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https://issues.apache.org/jira/browse/BEAM-13983?focusedWorklogId=757433&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-757433
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ASF GitHub Bot logged work on BEAM-13983:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 15/Apr/22 16:13
Start Date: 15/Apr/22 16:13
Worklog Time Spent: 10m
Work Description: ryanthompson591 commented on code in PR #17368:
URL: https://github.com/apache/beam/pull/17368#discussion_r851355795
##########
sdks/python/apache_beam/ml/inference/sklearn_loader.py:
##########
@@ -0,0 +1,73 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import abc
+import enum
+import pickle
+import sys
+from dataclasses import dataclass
+from typing import Any
+from typing import Iterable
+from typing import List
+
+import joblib
+import numpy
+
+import apache_beam.ml.inference.api as api
+import apache_beam.ml.inference.base as base
+import sklearn_loader
+from apache_beam.io.filesystems import FileSystems
+
+
+class SerializationType(enum.Enum):
+ PICKLE = 1
+ JOBLIB = 2
+
+
+class SKLearnInferenceRunner(base.InferenceRunner):
+ def run_inference(self, batch: List[numpy.array],
+ model: Any) -> Iterable[numpy.array]:
+ # vectorize data for better performance
+ vectorized_batch = numpy.stack(batch, axis=0)
+ predictions = model.predict(vectorized_batch)
+ return [api.PredictionResult(x, y) for x, y in zip(batch, predictions)]
+
+ def get_num_bytes(self, batch: List[numpy.array]) -> int:
+ """Returns the number of bytes of data for a batch."""
+ return sum(sys.getsizeof(element) for element in batch)
Review Comment:
Yeah, you pretty much got it. There is a little overhead that getsizeof
measures, but this would be similar to what serialization does.
Issue Time Tracking
-------------------
Worklog Id: (was: 757433)
Time Spent: 2h 20m (was: 2h 10m)
> Implement RunInference for Scikit-learn
> ---------------------------------------
>
> Key: BEAM-13983
> URL: https://issues.apache.org/jira/browse/BEAM-13983
> Project: Beam
> Issue Type: Sub-task
> Components: sdk-py-core
> Reporter: Andy Ye
> Priority: P2
> Labels: run-inference
> Time Spent: 2h 20m
> Remaining Estimate: 0h
>
> Implement RunInference for Scikit-learn as described in the design doc
> [https://s.apache.org/inference-sklearn-pytorch]
> There will be a sklearn_impl.py file that contains SklearnModelLoader and
> SlkearnInferenceRunner classes.
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