diff --git a/aimodel/slurm-TEST-deeplabv3p-rainfall.job b/aimodel/slurm-TEST-deeplabv3p-rainfall.job
index 0fc97ce..0552a2e 100755
--- a/aimodel/slurm-TEST-deeplabv3p-rainfall.job
+++ b/aimodel/slurm-TEST-deeplabv3p-rainfall.job
@@ -42,6 +42,7 @@ show_help() {
 	echo -e "    PATH_CHECKPOINT 	The path to a checkcpoint to load. If specified, a model will be loaded instead of being trained." >&2;
 	echo -e "    LEARNING_RATE      The learning rate to use. Default: 0.001." >&2;
 	echo -e "    UPSAMPLE           How much to upsample by at the beginning of the model. A value of disables upscaling. Default: 2." >&2;
+	echo -e "    STEPS_PER_EXECUTION How much to upsample by at the beginning of the model. A value of disables upscaling. Default: 2." >&2;
 	echo -e "    PREDICT_COUNT		The number of items from the (SCRAMBLED) dataset to make a prediction for." >&2;
 	echo -e "    POSTFIX			Postfix to append to the output dir (auto calculated)." >&2;
 	echo -e "    ARGS				Optional. Any additional arguments to pass to the python program." >&2;
@@ -72,7 +73,7 @@ echo -e ">>> DIR_OUTPUT: ${DIR_OUTPUT}";
 echo -e ">>> Additional args: ${ARGS}";
 
 export PATH=$HOME/software/bin:$PATH;
-export IMAGE_SIZE BATCH_SIZE DIR_RAINFALLWATER PATH_HEIGHTMAP PATH_COLOURMAP STEPS_PER_EPOCH DIR_OUTPUT PATH_CHECKPOINT EPOCHS PREDICT_COUNT NO_REMOVE_ISOLATED_PIXELS LOSS LEARNING_RATE DICE_LOG_COSH WATER_THRESHOLD;
+export IMAGE_SIZE BATCH_SIZE DIR_RAINFALLWATER PATH_HEIGHTMAP PATH_COLOURMAP STEPS_PER_EPOCH DIR_OUTPUT PATH_CHECKPOINT EPOCHS PREDICT_COUNT NO_REMOVE_ISOLATED_PIXELS LOSS LEARNING_RATE DICE_LOG_COSH WATER_THRESHOLD UPSAMPLE STEPS_PER_EXECUTION;
 
 echo ">>> Installing requirements";
 conda run -n py38 pip install -q -r requirements.txt;
diff --git a/aimodel/src/deeplabv3_plus_test_rainfall.py b/aimodel/src/deeplabv3_plus_test_rainfall.py
index b8c7100..4fcfc02 100755
--- a/aimodel/src/deeplabv3_plus_test_rainfall.py
+++ b/aimodel/src/deeplabv3_plus_test_rainfall.py
@@ -52,6 +52,7 @@ LEARNING_RATE = float(os.environ["LEARNING_RATE"]) if "LEARNING_RATE" in os.envi
 WATER_THRESHOLD = float(os.environ["WATER_THRESHOLD"]) if "WATER_THRESHOLD" in os.environ else 0.1
 UPSAMPLE = int(os.environ["UPSAMPLE"]) if "UPSAMPLE" in os.environ else 2
 
+STEPS_PER_EXECUTION = int(os.environ["STEPS_PER_EXECUTION"]) if "STEPS_PER_EXECUTION" in os.environ else 16
 DIR_OUTPUT=os.environ["DIR_OUTPUT"] if "DIR_OUTPUT" in os.environ else f"output/{datetime.utcnow().date().isoformat()}_deeplabv3plus_rainfall_TEST"
 
 PATH_CHECKPOINT = os.environ["PATH_CHECKPOINT"] if "PATH_CHECKPOINT" in os.environ else None
@@ -227,6 +228,7 @@ if PATH_CHECKPOINT is None:
 			specificity # How many true negatives were accurately predicted?
 			# TODO: Add IoU, F1, Precision, Recall,  here. 
 		],
+		steps_per_execution=STEPS_PER_EXECUTION
 	)
 	logger.info(">>> Beginning training")
 	history = model.fit(dataset_train,