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https://issues.apache.org/jira/browse/BEAM-14068?focusedWorklogId=771575&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-771575
]
ASF GitHub Bot logged work on BEAM-14068:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 17/May/22 20:42
Start Date: 17/May/22 20:42
Worklog Time Spent: 10m
Work Description: AnandInguva commented on code in PR #17462:
URL: https://github.com/apache/beam/pull/17462#discussion_r875245170
##########
sdks/python/apache_beam/ml/inference/pytorch_it_test.py:
##########
@@ -0,0 +1,99 @@
+#
+# 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.
+#
+
+# pylint: skip-file
+
+"""End-to-End test for Pytorch Inference"""
+
+import logging
+import unittest
+import uuid
+
+import pytest
+
+from apache_beam.io.filesystems import FileSystems
+from apache_beam.testing.test_pipeline import TestPipeline
+
+try:
+ import torch
+ from apache_beam.ml.inference.examples import pytorch_image_classification
+except ImportError as e:
+ torch = None
+
+_EXPECTED_OUTPUTS = {
Review Comment:
that's one way to do it. But these are only 15 samples. So thats why it was
like this. But once the sample size is more, I would be in more favor of your
approach.
Issue Time Tracking
-------------------
Worklog Id: (was: 771575)
Time Spent: 3h 50m (was: 3h 40m)
> RunInference Benchmarking tests
> -------------------------------
>
> Key: BEAM-14068
> URL: https://issues.apache.org/jira/browse/BEAM-14068
> Project: Beam
> Issue Type: Sub-task
> Components: sdk-py-core
> Reporter: Anand Inguva
> Assignee: Anand Inguva
> Priority: P2
> Time Spent: 3h 50m
> Remaining Estimate: 0h
>
> RunInference benchmarks will evaluate performance of Pipelines, which
> represent common use cases of Beam + Dataflow in Pytorch, sklearn and
> possibly TFX. These benchmarks would be the integration tests that exercise
> several software components using Beam, PyTorch, Scikit learn and TensorFlow
> extended.
> we would use the datasets that's available publicly (Eg; Kaggle).
> Size: small / 10 GB / 1 TB etc
> The default execution runner would be Dataflow unless specified otherwise.
> These tests would be run very less frequently(every release cycle).
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