Comments (5)
The pip version is always the latest release and not meant to be up to date with the github master. You can find the tutorial for the current pip version here: https://ax.dev/versions/0.1.2/tutorials/tune_cnn.html
If you want to live on the bleeding edge and run the tutorial off master, you need to install Ax from the github master.
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@priyankclarifai, one thing to clarify, the current pip version is not 0.1.2, but is the "stable" version on the Ax site (0.1.3).
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Understood thanks a lot,
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To add onto this conversation, we do have a bug at the moment with the site where it's easy to be redirected to the latest tutorial (bleeding edge) even though you think you're looking at the stable tutorials. This can happen, for example, if you use the left-hand side nav on the tutorials page. We will fix this shortly.
In the meantime, please use the following link directly as the stable (0.1.3) tutorial: https://ax.dev/tutorials/tune_cnn.html.
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Website versioning is fixed now, closing out the issue.
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Related Issues (20)
- MOO not respecting nonlinear constraints HOT 1
- Problem with Fixed parameters if nonlinear_inequality_constraint is imposed HOT 1
- 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
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