diff --git a/examples/Serving-Ranking-Models-With-Merlin-Systems.ipynb b/examples/Serving-Ranking-Models-With-Merlin-Systems.ipynb index ddbb137d9..47c445180 100644 --- a/examples/Serving-Ranking-Models-With-Merlin-Systems.ipynb +++ b/examples/Serving-Ranking-Models-With-Merlin-Systems.ipynb @@ -1026,8 +1026,8 @@ "\n", "# read in data for request\n", "batch = df_lib.read_parquet(\n", - " os.path.join(original_data_path,\"valid\", \"part.0.parquet\"), num_rows=3, columns=workflow.input_schema.column_names\n", - ")\n", + " os.path.join(original_data_path,\"valid\", \"part.0.parquet\"), columns=workflow.input_schema.column_names\n", + ").head(3)\n", "batch" ] }, diff --git a/tests/integration/examples/test_serving_ranking_models_with_merlin_systems.py b/tests/integration/examples/test_serving_ranking_models_with_merlin_systems.py index d16ab0a0c..2cdbde419 100644 --- a/tests/integration/examples/test_serving_ranking_models_with_merlin_systems.py +++ b/tests/integration/examples/test_serving_ranking_models_with_merlin_systems.py @@ -1,7 +1,6 @@ import os import pytest - from testbook import testbook from tests.conftest import REPO_ROOT @@ -97,9 +96,8 @@ def test_example_04_exporting_ranking_models(tb): # read in data for request batch = df_lib.read_parquet( os.path.join("/tmp/data/", "valid", "part.0.parquet"), - num_rows=3, columns=workflow.input_schema.column_names, - ) + ).head(3) batch = batch.drop(columns="click") outputs = tb.ref("output_cols") from merlin.dataloader.tf_utils import configure_tensorflow diff --git a/tests/integration/t4r/test_pytorch_backend.py b/tests/integration/t4r/test_pytorch_backend.py index 07d962c65..fe16f05a7 100644 --- a/tests/integration/t4r/test_pytorch_backend.py +++ b/tests/integration/t4r/test_pytorch_backend.py @@ -34,7 +34,6 @@ def test_serve_t4r_with_torchscript(tmpdir): - # =========================================== # Generate training data # =========================================== @@ -107,7 +106,7 @@ def test_serve_t4r_with_torchscript(tmpdir): if name in input_schema.column_names: dtype = input_schema[name].dtype - df_cols[name] = tensor.cpu().numpy().astype(dtype) + df_cols[name] = tensor.cpu().numpy().astype(dtype.name) if len(tensor.shape) > 1: df_cols[name] = list(df_cols[name])