import test_cluster_sanity test_cluster_sanity.test(a_node, pp, algos) # Clean up old objects from the DKV, in case the cluster has been doing other things: if h2o_test_utils.isVerbose(): print('Cleaning up old stuff. . .') h2o_test_utils.cleanup(a_node) import test_and_import_frames datasets = test_and_import_frames.load_and_test(a_node, pp) import test_models test_models.build_and_test(a_node, pp, datasets, algos, algo_additional_default_params) # Metadata used to get corrupted, so test again test_metadata.test(a_node, pp) import test_predict_and_model_metrics test_predict_and_model_metrics.test(a_node, pp) import test_final_sanity test_final_sanity.test(a_node, pp) # TODO: use built_models if clean_up_after: h2o_test_utils.cleanup(models=[ dl_airlines_model_name, 'deeplearning_prostate_binomial', 'kmeans_prostate' ], frames=['prostate_binomial', 'airlines_binomial'])
import test_cluster_sanity test_cluster_sanity.test(a_node, pp, algos) # Clean up old objects from the DKV, in case the cluster has been doing other things: if h2o_test_utils.isVerbose(): print('Cleaning up old stuff. . .') h2o_test_utils.cleanup(a_node) import test_and_import_frames datasets = test_and_import_frames.load_and_test(a_node, pp) import test_models test_models.build_and_test(a_node, pp, datasets, algos, algo_additional_default_params) # Metadata used to get corrupted, so test again test_metadata.test(a_node, pp) import test_predict_and_model_metrics test_predict_and_model_metrics.test(a_node, pp) import test_final_sanity test_final_sanity.test(a_node, pp) # TODO: use built_models if clean_up_after: h2o_test_utils.cleanup(models=[dl_airlines_model_name, 'deeplearning_prostate_binomial', 'kmeans_prostate'], frames=['prostate_binomial', 'airlines_binomial'])