Bash script for training on multiple models

configs/models/dynedge_position_example.yml

@@ -35,7 +35,7 @@ arguments:
             arguments: {}
             class_name: MSELoss
         loss_weight: null
-        target_labels: ["position_x", "position_y", "position_y"]
+        target_labels: ["position_x", "position_y", "position_z"]
         transform_inference: null
         transform_prediction_and_target: null
         transform_support: null

examples/04_training/01_train_model.py

@@ -35,6 +35,7 @@ def main(
     batch_size: int,
     num_workers: int,
     prediction_names: Optional[List[str]],
+    suffix: Optional[str] = None,
 ) -> None:
     """Run example."""
     # Initialise Weights & Biases (W&B) run
@@ -62,7 +63,10 @@ def main(
         dataloader={"batch_size": batch_size, "num_workers": num_workers},
     )
 
-    archive = os.path.join(EXAMPLE_OUTPUT_DIR, "train_model")
+    if suffix is not None:
+        archive = os.path.join(EXAMPLE_OUTPUT_DIR, f"train_model_{suffix}")
+    else:
+        archive = os.path.join(EXAMPLE_OUTPUT_DIR, "train_model")
     run_name = "dynedge_{}_example".format("_".join(config.target))
 
     # Construct dataloaders
@@ -154,6 +158,13 @@ def main(
         default=None,
     )
 
+    parser.add_argument(
+        "--suffix",
+        type=str,
+        help="Name addition to folder (default: %(default)s)",
+        default=None,
+    )
+
     args = parser.parse_args()
 
     main(
@@ -165,4 +176,5 @@ def main(
         args.batch_size,
         args.num_workers,
         args.prediction_names,
+        args.suffix,
     )

examples/04_training/01_train_models.sh

@@ -0,0 +1,62 @@
+#!/bin/bash
+
+#### This script enables the user to run multiple trainings in sequence on the same database but for different model configs.
+# To execute this file, copy the file path and write in the terminal; $ bash <filepath>
+
+
+# execution of bash file in same directory as the script
+bash_directory=$(dirname -- "$(readlink -f "${BASH_SOURCE}")")
+
+## Global; applies to all models
+# path to dataset configuration file in the GraphNeT directory
+dataset_config=$(realpath "$bash_directory/../../configs/datasets/training_example_data_sqlite.yml")
+# what GPU to use; more information can be gained with the module nvitop
+gpus=0
+# the maximum number of epochs; if used, this greatly affect learning rate scheduling
+max_epochs=5
+# early stopping threshold
+early_stopping_patience=5
+# events in a batch
+batch_size=16
+# number of CPUs to use
+num_workers=2
+
+## Model dependent; applies to each model in sequence
+# path to model files in the GraphNeT directory
+model_directory=$(realpath "$bash_directory/../../configs/models")
+# list of model configurations to train
+declare -a model_configs=(
+    "${model_directory}/example_direction_reconstruction_model.yml"
+    "${model_directory}/example_energy_reconstruction_model.yml"
+    "${model_directory}/example_vertex_position_reconstruction_model.yml"
+)
+
+# suffix ending on the created directory
+declare -a suffixs=(
+    "direction"
+    "energy"
+    "position"
+)
+
+# prediction name outputs per model
+declare -a prediction_names=(
+    "zenith_pred zenith_kappa_pred azimuth_pred azimuth_kappa_pred"
+    "energy_pred"
+    "position_x_pred position_y_pred position_z_pred"
+)
+
+for i in "${!model_configs[@]}"; do
+    echo "training iteration ${i} on ${model_configs[$i]} with output variables ${prediction_names[i][@]}"
+    python ${bash_directory}/01_train_model.py \
+        --dataset-config ${dataset_config} \
+        --model-config ${model_configs[$i]} \
+        --gpus ${gpus} \
+        --max-epochs ${max_epochs} \
+        --early-stopping-patience ${early_stopping_patience} \
+        --batch-size ${batch_size} \
+        --num-workers ${num_workers} \
+        --prediction-names ${prediction_names[i][@]} \
+        --suffix ${suffixs[i]}
+    wait
+done
+echo "all trainings are done."