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[BUG] MNMG nightly tests remaining sporadic RF tests failures #3820

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viclafargue opened this issue May 3, 2021 · 5 comments
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[BUG] MNMG nightly tests remaining sporadic RF tests failures #3820

viclafargue opened this issue May 3, 2021 · 5 comments
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bug Something isn't working Dask / cuml.dask Issue/PR related to Python level dask or cuml.dask features.

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@viclafargue
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MNMG nightly tests only has a few remaining test failures. Fixing these would make it possible to have a perfect MNMG testing for the first time!

3 GPUs:
cuml.test.dask.test_random_forest.test_rf_regression_dask_fil[5-float32]

8 GPUs:
cuml.test.dask.test_random_forest.test_rf_classification_dask_fil_predict_proba[5]
cuml.test.dask.test_random_forest.test_rf_broadcast[True-False-classification]

@hcho3 can you help on this? On my side, I'll investigate the test_rf_broadcast test and look for any regression related to CuPy update in it.

@viclafargue viclafargue added bug Something isn't working Dask / cuml.dask Issue/PR related to Python level dask or cuml.dask features. labels May 3, 2021
@JohnZed JohnZed changed the title [BUG] MNMG nightly tests remaining tests failures [BUG] MNMG nightly tests remaining sporadic RF tests failures May 4, 2021
@JohnZed
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JohnZed commented May 4, 2021

@lowener to take a first look and sync with @hcho3 if more details needed.

@hcho3
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hcho3 commented May 5, 2021

@viclafargue I filed #3830 to fix test_rf_regression_dask_fil. On the other hand, I was not able to reproduce the failure of test_rf_classification_dask_fil_predict_proba, even when I ran it 20 times in a row.

@lowener
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lowener commented May 5, 2021

I was able to reproduce the error of test_rf_classification_dask_fil_predict_proba so I made a PR, see #3831

@viclafargue
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Thanks guys! On my side, I could discover the reason behind the test_rf_broadcast failure. It wasn't related to CuPy update. I created a fix for it, see #3833.

rapids-bot bot pushed a commit that referenced this issue May 10, 2021
Answers #3820.

This PR fixes the broadcast feature of the Random Forest estimator. The weights used by the reduction step were generated incorrectly. Indeed, the right values are to be deducted, for each chunk to be predicted, by the number estimators trained by the specific worker holding that chunk. The values wrongly used previously were the number of estimators held by each worker in the order of their construction.

Authors:
  - Victor Lafargue (https://github.com/viclafargue)

Approvers:
  - Dante Gama Dessavre (https://github.com/dantegd)

URL: #3833
rapids-bot bot pushed a commit that referenced this issue May 22, 2021
@viclafargue
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Closed by #3830, #3831 and #3833.

vimarsh6739 pushed a commit to vimarsh6739/cuml that referenced this issue Oct 9, 2023
Answers rapidsai#3820.

This PR fixes the broadcast feature of the Random Forest estimator. The weights used by the reduction step were generated incorrectly. Indeed, the right values are to be deducted, for each chunk to be predicted, by the number estimators trained by the specific worker holding that chunk. The values wrongly used previously were the number of estimators held by each worker in the order of their construction.

Authors:
  - Victor Lafargue (https://github.com/viclafargue)

Approvers:
  - Dante Gama Dessavre (https://github.com/dantegd)

URL: rapidsai#3833
vimarsh6739 pushed a commit to vimarsh6739/cuml that referenced this issue Oct 9, 2023
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