Fix slow tests (#831)

tests/integration/test_bayesian_optimization.py

@@ -494,9 +494,6 @@ def test_bayesian_optimizer_with_dgp_finds_minima_of_simple_quadratic(
 
 @random_seed
 @pytest.mark.slow
-@pytest.mark.parametrize(
-    "dtype", [pytest.param(tf.float64, id="float64"), pytest.param(tf.float32, id="float32")]
-)
 @pytest.mark.parametrize(
     "num_steps, acquisition_rule",
     [
@@ -517,7 +514,6 @@ def test_bayesian_optimizer_with_dgp_finds_minima_of_simple_quadratic(
     ],
 )
 def test_bayesian_optimizer_with_deep_ensemble_finds_minima_of_scaled_branin(
-    dtype: tf.DType,
     num_steps: int,
     acquisition_rule: Callable[[], AcquisitionRule[TensorType, SearchSpace, DeepEnsemble]],
 ) -> None:
@@ -527,7 +523,6 @@ def test_bayesian_optimizer_with_deep_ensemble_finds_minima_of_scaled_branin(
         acquisition_rule(),
         optimize_branin=True,
         model_args={"bootstrap": True, "diversify": False},
-        single_precision=dtype == tf.float32,
     )
 
 

trieste/models/gpflow/builders.py

@@ -34,7 +34,10 @@
 from ...types import TensorType
 from ..gpflow.models import GaussianProcessRegression
 
-KERNEL_LENGTHSCALE = tf.constant(0.2, dtype=gpflow.default_float())
+# NOTE: As a static non-Tensor, this should really be a tf.constant (like the other constants).
+# However, changing it breaks serialisation during the expected_improvement.pct.py notebook.
+# See https://github.com/secondmind-labs/trieste/issues/833 for details.
+KERNEL_LENGTHSCALE = tf.cast(0.2, dtype=gpflow.default_float())
 """
 Default value of the kernel lengthscale parameter.
 """