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02_​H2O_​for_​Performance

Performance and Scalability Testing: Example KNIME with H2O

This workflows show how to learn a random forest using the KNIME H2O nodes.

We here are measuring the speed of the workflow with the last metanode. In addition it collects the max used memory and the start parameters of this instantiation of the KNIME Analytics Platform.

Performance and Scalability Testing: Example KNIME with H2OThis workflows show how to learn a random forest using the KNIME H2O nodes.We here are measuring the speed of the workflow with the last metanode. In addition it collects the max used memory and the startparameters of this instantiation of the KNIME Analytics Platform. Data Conversion and Transfer Preprocessing Receivedata from caller workfowRead Input DataIncrease iterationsfor mutliple evaluationrounds Combine Table to H2O H2O Local Context H2O Partitioning CSV Writer ContainerOutput (Table) ContainerInput (Table) Missing Value File Reader Capture Accuracy Benchmark End(Memory Monitoring) Benchmark Start(Memory Monitoring) H2O RandomForest Learner H2O Predictor(Classification) H2O MultinomialScorer Performance and Scalability Testing: Example KNIME with H2OThis workflows show how to learn a random forest using the KNIME H2O nodes.We here are measuring the speed of the workflow with the last metanode. In addition it collects the max used memory and the startparameters of this instantiation of the KNIME Analytics Platform. Data Conversion and Transfer Preprocessing Receivedata from caller workfowRead Input DataIncrease iterationsfor mutliple evaluationrounds Combine Table to H2O H2O Local Context H2O Partitioning CSV Writer ContainerOutput (Table) ContainerInput (Table) Missing Value File Reader Capture Accuracy Benchmark End(Memory Monitoring) Benchmark Start(Memory Monitoring) H2O RandomForest Learner H2O Predictor(Classification) H2O MultinomialScorer

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