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).
Imported from Jira BEAM-14068. Original Jira may contain additional context.
Reported by: Anand Inguva.
Subtask of issue #21435
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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).
Imported from Jira BEAM-14068. Original Jira may contain additional context. Reported by: Anand Inguva. Subtask of issue #21435