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A potential solution for #177 (still to be verified).
We return np.float which is a deprecated alias for np.float64. However, when we infer the input schemas in PredictTensorflow, continuously valued model inputs are np.float32, thus we get a schema mis-match when we chain QueryFeast into PredictTensorflow.
=============================== warnings summary ===============================
../../../../../usr/local/lib/python3.8/dist-packages/nvtabular/framework_utils/init.py:18
/usr/local/lib/python3.8/dist-packages/nvtabular/framework_utils/init.py:18: DeprecationWarning: The nvtabular.framework_utils module is being replaced by the Merlin Models library. Support for importing from nvtabular.framework_utils is deprecated, and will be removed in a future version. Please consider using the models and layers from Merlin Models instead.
warnings.warn(
tests/unit/examples/test_serving_ranking_models_with_merlin_systems.py: 1 warning
tests/unit/systems/test_export.py: 1 warning
tests/unit/systems/dag/test_op_runner.py: 4 warnings
tests/unit/systems/ops/nvtabular/test_op.py: 2 warnings
tests/unit/systems/ops/tf/test_ensemble.py: 2 warnings
/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.
warnings.warn(
tests/unit/systems/test_export.py::test_export_run_ensemble_triton[tensorflow-parquet]
/var/jenkins_home/workspace/merlin_systems/systems/merlin/systems/triton/export.py:304: UserWarning: Column x is being generated by NVTabular workflow but is unused in test_name_tf model
warnings.warn(
tests/unit/systems/test_export.py::test_export_run_ensemble_triton[tensorflow-parquet]
/var/jenkins_home/workspace/merlin_systems/systems/merlin/systems/triton/export.py:304: UserWarning: Column y is being generated by NVTabular workflow but is unused in test_name_tf model
warnings.warn(
tests/unit/systems/test_export.py::test_export_run_ensemble_triton[tensorflow-parquet]
/var/jenkins_home/workspace/merlin_systems/systems/merlin/systems/triton/export.py:304: UserWarning: Column id is being generated by NVTabular workflow but is unused in test_name_tf model
warnings.warn(
tests/unit/systems/ops/fil/test_forest.py::test_export_merlin_models
/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first
self.make_current()
tests/unit/systems/ops/fil/test_op.py::test_binary_classifier_default[sklearn_forest_classifier-get_model_params4]
tests/unit/systems/ops/fil/test_op.py::test_binary_classifier_with_proba[sklearn_forest_classifier-get_model_params4]
tests/unit/systems/ops/fil/test_op.py::test_multi_classifier[sklearn_forest_classifier-get_model_params4]
tests/unit/systems/ops/fil/test_op.py::test_regressor[sklearn_forest_regressor-get_model_params4]
tests/unit/systems/ops/fil/test_op.py::test_model_file[sklearn_forest_regressor-checkpoint.tl]
/usr/local/lib/python3.8/dist-packages/sklearn/utils/deprecation.py:103: FutureWarning: Attribute n_features_ was deprecated in version 1.0 and will be removed in 1.2. Use n_features_in_ instead.
warnings.warn(msg, category=FutureWarning)
tests/unit/systems/ops/implicit/test_op.py::test_reload_from_config[AlternatingLeastSquares]
/usr/local/lib/python3.8/dist-packages/implicit/utils.py:28: UserWarning: OpenBLAS detected. Its highly recommend to set the environment variable 'export OPENBLAS_NUM_THREADS=1' to disable its internal multithreading
warnings.warn(
tests/unit/systems/ops/torch/test_op.py::test_pytorch_op_serving[True-True]
/usr/local/lib/python3.8/dist-packages/torch/serialization.py:707: UserWarning: 'torch.load' received a zip file that looks like a TorchScript archive dispatching to 'torch.jit.load' (call 'torch.jit.load' directly to silence this warning)
warnings.warn("'torch.load' received a zip file that looks like a TorchScript archive"
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A potential solution for #177 (still to be verified).
We return
np.floatwhich is a deprecated alias fornp.float64. However, when we infer the input schemas inPredictTensorflow, continuously valued model inputs arenp.float32, thus we get a schema mis-match when we chain QueryFeast into PredictTensorflow.