do need sde in det configs

src/ml_downscaling_emulator/score_sde_pytorch/configs/deterministic/default_configs.py

@@ -44,7 +44,7 @@ def get_default_configs():
   training.n_epochs = 20
   training.snapshot_freq = 5
   training.eval_freq = 5000
-#   training.sde = ""
+  training.sde = ""
 
   # sampling
   config.sampling = sampling = ml_collections.ConfigDict()

src/ml_downscaling_emulator/score_sde_pytorch/configs/deterministic/ukcp_local_pr_debug.py

@@ -0,0 +1,42 @@
+# coding=utf-8
+# Copyright 2020 The Google Research Authors.
+# Modifications copyright 2024 Henry Addison
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""Debug config for training in a deterministic fashion."""
+
+from ml_downscaling_emulator.score_sde_pytorch.configs.deterministic.default_configs import get_default_configs
+
+def get_config():
+  config = get_default_configs()
+
+  # training
+  training = config.training
+  training.n_epochs = 2
+  training.snapshot_freq = 5
+  training.eval_freq = 100
+  training.log_freq = 50
+  training.batch_size = 2
+
+  # data
+  data = config.data
+  data.dataset_name = 'debug-sample'
+  data.time_inputs=True
+
+  # model
+  model = config.model
+  model.name = 'cunet'
+
+  return config

tests/smoke-test-det

@@ -0,0 +1,20 @@
+#! /usr/bin/env bash
+
+set -euo pipefail
+
+config_name="ukcp_local_pr_debug"
+
+workdir="output/test/deterministic/${config_name}/test-run"
+config_path="src/ml_downscaling_emulator/score_sde_pytorch/configs/deterministic/${config_name}.py"
+
+loc_spec_channels=0
+
+rm -rf ${workdir}
+WANDB_EXPERIMENT_NAME="test" python bin/main.py --workdir ${workdir} --config ${config_path} --mode train  --config.model.loc_spec_channels=${loc_spec_channels}
+
+num_samples=2
+eval_batch_size=32
+checkpoint="epoch_${epoch}"
+
+rm -rf "${workdir}/samples/${checkpoint}/${dataset}"
+python bin/predict.py ${workdir} --dataset ${dataset} --checkpoint ${checkpoint} --batch-size ${eval_batch_size} --num-samples ${num_samples} --ensemble-member 01