More integration tests info (#5319)

.github/workflows/integration-runner.yml

@@ -141,8 +141,8 @@ jobs:
         id: create_comment
         uses: KeisukeYamashita/create-comment@v1
         with:
-          # if triggered by PR, use PR number, otherwise use 5077 as fallback issue number for manual triggers
-          number: ${{ github.event_name == 'pull_request' && github.event.pull_request.number || 5077 }}
+          # if triggered by PR, use PR number, otherwise use 5318 as fallback issue number for manual triggers
+          number: ${{ github.event_name == 'pull_request' && github.event.pull_request.number || 5318 }}
           unique: false
           comment: |
               Trigger by: ${{ github.event_name == 'pull_request' && format('Pull Request (integration-test label on PR #{0})', github.event.pull_request.number) || (github.event_name == 'workflow_dispatch' && format('Manual Trigger: {0}', github.event.inputs.reason)) || 'Nightly Scheduled Run' }}
@@ -155,4 +155,4 @@ jobs:
               DeepSeek LLM Test Results:
               ${{ env.INTEGRATION_TEST_REPORT_DEEPSEEK }}
               ---
-              Download evaluation outputs (includes both Haiku and DeepSeek results): [Download](${{ steps.upload_results_artifact.outputs.artifact-url }})
+              Download testing outputs (includes both Haiku and DeepSeek results): [Download](${{ steps.upload_results_artifact.outputs.artifact-url }})

evaluation/integration_tests/run_infer.py

@@ -218,6 +218,8 @@ if __name__ == '__main__':
     )
 
     df = pd.read_json(output_file, lines=True, orient='records')
+
+    # record success and reason for failure for the final report
     df['success'] = df['test_result'].apply(lambda x: x['success'])
     df['reason'] = df['test_result'].apply(lambda x: x['reason'])
     logger.info('-' * 100)
@@ -231,9 +233,16 @@ if __name__ == '__main__':
     )
     logger.info('-' * 100)
 
+    # record cost for each instance, with 3 decimal places
+    df['cost'] = df['metrics'].apply(lambda x: round(x['accumulated_cost'], 3))
+    logger.info(f'Total cost: USD {df["cost"].sum():.2f}')
+
     report_file = os.path.join(metadata.eval_output_dir, 'report.md')
     with open(report_file, 'w') as f:
         f.write(
             f'Success rate: {df["success"].mean():.2%} ({df["success"].sum()}/{len(df)})\n'
         )
-        f.write(df[['instance_id', 'success', 'reason']].to_markdown(index=False))
+        f.write(f'\nTotal cost: USD {df["cost"].sum():.2f}\n')
+        f.write(
+            df[['instance_id', 'success', 'reason', 'cost']].to_markdown(index=False)
+        )