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https://issues.apache.org/jira/browse/BEAM-13985?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Anand Inguva updated BEAM-13985:
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Description:
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.
was:
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.
> Implement benchmark E2E tests for RunInference classes
> ------------------------------------------------------
>
> Key: BEAM-13985
> URL: https://issues.apache.org/jira/browse/BEAM-13985
> Project: Beam
> Issue Type: Sub-task
> Components: sdk-py-core
> Reporter: Andy Ye
> Assignee: Anand Inguva
> Priority: P2
> Labels: run-inference
>
> 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.
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