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cuML - RAPIDS Machine Learning Library
https://docs.rapids.ai/api/cuml/stable/
Apache License 2.0
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[BUG] Random Forest issue with more than 2 models with different criterion settings #5884

Open tiraldj opened 1 month ago

tiraldj commented 1 month ago

hello, i replicated a simple glitch in cuml. it has to do with Random Forrest. when I change the split-criterion setting and do more than 2 random forests the kernal crashes

rforest0 = cuRFC(split_criterion=0)
rforest0.fit(X_train,y_train)
print("Split Criterion 0")
print('Random Forest accuracy on Training Data: ', round(rforest0.score(X_train,y_train),3))
print('Random Forest accuracy on Test Data: ', round(rforest0.score(X_test,y_test),3))

Expected behavior A clear and concise description of what you expected to happen.

I was expecting to have multiple random forest models and compare their accuracy versus each other. I did not expect models to stop after 2 were generated

so i did that multiple times, each time changing the name of the random forest and changing the split criterion after 2 or 3 times (feels random) the notebook crashes. When I originally had this problem the gpu crashed so hard that i couldn't restart my ubuntu machine.

At first I thought it was a GPU VRAM issue but there is plenty of memory available. Then I wondered if one of the splits criterion was buggy and everything else works but that wasn't the case either. I then tried not naming the random forest more than once (so that the machine keeps overwriting the previous one) and that didn't work either.

So basically i was able to do 2 different versions of random forest one after another.

the dataset I'm using is not big. ~24k rows, 500 columns

here is the first error message. the second time the kernal crashes. This glitch was replicated on a on prem Titan RTX and on Paperspace Gradient cloud computer using RAPIDS image and P6000

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In[112], line 3
      1 rforest3 = cuRFC(split_criterion=3)
----> 3 rforest3.fit(X_train,y_train)
      5 print("Split Criterion 3")
      6 print('Random Forest accuracy on Training Data: ', round(rforest3.score(X_train,y_train),3))

File /media/mosman/tank/anaconda/envs/rapids/lib/python3.11/site-packages/nvtx/nvtx.py:116, in annotate.__call__.<locals>.inner(*args, **kwargs)
    113 @wraps(func)
    114 def inner(*args, **kwargs):
    115     libnvtx_push_range(self.attributes, self.domain.handle)
--> 116     result = func(*args, **kwargs)
    117     libnvtx_pop_range(self.domain.handle)
    118     return result

File /media/mosman/tank/anaconda/envs/rapids/lib/python3.11/site-packages/cuml/internals/api_decorators.py:188, in _make_decorator_function.<locals>.decorator_function.<locals>.decorator_closure.<locals>.wrapper(*args, **kwargs)
    185     set_api_output_dtype(output_dtype)
    187 if process_return:
--> 188     ret = func(*args, **kwargs)
    189 else:
    190     return func(*args, **kwargs)

File randomforestclassifier.pyx:452, in cuml.ensemble.randomforestclassifier.RandomForestClassifier.fit()

RuntimeError: exception occurred! file=/opt/conda/conda-bld/work/cpp/src/decisiontree/decisiontree.cu line=38: MAE not supported.
Obtained 52 stack frames
#1 in /media/mosman/tank/anaconda/envs/rapids/lib/python3.11/site-packages/cuml/internals/../../../../libcuml++.so(+0x187b36) [0x7d47c6b87b36]
#2 in /media/mosman/tank/anaconda/envs/rapids/lib/python3.11/site-packages/cuml/internals/../../../../libcuml++.so: ML::DT::set_tree_params(ML::DT::DecisionTreeParams&, int, int, float, int, int, int, float, ML::CRITERION, int) +0x61 [0x7d47c6f29ff1]
#3 in /media/mosman/tank/anaconda/envs/rapids/lib/python3.11/site-packages/cuml/internals/../../../../libcuml++.so: ML::set_rf_params(int, int, float, int, int, int, float, bool, int, float, unsigned long, ML::CRITERION, int, int) +0x45 [0x7d47c779b105]
#4 in /media/mosman/tank/anaconda/envs/rapids/lib/python3.11/site-packages/cuml/ensemble/randomforestclassifier.cpython-311-x86_64-linux-gnu.so(+0x2c88b) [0x7d47f1f7188b]
#5 in /media/mosman/tank/anaconda/envs/rapids/bin/python: _PyEval_EvalFrameDefault +0x49f9 [0x57cfd10e7119]
#6 in /media/mosman/tank/anaconda/envs/rapids/bin/python: _PyFunction_Vectorcall +0x181 [0x57cfd11064c1]
#7 in /media/mosman/tank/anaconda/envs/rapids/bin/python: _PyEval_EvalFrameDefault +0x49f9 [0x57cfd10e7119]
#8 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x2a442d) [0x57cfd119942d]
#9 in /media/mosman/tank/anaconda/envs/rapids/bin/python: PyEval_EvalCode +0x9f [0x57cfd1198abf]
#10 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x2bb07e) [0x57cfd11b007e]
#11 in /media/mosman/tank/anaconda/envs/rapids/bin/python: _PyEval_EvalFrameDefault +0x3a86 [0x57cfd10e61a6]
#12 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x2b85c9) [0x57cfd11ad5c9]
#13 in /media/mosman/tank/anaconda/envs/rapids/bin/python: _PyEval_EvalFrameDefault +0x3341 [0x57cfd10e5a61]
#14 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x2b85c9) [0x57cfd11ad5c9]
#15 in /media/mosman/tank/anaconda/envs/rapids/bin/python: _PyEval_EvalFrameDefault +0x3341 [0x57cfd10e5a61]
#16 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x2b85c9) [0x57cfd11ad5c9]
#17 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x2b8f47) [0x57cfd11adf47]
#18 in /media/mosman/tank/anaconda/envs/rapids/bin/python: _PyEval_EvalFrameDefault +0x396a [0x57cfd10e608a]
#19 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x2303a4) [0x57cfd11253a4]
#20 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x22fb8e) [0x57cfd1124b8e]
#21 in /media/mosman/tank/anaconda/envs/rapids/bin/python: PyObject_Call +0x130 [0x57cfd11101e0]
#22 in /media/mosman/tank/anaconda/envs/rapids/bin/python: _PyEval_EvalFrameDefault +0x49f9 [0x57cfd10e7119]
#23 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x2b85c9) [0x57cfd11ad5c9]
#24 in /media/mosman/tank/anaconda/envs/rapids/bin/python: _PyEval_EvalFrameDefault +0x3341 [0x57cfd10e5a61]
#25 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x2b85c9) [0x57cfd11ad5c9]
#26 in /media/mosman/tank/anaconda/envs/rapids/bin/python: _PyEval_EvalFrameDefault +0x3341 [0x57cfd10e5a61]
#27 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x2b85c9) [0x57cfd11ad5c9]
#28 in /media/mosman/tank/anaconda/envs/rapids/bin/python: _PyEval_EvalFrameDefault +0x3341 [0x57cfd10e5a61]
#29 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x2b85c9) [0x57cfd11ad5c9]
#30 in /media/mosman/tank/anaconda/envs/rapids/bin/python: _PyEval_EvalFrameDefault +0x3341 [0x57cfd10e5a61]
#31 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x2b85c9) [0x57cfd11ad5c9]
#32 in /media/mosman/tank/anaconda/envs/rapids/bin/python: _PyEval_EvalFrameDefault +0x3341 [0x57cfd10e5a61]
#33 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x2b85c9) [0x57cfd11ad5c9]
#34 in /media/mosman/tank/anaconda/envs/rapids/lib/python3.11/lib-dynload/_asyncio.cpython-311-x86_64-linux-gnu.so(+0x78b6) [0x7d4887e528b6]
#35 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x1fff1a) [0x57cfd10f4f1a]
#36 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x199a53) [0x57cfd108ea53]
#37 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x19bb03) [0x57cfd1090b03]
#38 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x1faadf) [0x57cfd10efadf]
#39 in /media/mosman/tank/anaconda/envs/rapids/bin/python: _PyEval_EvalFrameDefault +0x9004 [0x57cfd10eb724]
#40 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x2a442d) [0x57cfd119942d]
#41 in /media/mosman/tank/anaconda/envs/rapids/bin/python: PyEval_EvalCode +0x9f [0x57cfd1198abf]
#42 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x2bb07e) [0x57cfd11b007e]
#43 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x1faadf) [0x57cfd10efadf]
#44 in /media/mosman/tank/anaconda/envs/rapids/bin/python: PyObject_Vectorcall +0x2c [0x57cfd10ef9cc]
#45 in /media/mosman/tank/anaconda/envs/rapids/bin/python: _PyEval_EvalFrameDefault +0x716 [0x57cfd10e2e36]
#46 in /media/mosman/tank/anaconda/envs/rapids/bin/python: _PyFunction_Vectorcall +0x181 [0x57cfd11064c1]
#47 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x2cdb38) [0x57cfd11c2b38]
#48 in /media/mosman/tank/anaconda/envs/rapids/bin/python: Py_RunMain +0x139 [0x57cfd11c24b9]
#49 in /media/mosman/tank/anaconda/envs/rapids/bin/python: Py_BytesMain +0x37 [0x57cfd1188a77]
#50 in /lib/x86_64-linux-gnu/libc.so.6(+0x28150) [0x7d4887c28150]
#51 in /lib/x86_64-linux-gnu/libc.so.6: __libc_start_main +0x89 [0x7d4887c28209]
#52 in /media/mosman/tank/anaconda/envs/rapids/bin/python(+0x29391d) [0x57cfd118891d]

Environment details (please complete the following information):

results from conda list

Name Version Build Channel

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dantegd commented 1 month ago

Thanks for the issue @tiraldj, the problem in the stack you posted is that the MAE criterion is not supported as can be seen here: https://docs.rapids.ai/api/cuml/stable/api/#random-forest . There was an old PR to add support for it but it wasn't merged: https://github.com/rapidsai/cuml/pull/4030. If you avoid the split criterion MAE (or 3 in the list of options), do you still encounter the issue?

We will add better handling of the parameter in the python library so that a C++ stack trace is not propagated from this issue, and will consider adding MAE in the future.

mcmrc commented 1 month ago

@tiraldj @dantegd Hello, all. I read through the discussion on PR #4030. Can I take this issue?

dantegd commented 3 weeks ago

@mcmrc apologies I missed your reply, we would be very glad for a contribution from you here!