_preference_feedback_component`. @@ -127,4 +127,4 @@ Then the image is uploaded to the artifact store, and finally, the ``artifact_id artifact_id = upload_artifact(trial, image_path, artifact_store) trial.set_user_attr(user_attr_key, artifact_id) -.. _generator.py: https://github.com/optuna/optuna-dashboard/blob/main/examples/preferential-optimization/generator.py +.. _generator.py: https://github.com/optuna/optuna-examples/blob/main/dashboard/preferential-optimization/generator.py