Ax1.0

.github/workflows/unix-noax.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.10', 3.11, 3.12]
+        python-version: [3.11, 3.12]
 
     steps:
     - uses: actions/checkout@v4
@@ -28,12 +28,11 @@ jobs:
     - shell: bash -l {0}
       name: Install dependencies
       run: |
-        conda install -c conda-forge pytorch-cpu
-        conda install -c pytorch numpy pandas
         conda install -c conda-forge mpi4py mpich
+        pip install torch --index-url https://download.pytorch.org/whl/cpu
         pip install .[test]
-        pip install git+https://github.com/campa-consortium/gest-api.git
-        pip install git+https://github.com/xopt-org/xopt.git
+        pip install gest-api
+        pip install xopt
         pip uninstall --yes ax-platform # Run without Ax
     - shell: bash -l {0}
       name: Run unit tests without Ax

.github/workflows/unix-openmpi.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.10', 3.11, 3.12]
+        python-version: [3.11, 3.12]
 
     steps:
     - uses: actions/checkout@v4
@@ -28,12 +28,11 @@ jobs:
     - shell: bash -l {0}
       name: Install dependencies
       run: |
-        conda install -c conda-forge "numpy<2.4" "pandas<3"
-        conda install -c conda-forge pytorch-cpu
         conda install -c conda-forge mpi4py openmpi=5.*
+        pip install torch --index-url https://download.pytorch.org/whl/cpu
         pip install .[test]
-        pip install git+https://github.com/campa-consortium/gest-api.git
-        pip install --upgrade-strategy=only-if-needed git+https://github.com/xopt-org/xopt.git
+        pip install gest-api
+        pip install xopt
     - shell: bash -l {0}
       name: Run unit tests with openMPI
       run: |

.github/workflows/unix.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.10', 3.11, 3.12]
+        python-version: [3.11, 3.12]
 
     steps:
     - uses: actions/checkout@v4
@@ -28,12 +28,11 @@ jobs:
     - shell: bash -l {0}
       name: Install dependencies
       run: |
-        conda install -c conda-forge "numpy<2.4" "pandas<3"
-        conda install -c conda-forge pytorch-cpu
         conda install -c conda-forge mpi4py mpich
+        pip install torch --index-url https://download.pytorch.org/whl/cpu
         pip install .[test]
-        pip install git+https://github.com/campa-consortium/gest-api.git
-        pip install --upgrade-strategy=only-if-needed git+https://github.com/xopt-org/xopt.git
+        pip install gest-api
+        pip install xopt
     - shell: bash -l {0}
       name: Run unit tests with MPICH
       run: |

doc/environment.yaml

@@ -5,13 +5,13 @@ dependencies:
   - pip
   - pip:
     - -e ..
-    - ax-platform >= 0.5.0, < 1.0.0
+    - ax-platform >=1.0.0
     - autodoc_pydantic >= 2.0.1
     - ipykernel
     - matplotlib
     - nbsphinx
     - numpydoc
-    - git+https://github.com/campa-consortium/gest-api.git
+    - gest-api
     - pydata-sphinx-theme
     - sphinx-copybutton
     - sphinx-design

optimas/generators/ax/developer/ax_metric.py

@@ -1,7 +1,7 @@
 """Contains the definition of the Ax metric used for multitask optimization."""
 
 import pandas as pd
-from ax import Metric
+from ax.core.metric import Metric
 from ax.core.batch_trial import BatchTrial
 from ax.core.data import Data
 from ax.utils.common.result import Ok

optimas/generators/ax/developer/multitask.py

@@ -16,45 +16,33 @@
 from ax.core.optimization_config import OptimizationConfig
 from ax.core.objective import Objective as AxObjective
 from ax.runners import SyntheticRunner
-from ax.modelbridge.factory import get_sobol
-from ax.modelbridge.torch import TorchModelBridge
+from ax.adapter.factory import get_sobol
+from ax.adapter.torch import TorchAdapter
 from ax.core.observation import ObservationFeatures
 from ax.core.generator_run import GeneratorRun
 from ax.storage.json_store.save import save_experiment
 from ax.storage.metric_registry import register_metrics
 
-from ax.modelbridge.registry import Models, ST_MTGP_trans
-
-try:
-    # For Ax >= 0.5.0
-    from ax.modelbridge.transforms.derelativize import Derelativize
-    from ax.modelbridge.transforms.convert_metric_names import (
-        ConvertMetricNames,
-    )
-    from ax.modelbridge.transforms.trial_as_task import TrialAsTask
-    from ax.modelbridge.transforms.stratified_standardize_y import (
-        StratifiedStandardizeY,
-    )
-    from ax.modelbridge.transforms.task_encode import TaskChoiceToIntTaskChoice
-    from ax.modelbridge.registry import MBM_X_trans
-
-    MT_MTGP_trans = MBM_X_trans + [
-        Derelativize,
-        ConvertMetricNames,
-        TrialAsTask,
-        StratifiedStandardizeY,
-        TaskChoiceToIntTaskChoice,
-    ]
+from ax.adapter.registry import Generators, MBM_X_trans
+from ax.adapter.transforms.derelativize import Derelativize
+from ax.adapter.transforms.metrics_as_task import MetricsAsTask
+from ax.adapter.transforms.trial_as_task import TrialAsTask
+from ax.adapter.transforms.stratified_standardize_y import (
+    StratifiedStandardizeY,
+)
+from ax.adapter.transforms.task_encode import TaskChoiceToIntTaskChoice
+from ax.adapter.registry import ST_MTGP_trans
 
-except ImportError:
-    # For Ax < 0.5.0
-    from ax.modelbridge.registry import MT_MTGP_trans
+MT_MTGP_trans = MBM_X_trans + [
+    Derelativize,
+    MetricsAsTask,
+    TrialAsTask,
+    StratifiedStandardizeY,
+    TaskChoiceToIntTaskChoice,
+]
 
 from ax.core.experiment import Experiment
 from ax.core.data import Data
-from ax.modelbridge.transforms.convert_metric_names import (
-    tconfig_from_mt_experiment,
-)
 
 from optimas.generators.ax.base import AxGenerator
 from optimas.core import (
@@ -81,7 +69,7 @@ def get_MTGP(
     trial_index: Optional[int] = None,
     device: torch.device = torch.device("cpu"),
     dtype: torch.dtype = torch.double,
-) -> TorchModelBridge:
+) -> TorchAdapter:
     """Instantiate a Multi-task Gaussian Process (MTGP) model.
 
     Points are generated with EI (Expected Improvement).
@@ -94,11 +82,21 @@ def get_MTGP(
             t.index: t.trial_type for t in experiment.trials.values()
         }
         transforms = MT_MTGP_trans
+
+        # Build MetricsAsTask config manually (replaces tconfig_from_mt_experiment)
+        canonical = experiment._metric_to_canonical_name
+        metric_task_map = {}
+        for metric_name, canonical_name in canonical.items():
+            if metric_name != canonical_name:
+                if canonical_name not in metric_task_map:
+                    metric_task_map[canonical_name] = []
+                metric_task_map[canonical_name].append(metric_name)
+
         transform_configs = {
             "TrialAsTask": {
                 "trial_level_map": {"trial_type": trial_index_to_type}
             },
-            "ConvertMetricNames": tconfig_from_mt_experiment(experiment),
+            "MetricsAsTask": {"metric_task_map": metric_task_map},
         }
     else:
         # Set transforms for a Single-type MTGP model.
@@ -125,7 +123,7 @@ def get_MTGP(
         )
 
     return assert_is_instance(
-        Models.ST_MTGP(
+        Generators.ST_MTGP(
             experiment=experiment,
             search_space=search_space or experiment.search_space,
             data=data,
@@ -135,7 +133,7 @@ def get_MTGP(
             torch_device=device,
             status_quo_features=status_quo_features,
         ),
-        TorchModelBridge,
+        TorchAdapter,
     )
 
 
@@ -354,7 +352,6 @@ def _incorporate_external_data(self, trials: List[Trial]) -> None:
                 arms.append(
                     Arm(parameters=params, name=param_to_name[arm.signature])
                 )
-                # self._next_id += 1
 
             # Create new batch trial.
             gr = GeneratorRun(arms=arms, weights=[1.0] * len(arms))
@@ -597,7 +594,7 @@ def _save_model_to_file(self) -> None:
 
 
 def max_utility_from_GP(
-    n: int, m: TorchModelBridge, gr: GeneratorRun, hifi_task: str
+    n: int, m: TorchAdapter, gr: GeneratorRun, hifi_task: str
 ) -> GeneratorRun:
     """Select the max utility points according to the MTGP predictions.
 

optimas/generators/ax/import_error_dummy_generator.py

@@ -12,5 +12,5 @@ def __init__(self, *args, **kwargs) -> None:
         raise RuntimeError(
             "You need to install ax-platform, in order "
             "to use Ax-based generators in optimas.\n"
-            "e.g. with `pip install 'ax-platform<1.0.0'`"
+            "e.g. with `pip install 'ax-platform'`"
         )

optimas/generators/ax/service/ax_client.py

@@ -134,8 +134,9 @@ def _create_ax_client(self) -> AxClient:
 
     def _use_cuda(self, ax_client: AxClient):
         """Determine whether the AxClient uses CUDA."""
-        for step in ax_client.generation_strategy._steps:
-            if "torch_device" in step.model_kwargs:
-                if step.model_kwargs["torch_device"] == "cuda":
-                    return True
+        for node in ax_client.generation_strategy._nodes:
+            for gs in node.generator_specs:
+                if "torch_device" in gs.generator_kwargs:
+                    if gs.generator_kwargs["torch_device"] == "cuda":
+                        return True
         return False

optimas/generators/ax/service/base.py

@@ -10,13 +10,11 @@
     ObjectiveProperties,
     FixedFeatures,
 )
-from ax.modelbridge.registry import Models
-from ax.modelbridge.generation_strategy import (
-    GenerationStep,
-    GenerationStrategy,
-)
-from ax.modelbridge.transition_criterion import MaxTrials, MinTrials
-from ax import Arm
+from ax.adapter.registry import Generators
+from ax.generation_strategy.generation_node import GenerationStep
+from ax.generation_strategy.generation_strategy import GenerationStrategy
+from ax.generation_strategy.transition_criterion import MinTrials
+from ax.core.arm import Arm
 
 from optimas.core import (
     Trial,
@@ -262,15 +260,19 @@ def _insert_unknown_trial(self, trial: Trial) -> None:
         # initialization trials, but only if they have not failed.
         if trial.completed and not self._enforce_n_init:
             generation_strategy = self._ax_client.generation_strategy
-            current_step = generation_strategy.current_step
+            current_node = generation_strategy.current_node
             # Reduce only if there are still Sobol trials left.
-            if current_step.model == Models.SOBOL:
-                for tc in current_step.transition_criteria:
-                    # Looping over all criterial makes sure we reduce
+            is_sobol = any(
+                gs.generator_enum == Generators.SOBOL
+                for gs in current_node.generator_specs
+            )
+            if is_sobol:
+                for tc in current_node.transition_criteria:
+                    # Looping over all criteria makes sure we reduce
                     # the transition thresholds due to `_n_init`
                     # (i.e., max trials) and `min_trials_observed=1` (
                     # i.e., min trials).
-                    if isinstance(tc, (MinTrials, MaxTrials)):
+                    if isinstance(tc, MinTrials):
                         tc.threshold -= 1
                 generation_strategy._maybe_transition_to_next_node()
         return ax_trial
@@ -296,13 +298,11 @@ def _complete_trial(self, ax_trial_index: int, trial: Trial) -> None:
     def _create_ax_client(self) -> AxClient:
         """Create Ax client."""
         bo_model_kwargs = {
-            "torch_dtype": torch.double,
             "torch_device": torch.device(self.torch_device),
-            "fit_out_of_design": self._fit_out_of_design,
         }
         ax_client = AxClient(
             generation_strategy=GenerationStrategy(
-                self._create_generation_steps(bo_model_kwargs)
+                nodes=self._create_generation_steps(bo_model_kwargs)
             ),
             verbose_logging=False,
         )
@@ -339,7 +339,7 @@ def _create_sobol_step(self) -> GenerationStep:
         # This also allows the generator to work well when
         # `sim_workers` > `n_init`.
         return GenerationStep(
-            model=Models.SOBOL,
+            generator=Generators.SOBOL,
             num_trials=self._n_init,
             min_trials_observed=1,
             enforce_num_trials=False,
@@ -366,8 +366,8 @@ def _update_parameter(self, parameter):
         # Delete the fitted model from the generation strategy, otherwise
         # the parameter won't be updated.
         generation_strategy = self._ax_client.generation_strategy
-        if generation_strategy._model is not None:
-            del generation_strategy._curr.model_spec._fitted_model
+        if generation_strategy._curr.generator_spec._fitted_adapter is not None:
+            generation_strategy._curr.generator_spec._fitted_adapter = None
         parameters = self._create_ax_parameters()
         new_search_space = InstantiationBase.make_search_space(parameters, None)
         self._ax_client.experiment.search_space.update_parameter(