Migrate to GPSampler from skopt for bayesianoptimization

.github/workflows/publish-algorithm-images.yaml

@@ -26,8 +26,6 @@ jobs:
             dockerfile: cmd/suggestion/hyperopt/v1beta1/Dockerfile
           - component-name: suggestion-hyperband
             dockerfile: cmd/suggestion/hyperband/v1beta1/Dockerfile
-          - component-name: suggestion-skopt
-            dockerfile: cmd/suggestion/skopt/v1beta1/Dockerfile
           - component-name: suggestion-goptuna
             dockerfile: cmd/suggestion/goptuna/v1beta1/Dockerfile
           - component-name: suggestion-optuna

.github/workflows/test-python.yaml

@@ -25,23 +25,3 @@ jobs:
 
       - name: Run Python test
         run: make pytest
-
-  # The skopt service doesn't work appropriately with Python 3.11.
-  # So, we need to run the test with Python 3.9.
-  # TODO (tenzen-y): Once we stop to support skopt, we can remove this test.
-  # REF: https://github.com/kubeflow/katib/issues/2280
-  test-skopt:
-    name: Test Skopt
-    runs-on: ubuntu-22.04
-
-    steps:
-      - name: Check out code
-        uses: actions/checkout@v4
-
-      - name: Setup Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: 3.9
-
-      - name: Run Python test
-        run: make pytest-skopt

Makefile

@@ -173,7 +173,7 @@ endif
 # TODO(Electronic-Waste): Remove the import rewrite when protobuf supports `python_package` option.
 # REF: https://github.com/protocolbuffers/protobuf/issues/7061
 pytest: prepare-pytest prepare-pytest-testdata
-	pytest ./test/unit/v1beta1/suggestion --ignore=./test/unit/v1beta1/suggestion/test_skopt_service.py
+	pytest ./test/unit/v1beta1/suggestion
 	pytest ./test/unit/v1beta1/earlystopping
 	pytest ./test/unit/v1beta1/metricscollector
 	cp ./pkg/apis/manager/v1beta1/python/api_pb2.py ./sdk/python/v1beta1/kubeflow/katib/katib_api_pb2.py
@@ -182,12 +182,3 @@ pytest: prepare-pytest prepare-pytest-testdata
 	pytest ./sdk/python/v1beta1/kubeflow/katib
 	rm ./sdk/python/v1beta1/kubeflow/katib/katib_api_pb2.py ./sdk/python/v1beta1/kubeflow/katib/katib_api_pb2_grpc.py
 
-# The skopt service doesn't work appropriately with Python 3.11.
-# So, we need to run the test with Python 3.9.
-# TODO (tenzen-y): Once we stop to support skopt, we can remove this test.
-# REF: https://github.com/kubeflow/katib/issues/2280
-pytest-skopt:
-	pip install six
-	pip install --prefer-binary -r test/unit/v1beta1/requirements.txt
-	pip install --prefer-binary -r cmd/suggestion/skopt/v1beta1/requirements.txt
-	pytest ./test/unit/v1beta1/suggestion/test_skopt_service.py

cmd/suggestion/optuna/v1beta1/requirements.txt

@@ -1,4 +1,7 @@
 grpcio>=1.64.1
 protobuf>=4.21.12,<5
 googleapis-common-protos==1.53.0
-optuna==3.3.0
+optuna==3.6.0
+torch==2.5.1
+scipy==1.15.1
+cmaes==0.11.1

docs/images-location.md

@@ -140,17 +140,6 @@ and the [Katib Early Stopping algorithms](https://www.kubeflow.org/docs/componen
         <a href="https://github.com/kubeflow/katib/blob/master/cmd/suggestion/hyperopt/v1beta1/Dockerfile">Dockerfile</a>
       </td>
     </tr>
-    <tr align="center">
-      <td>
-        <code>docker.io/kubeflowkatib/suggestion-skopt</code>
-      </td>
-      <td>
-        <a href="https://github.com/scikit-optimize/scikit-optimize">Skopt</a> Suggestion
-      </td>
-      <td>
-        <a href="https://github.com/kubeflow/katib/blob/master/cmd/suggestion/skopt/v1beta1/Dockerfile">Dockerfile</a>
-      </td>
-    </tr>
     <tr align="center">
       <td>
         <code>docker.io/kubeflowkatib/suggestion-optuna</code>

manifests/v1beta1/installs/katib-cert-manager/katib-config.yaml

@@ -31,7 +31,7 @@ runtime:
     - algorithmName: hyperband
       image: docker.io/kubeflowkatib/suggestion-hyperband:latest
     - algorithmName: bayesianoptimization
-      image: docker.io/kubeflowkatib/suggestion-skopt:latest
+      image: docker.io/kubeflowkatib/suggestion-optuan:latest
     - algorithmName: cmaes
       image: docker.io/kubeflowkatib/suggestion-goptuna:latest
     - algorithmName: sobol

manifests/v1beta1/installs/katib-external-db/katib-config.yaml

@@ -33,7 +33,7 @@ runtime:
     - algorithmName: hyperband
       image: docker.io/kubeflowkatib/suggestion-hyperband:latest
     - algorithmName: bayesianoptimization
-      image: docker.io/kubeflowkatib/suggestion-skopt:latest
+      image: docker.io/kubeflowkatib/suggestion-optuna:latest
     - algorithmName: cmaes
       image: docker.io/kubeflowkatib/suggestion-goptuna:latest
     - algorithmName: sobol

manifests/v1beta1/installs/katib-leader-election/katib-config.yaml

@@ -34,7 +34,7 @@ runtime:
     - algorithmName: hyperband
       image: docker.io/kubeflowkatib/suggestion-hyperband:latest
     - algorithmName: bayesianoptimization
-      image: docker.io/kubeflowkatib/suggestion-skopt:latest
+      image: docker.io/kubeflowkatib/suggestion-goptuna:latest
     - algorithmName: cmaes
       image: docker.io/kubeflowkatib/suggestion-goptuna:latest
     - algorithmName: sobol

manifests/v1beta1/installs/katib-openshift/katib-config.yaml

@@ -31,7 +31,7 @@ runtime:
     - algorithmName: hyperband
       image: docker.io/kubeflowkatib/suggestion-hyperband:latest
     - algorithmName: bayesianoptimization
-      image: docker.io/kubeflowkatib/suggestion-skopt:latest
+      image: docker.io/kubeflowkatib/suggestion-optuna:latest
     - algorithmName: cmaes
       image: docker.io/kubeflowkatib/suggestion-goptuna:latest
     - algorithmName: sobol

manifests/v1beta1/installs/katib-standalone-postgres/katib-config.yaml

@@ -33,7 +33,7 @@ runtime:
     - algorithmName: hyperband
       image: docker.io/kubeflowkatib/suggestion-hyperband:latest
     - algorithmName: bayesianoptimization
-      image: docker.io/kubeflowkatib/suggestion-skopt:latest
+      image: docker.io/kubeflowkatib/suggestion-optuna:latest
     - algorithmName: cmaes
       image: docker.io/kubeflowkatib/suggestion-goptuna:latest
     - algorithmName: sobol

manifests/v1beta1/installs/katib-standalone/katib-config.yaml

@@ -33,7 +33,7 @@ runtime:
     - algorithmName: hyperband
       image: docker.io/kubeflowkatib/suggestion-hyperband:latest
     - algorithmName: bayesianoptimization
-      image: docker.io/kubeflowkatib/suggestion-skopt:latest
+      image: docker.io/kubeflowkatib/suggestion-optuna:latest
     - algorithmName: cmaes
       image: docker.io/kubeflowkatib/suggestion-goptuna:latest
     - algorithmName: sobol

pkg/suggestion/v1beta1/optuna/base_service.py

@@ -59,6 +59,9 @@ def _create_sampler(self):
             )
             return optuna.samplers.GridSampler(combinations, **self.algorithm_config)
 
+        elif self.algorithm_name == "bayesianoptimization":
+            return optuna.samplers.GPSampler(**self.algorithm_config)
+
     def get_suggestions(self, trials, current_request_number):
         if len(trials) != 0:
             self._tell(trials)

pkg/suggestion/v1beta1/optuna/service.py

@@ -94,6 +94,10 @@ class OptimizerConfiguration(object):
         "grid": {
             "seed": lambda x: int(x),
         },
+        "bayesianoptimization": {
+            "n_startup_trials": lambda x: int(x),
+            "seed": lambda x: int(x),
+        },
     }
 
     @classmethod
@@ -132,6 +136,8 @@ def validate_algorithm_spec(cls, experiment):
             return cls._validate_random_setting(algorithm_settings)
         elif algorithm_name == "grid":
             return cls._validate_grid_setting(experiment)
+        elif algorithm_name == "bayesianoptimization":
+            return cls._validate_bayesianoptimization_setting(algorithm_settings)
         else:
             return False, "unknown algorithm name {}".format(algorithm_name)
 
@@ -259,3 +265,27 @@ def _validate_grid_setting(cls, experiment):
             )
 
         return True, ""
+
+    @classmethod
+    def _validate_bayesianoptimization_setting(cls, algorithm_settings):
+        for s in algorithm_settings:
+            try:
+                if s.name in ["n_startup_trials", "random_state"]:
+                    if not int(s.value) >= 0:
+                        return False, "{} should be greate or equal than zero".format(
+                            s.name
+                        )
+                else:
+                    return (
+                        False,
+                        "unknown setting {} for algorithm bayesianoptimization".format(
+                            s.name
+                        ),
+                    )
+
+            except Exception as e:
+                return False, "failed to validate {name}({value}): {exception}".format(
+                    name=s.name, value=s.value, exception=e
+                )
+
+        return True, ""