[DGL-Go] Inference for Node Prediction Pipeline (full & ns) (#4095)

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dglgo/recipes/nodepred_pubmed_sage.yaml

 # Accuracy across 10 runs: 0.7819 ± 0.003176
 version: 0.0.1
 pipeline_name: nodepred
+pipeline_mode: train
 device: cuda:0
 data:
   name: pubmed
@@ -23,5 +24,5 @@ general_pipeline:
     lr: 0.01
     weight_decay: 0.0005
   loss: CrossEntropyLoss
-  save_path: "model.pth"
+  save_path: "results"        # Directory to save the experiment results
   num_runs: 10                # Number of experiments to run

dglgo/tests/cfg.yml

 version: 0.0.1
-pipeline_name: nodepred
+pipeline:
+  name: nodepred
+  mode: train
 device: cpu
 data:
   name: cora

tests/go/test_pipeline.py

@@ -106,5 +106,13 @@ def test_recipe(recipe):
 def test_node_cora():
     os.system('dgl configure nodepred --data cora --model gcn')
     os.system('dgl train --cfg nodepred_cora_gcn.yaml')
-    assert os.path.exists('checkpoint.pth')
-    assert os.path.exists('model.pth')
+    assert os.path.exists('results')
+    assert os.path.exists('results/run_0.pth')
+    os.system('dgl configure-apply nodepred --cpt results/run_0.pth')
+    assert os.path.exists('apply_nodepred_cora_gcn.yaml')
+    os.system('dgl configure-apply nodepred --data cora --cpt results/run_0.pth --cfg apply.yaml')
+    assert os.path.exists('apply.yaml')
+    os.system('dgl apply --cfg apply.yaml')
+    assert os.path.exists('apply_results/output.csv')
+    os.system('dgl export --cfg apply.yaml --output apply.py')
+    assert os.path.exists('apply.py')