1.3.0

Compatibility

• Require BoTorch==0.18.0 (#5216)
  • Requires PyTorch>=2.4.0
  • Utilizes NumPyro for faster fully-Bayesian model fitting, requiring JAX as a dependency
• Storage: Add step_size to SQAParameter class, and as a column to the parameter_v2 table in the DB (#5212).

API Changes (ax/api)

• Client.attach_data (and therefore Client.complete_trial) now auto-registers metrics in raw_data that are not part of
  the optimization config as tracking metrics, matching the documented contract. Previously this raised UserInputError (#5135)
• Client.configure_optimization / objective and outcome-constraint strings now support linear equality constraints (w^T x == b) (#5174)

Docs

• New example starting an Ax experiment from dataframe data (#5098)

Core (ax/core)

• Objective and OutcomeConstraint are now expression-based and no longer hold Metric instances;
  metrics live only on the Experiment. Construct them with an expression string, e.g.
  Objective(expression="ne1 + 2*ne2") or OutcomeConstraint(expression="qps >= 7000")
  (single, scalarized, and multi-objective are all expressed this way). Old-style constructors still
  work but discard the Metric after init. Objective.metric is removed -- get a metric's name from
  the expression and look it up via Experiment.get_metric(name). This is a step toward deprecating
  ScalarizedObjective, MultiObjective, ScalarizedOutcomeConstraint, and ObjectiveThreshold (#5000, #5122, #5045, #5041)
  Note: We are aware of some edge-case bugs in this setup and we may be iterating on this in future releases.
• ParameterConstraint gains a new equality= kwarg for linear equality constraints (w^T x == b); equality constraints are honored in SearchSpace membership checks (#5173, #5175)
• Make all mark_* methods on BaseTrial no-op when the status is unchanged, and avoid rewriting timestamps when the status is unchanged (#5097, #5074)
• Runner lifecycle for search space editing: new Runner.update and Runner.on_search_space_update methods, a typed RunnerConfig infrastructure, and an UNSET sentinel (#5131, #5132, #5133, #5130)
• Support for Language-in-the-Loop labeling trials, with data-freshness hashing/stamping and stale-data handling (#4986, #4992, #5144)
• AddExecutionViability transform (lives in adapter; relies on core trial/metric plumbing) (#4547)

Generation (ax/generators, ax/adapter, ax/generation_strategy)

• Replace Pyro with NumPyro (JAX-backed) for fully Bayesian NUTS inference -- roughly 25x faster fit on CPU with equivalent model quality (#5087)
• Threaded equality constraints from ParameterConstraint all the way down to BoTorch (#5176, #5177, #5179, #5178, #5182)
• Open-sourced the Transfer Learning stack: TransferLearningAdapter (registered as Generators.BOTL) (#5052, #5048, #5096, #5047)
• Heterogeneous TL now defaults to MultiTaskGP + LearnedFeatureImputation (ImputedMultiTaskGP) (#5106, #5183, #5193, #5192)
• Dispatch qEUBO for preferential BO (#5093) and support PairwiseGP in ModelList pipelines (#5092)
• GenerationStrategy.fit(experiment, data) public method encapsulating the transition-then-fit pattern (#4922)
• New InSampleUniformGenerator for model-free in-sample candidate selection (#4987)
• FreshLILOLabelCheck transition criterion and hash-based filtering of stale LILO data in the adapter (#4994, #4993)
• Expand the model space for numeric ordered ChoiceParameters (#5210)

Breaking

• Removed the deprecated steps= kwarg from GenerationStrategy.__init__ (use nodes=) (#5142)
• Removed the deprecated use_update argument to GenerationStep (#5187)
• Removed the deprecated generated_points argument to RandomGenerator (#5186)
• Re-index TorchOptConfig.objective_thresholds from (n_outcomes,) to (n_objectives,) (#5018)

Analysis (ax/analysis, ax/plot)

• New TransferLearningAnalysis with paste/diff comparison links (#4918, #4980)
• New UtilityRankingPlot for preference metrics; preference metrics handled across the analysis layer (#5166, #5165)
• New NotApplicableStateAnalysisCard (#5035, #5036, #5051, #5163)
• SensitivityAnalysisPlot aesthetic improvements; use total-order sensitivity for high-dimensional experiments (#5015, #5115)

Storage (ax/storage)

• Add step_size to SQAParameter class, and as a column to the parameter_v2 table in the DB (#5212). This will be leveraged in the next version.
• SQLAlchemy 2.0 migration: migrate SQA declarative classes to SA 2.0 Mapped[T], make the SQA store SA 2.0-compatible (#5205, #5201, #5169, #5203, #5206)
• Support equality constraints in JSON and SQA storage (#5181)

Legacy API (ax/service)

Note: These are deprecated and may be removed in a future release.

• optimize() is rewritten as a thin wrapper over ax.api.client.Client; the OptimizationLoop class is removed. The optimize() signature and return type are preserved (#5143)
• Removed AxClient.get_optimization_trace -- superseded by UtilityProgressionAnalysis (#5168)
• Search space editing on AxClient: new add_parameters, update_parameters, and disable_parameters methods to mutate the search space after trials have started; parameter constraints can be added alongside new parameters (#4945, #5023)

Orchestration (ax/orchestration)

• Removed Scheduler, SchedulerOptions, and SchedulerInternalError -- deprecated since Ax 1.0.0; use the Orchestrator instead (#5188)