feat(dataset api): (1.6/n) fix all iterrows callsites (#1660)

# What does this PR do?
- as title

[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])

## Test Plan
CI

```
pytest -v -s --nbval-lax ./docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb
```
<img width="587" alt="image"
src="https://github.com/user-attachments/assets/4a25f493-501e-43f4-9836-d9802223a93a"
/>


[//]: # (## Documentation)

llama_stack/distribution/ui/page/evaluations/native_eval.py

@@ -166,11 +166,10 @@ def run_evaluation_3():
     eval_candidate = st.session_state["eval_candidate"]
 
     dataset_id = benchmarks[selected_benchmark].dataset_id
-    rows = llama_stack_api.client.datasetio.iterrows(
+    rows = llama_stack_api.client.datasets.iterrows(
         dataset_id=dataset_id,
-        rows_in_page=-1,
     )
-    total_rows = len(rows.rows)
+    total_rows = len(rows.data)
     # Add number of examples control
     num_rows = st.number_input(
         "Number of Examples to Evaluate",
@@ -195,7 +194,7 @@ def run_evaluation_3():
     if st.button("Run Evaluation"):
         progress_text = "Running evaluation..."
         progress_bar = st.progress(0, text=progress_text)
-        rows = rows.rows
+        rows = rows.data
         if num_rows < total_rows:
             rows = rows[:num_rows]
 

llama_stack/providers/inline/eval/meta_reference/eval.py

@@ -92,11 +92,11 @@ class MetaReferenceEvalImpl(
         validate_dataset_schema(dataset_def.dataset_schema, get_valid_schemas(Api.eval.value))
         all_rows = await self.datasetio_api.iterrows(
             dataset_id=dataset_id,
-            rows_in_page=(-1 if benchmark_config.num_examples is None else benchmark_config.num_examples),
+            limit=(-1 if benchmark_config.num_examples is None else benchmark_config.num_examples),
         )
         res = await self.evaluate_rows(
             benchmark_id=benchmark_id,
-            input_rows=all_rows.rows,
+            input_rows=all_rows.data,
             scoring_functions=scoring_functions,
             benchmark_config=benchmark_config,
         )

llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py

@@ -330,11 +330,11 @@ class LoraFinetuningSingleDevice:
         async def fetch_rows(dataset_id: str):
             return await self.datasetio_api.iterrows(
                 dataset_id=dataset_id,
-                rows_in_page=-1,
+                limit=-1,
             )
 
         all_rows = await fetch_rows(dataset_id)
-        rows = all_rows.rows
+        rows = all_rows.data
 
         await validate_input_dataset_schema(
             datasets_api=self.datasets_api,

llama_stack/providers/inline/scoring/basic/scoring.py

@@ -86,10 +86,10 @@ class BasicScoringImpl(
 
         all_rows = await self.datasetio_api.iterrows(
             dataset_id=dataset_id,
-            rows_in_page=-1,
+            limit=-1,
         )
         res = await self.score(
-            input_rows=all_rows.rows,
+            input_rows=all_rows.data,
             scoring_functions=scoring_functions,
         )
         if save_results_dataset:

llama_stack/providers/inline/scoring/braintrust/braintrust.py

@@ -169,9 +169,9 @@ class BraintrustScoringImpl(
 
         all_rows = await self.datasetio_api.iterrows(
             dataset_id=dataset_id,
-            rows_in_page=-1,
+            limit=-1,
         )
-        res = await self.score(input_rows=all_rows.rows, scoring_functions=scoring_functions)
+        res = await self.score(input_rows=all_rows.data, scoring_functions=scoring_functions)
         if save_results_dataset:
             # TODO: persist and register dataset on to server for reading
             # self.datasets_api.register_dataset()

llama_stack/providers/inline/scoring/llm_as_judge/scoring.py

@@ -74,10 +74,10 @@ class LlmAsJudgeScoringImpl(
 
         all_rows = await self.datasetio_api.iterrows(
             dataset_id=dataset_id,
-            rows_in_page=-1,
+            limit=-1,
         )
         res = await self.score(
-            input_rows=all_rows.rows,
+            input_rows=all_rows.data,
             scoring_functions=scoring_functions,
         )
         if save_results_dataset: