Prettify code

Author: krystianfranus

configs/inference/config.yaml

@@ -12,5 +12,5 @@ tags: ["${now:%Y-%m-%d}"]
 data_processing_task_id: null
 training_task_id: null
 model_type: "simple_mlp"  # simple_mlp, simple_mf
-num_workers: 8
+num_workers: 11
 pin_memory: true

configs/training/experiment/mf_with_history.yaml

@@ -5,7 +5,7 @@ defaults:
   - override /model: mf.yaml
 
 datamodule:
-  num_workers: 7
+  num_workers: 11
   pin_memory: true
 
 model:
@@ -16,7 +16,7 @@ model:
 trainer:
   accelerator: "gpu"
   devices: 1
-  max_epochs: 2
+  max_epochs: 5
 
 callbacks:
   early_stopping:

configs/training/experiment/mf_wo_history.yaml

@@ -5,7 +5,7 @@ defaults:
   - override /model: mf.yaml
 
 datamodule:
-  num_workers: 7
+  num_workers: 11
   pin_memory: true
 
 model:
@@ -16,7 +16,7 @@ model:
 trainer:
   accelerator: "gpu"
   devices: 1
-  max_epochs: 2
+  max_epochs: 5
 
 callbacks:
   early_stopping:

configs/training/experiment/mlp_with_history.yaml

@@ -5,7 +5,7 @@ defaults:
   - override /model: mlp
 
 datamodule:
-  num_workers: 7
+  num_workers: 11
   pin_memory: true
 
 model:
@@ -20,7 +20,7 @@ model:
 trainer:
   accelerator: "gpu"
   devices: 1
-  max_epochs: 2
+  max_epochs: 5
 
 callbacks:
   early_stopping:

configs/training/experiment/mlp_wo_history.yaml

@@ -5,7 +5,7 @@ defaults:
   - override /model: mlp
 
 datamodule:
-  num_workers: 7
+  num_workers: 11
   pin_memory: true
 
 model:
@@ -20,7 +20,7 @@ model:
 trainer:
   accelerator: "gpu"
   devices: 1
-  max_epochs: 2
+  max_epochs: 5
 
 callbacks:
   early_stopping:

steps/optimize_hparams.py

@@ -31,17 +31,17 @@ def main():
             DiscreteParameterRange(
                 "Hydra/model.weight_decay", values=[0.01, 0.05, 0.1]
             ),
-            DiscreteParameterRange("Hydra/trainer.max_epochs", values=[100]),
+            DiscreteParameterRange("Hydra/trainer.max_epochs", values=[2]),
         ],
-        objective_metric_title="val",
-        objective_metric_series="auroc",
+        objective_metric_title="auroc",
+        objective_metric_series="val",
         objective_metric_sign="max_global",
         optimizer_class=OptimizerOptuna,
         max_number_of_concurrent_tasks=1,
         save_top_k_tasks_only=-1,
         total_max_jobs=10,
-        min_iteration_per_job=10 * 857,
-        max_iteration_per_job=50 * 857,
+        min_iteration_per_job=2 * 857,
+        max_iteration_per_job=2 * 857,
         # execution_queue="default",
         spawn_project="MyProjectHPO",
     )

steps/run_pipeline.py

@@ -21,7 +21,7 @@ def main():
         cache_executed_step=True,
         parents=["DataProcessing"],
         parameter_override={
-            "Hydra/prev_task_id": "${DataProcessing.id}"
+            "Hydra/data_processing_task_id": "${DataProcessing.id}"
         },  # It is preferable
         # parameter_override={
         #     "Args/overrides": "['prev_task_id=${DataProcessing.id}']"
@@ -34,17 +34,20 @@ def main():
         base_task_name="Training",
         cache_executed_step=True,
         parents=["DataProcessing"],
-        parameter_override={"Hydra/prev_task_id": "${DataProcessing.id}"},
+        parameter_override={"Hydra/data_processing_task_id": "${DataProcessing.id}"},
     )
 
-    # pipe.add_step(
-    #     name="Inference",
-    #     base_task_project="MyProject",
-    #     base_task_name="Inference",
-    #     cache_executed_step=True,
-    #     parents=["Training"],
-    #     parameter_override={"Hydra/prev_task_id": "${Training.id}"},
-    # )
+    pipe.add_step(
+        name="Inference",
+        base_task_project="MyProject",
+        base_task_name="Inference",
+        cache_executed_step=True,
+        parents=["DataProcessing", "Training"],
+        parameter_override={
+            "Hydra/data_processing_task_id": "${DataProcessing.id}",
+            "Hydra/training_task_id": "${Training.id}",
+        },
+    )
 
     # for debugging purposes use local jobs
     # pipe.start_locally(run_pipeline_steps_locally=False)