Comments (5)
Hi @glenn-jocher -- currently working on this! Fix should be in shortly.
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Great!
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Hi @glenn-jocher -- so I think this should be fixed by 10b3a9f, which removes our dependency on simplejson. We'll be pushing a new release at the end of the week with this fix, or else in the meantime you can try building off master. Hope this takes care of it for you!
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@ldworkin thanks for the update! I recently tried this again. This time there is no error upon plotting, but now no plots show up in the jupyter notebook. I saved the outputs here so you could reproduce:
https://colab.research.google.com/drive/1P6TvA9UZDtLf9dMFTcYWm_RUBW0wpsiV#scrollTo=DMsfBROgGSBK
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Hi @glenn-jocher! Ah yes, we just discovered this is a problem -- see issue #94. We will have a fix for this next week, but in the meantime, you can workaround it by using vanilla Jupyter (not Jupyter Labs). The plots should all work without any issues there.
Will close the task now since the original issue is fixed in our current release, and you can follow #94 for updates on this other issue.
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Related Issues (20)
- Safe optimization in the Service API HOT 5
- The same point is evaluated multiple times during Integer Optimization with BO. HOT 5
- ax_client.generation_strategy.trials_as_df HOT 9
- Managing Objective Function Evaluation Failures in Ax for MOO HOT 6
- [GENERAL SUPPORT]: Managing Objective Function Evaluation Failures in Ax for MOO HOT 3
- [GENERAL SUPPORT]: Using qNegIntegratedPosteriorVariance HOT 3
- [GENERAL SUPPORT]: Reference point for multi-objective bayesian optimization HOT 4
- [GENERAL SUPPORT]: Adjusting search space or accommodating out-of-bounds initial data HOT 19
- [GENERAL SUPPORT]: Manual configuration, HOT 1
- [Bug]: Custom metric issue HOT 4
- [GENERAL SUPPORT]: CI_Level Paretofrontier
- [Bug]: Large sample time increase in ax-platform >= version 0.3.5 HOT 6
- [GENERAL SUPPORT]: Reference Point for Multi-Objective Bayesian Optimization HOT 5
- [GENERAL SUPPORT]: Plotting Pareto fronts / Posterior mean model HOT 6
- [GENERAL SUPPORT]: Getting best predicted point of a botorch model HOT 10
- [GENERAL SUPPORT]: Logical-or in outcome constraints HOT 4
- [GENERAL SUPPORT]: Parallelism and arbitrary parameter type support. HOT 5
- [GENERAL SUPPORT]: How to Standardize and Normalize Data for Service API HOT 6
- [FEATURE REQUEST]: Implement ImprovementGlobalStoppingStrategy for batch trials HOT 2
- [GENERAL SUPPORT]: Plot observed pareto frontier alongside remaining Arms HOT 2
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