Add PyTorch Image Classification EfficientNet-B0 Streaming (Right-fitting) ML pipeline

Author: Amar3tto

.github/workflows/beam_Inference_Python_Benchmarks_Dataflow.yml

@@ -92,6 +92,7 @@ jobs:
             ${{ github.workspace }}/.github/workflows/load-tests-pipeline-options/beam_Inference_Python_Benchmarks_Dataflow_Pytorch_Sentiment_Streaming_DistilBert_Base_Uncased.txt
             ${{ github.workspace }}/.github/workflows/load-tests-pipeline-options/beam_Inference_Python_Benchmarks_Dataflow_Pytorch_Sentiment_Batch_DistilBert_Base_Uncased.txt
             ${{ github.workspace }}/.github/workflows/load-tests-pipeline-options/beam_Inference_Python_Benchmarks_Dataflow_VLLM_Gemma_Batch.txt
+            ${{ github.workspace }}/.github/workflows/load-tests-pipeline-options/beam_Inference_Python_Benchmarks_Dataflow_Pytorch_Image_Classification_Rightfit.txt
       # The env variables are created and populated in the test-arguments-action as "<github.job>_test_arguments_<argument_file_paths_index>"
       - name: get current time
         run: echo "NOW_UTC=$(date '+%m%d%H%M%S' --utc)" >> $GITHUB_ENV
@@ -189,4 +190,15 @@ jobs:
             -Prunner=DataflowRunner \
             -PpythonVersion=3.10 \
             -PloadTest.requirementsTxtFile=apache_beam/ml/inference/torch_tests_requirements.txt \
-            '-PloadTest.args=${{ env.beam_Inference_Python_Benchmarks_Dataflow_test_arguments_5 }} --job_name=benchmark-tests-pytorch-imagenet-python-gpu-${{env.NOW_UTC}} --output=gs://temp-storage-for-end-to-end-tests/torch/result_resnet152_gpu-${{env.NOW_UTC}}.txt'
+            '-PloadTest.args=${{ env.beam_Inference_Python_Benchmarks_Dataflow_test_arguments_5 }} --job_name=benchmark-tests-pytorch-imagenet-python-gpu-${{env.NOW_UTC}} --output=gs://temp-storage-for-end-to-end-tests/torch/result_resnet152_gpu-${{env.NOW_UTC}}.txt'
+      - name: run PyTorch Image Classification EfficientNet-B0 Streaming (Right-fitting)
+        uses: ./.github/actions/gradle-command-self-hosted-action
+        timeout-minutes: 180
+        with:
+          gradle-command: :sdks:python:apache_beam:testing:load_tests:run
+          arguments: |
+            -PloadTest.mainClass=apache_beam.testing.benchmarks.inference.pytorch_imagenet_rightfit_benchmarks \
+            -Prunner=DataflowRunner \
+            -PpythonVersion=3.10 \
+            -PloadTest.requirementsTxtFile=apache_beam/ml/inference/pytorch_rightfit_requirements.txt \
+            '-PloadTest.args=${{ env.beam_Inference_Python_Benchmarks_Dataflow_test_arguments_9 }} --mode=streaming --job_name=benchmark-tests-pytorch-imagenet-rightfit-streaming-${{env.NOW_UTC}} --output_table=apache-beam-testing.beam_run_inference.result_torch_inference_imagenet_stream_rightfit' \

.github/workflows/load-tests-pipeline-options/beam_Inference_Python_Benchmarks_Dataflow_Pytorch_Image_Classification_Rightfit.txt

@@ -0,0 +1,46 @@
+#  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.
+
+--region=us-central1
+--machine_type=n1-standard-4
+--num_workers=50
+--disk_size_gb=50
+--autoscaling_algorithm=NONE
+--staging_location=gs://temp-storage-for-perf-tests/loadtests
+--temp_location=gs://temp-storage-for-perf-tests/loadtests
+--requirements_file=apache_beam/ml/inference/pytorch_rightfit_requirements.txt
+--publish_to_big_query=true
+--metrics_dataset=beam_run_inference
+--metrics_table=torch_inference_imagenet_results_stream_rightfit
+--influx_measurement=torch_inference_imagenet_stream_rightfit
+--pretrained_model_name=efficientnet_b0
+--device=GPU
+--input_file=gs://apache-beam-ml/testing/inputs/openimage_50k_benchmark.txt
+--runner=DataflowRunner
+--mode=streaming
+--input_mode=gcs_uris
+--input_options={}
+--pubsub_topic=projects/apache-beam-testing/topics/images_topic
+--pubsub_subscription=projects/apache-beam-testing/subscriptions/images_subscription
+--model_state_dict_path=gs://apache-beam-ml/models/efficientnet_b0_state_dict.pth
+--rate_limit=250
+--image_size=224
+--top_k=5
+--inference_batch_size=auto
+--window_sec=60
+--trigger_proc_time_sec=30
+--enable_dedup=false
+--experiments=worker_accelerator=type:nvidia-tesla-t4;count:1;install-nvidia-driver:5xx

.test-infra/tools/refresh_looker_metrics.py

@@ -43,6 +43,7 @@
     ("82", ["263", "264", "265", "266", "267"]),  # PyTorch Sentiment Streaming DistilBERT base uncased
     ("85", ["268", "269", "270", "271", "272"]),  # PyTorch Sentiment Batch DistilBERT base uncased
     ("86", ["284", "285", "286", "287", "288"]),  # VLLM Batch Gemma
+    ("92", ["289", "290", "291", "292", "293"]),  # PyTorch Image Classification EfficientNet-B0 Streaming (Right-fitting)
 ]