[BUG] Random Forest issue with more than 2 models with different criterion settings

[tiraldj]

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):

• Environment location: test with both Bare-metal and Cloud(Paperspace)]
• Linux Distro/Architecture: [Ubuntu 23.10]
• GPU Model/Driver: [Titan RTX, P5000 and driver 535.171.04 CUDA Version: 12.2
• Method of cuDF & cuML install: [conda]
  • If method of install is [conda], run conda list and include results here
    NVIDIA-SMI 535.171.04 Driver Version: 535.171.04 CUDA Version: 12.2

results from conda list

Name Version Build Channel

_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_kmp_llvm conda-forge
absl-py 2.1.0 pyhd8ed1ab_0 conda-forge
aiohttp 3.9.3 py311h459d7ec_1 conda-forge
aiosignal 1.3.1 pyhd8ed1ab_0 conda-forge
annotated-types 0.6.0 pyhd8ed1ab_0 conda-forge
anyio 3.7.1 pyhd8ed1ab_0 conda-forge
aom 3.8.2 h59595ed_0 conda-forge
appdirs 1.4.4 pyh9f0ad1d_0 conda-forge
argon2-cffi 23.1.0 pyhd8ed1ab_0 conda-forge
argon2-cffi-bindings 21.2.0 py311h459d7ec_4 conda-forge
arrow 1.3.0 pyhd8ed1ab_0 conda-forge
asttokens 2.4.1 pyhd8ed1ab_0 conda-forge
astunparse 1.6.3 pyhd8ed1ab_0 conda-forge
async-lru 2.0.4 pyhd8ed1ab_0 conda-forge
attrs 23.2.0 pyh71513ae_0 conda-forge
aws-c-auth 0.7.11 h0b4cabd_1 conda-forge
aws-c-cal 0.6.9 h14ec70c_3 conda-forge
aws-c-common 0.9.12 hd590300_0 conda-forge
aws-c-compression 0.2.17 h572eabf_8 conda-forge
aws-c-event-stream 0.4.1 h97bb272_2 conda-forge
aws-c-http 0.8.0 h9129f04_2 conda-forge
aws-c-io 0.14.0 hf8f278a_1 conda-forge
aws-c-mqtt 0.10.1 h2b97f5f_0 conda-forge
aws-c-s3 0.4.9 hca09fc5_0 conda-forge
aws-c-sdkutils 0.1.13 h572eabf_1 conda-forge
aws-checksums 0.1.17 h572eabf_7 conda-forge
aws-crt-cpp 0.26.0 h04327c0_8 conda-forge
aws-sdk-cpp 1.11.210 hba3e011_10 conda-forge
azure-core-cpp 1.10.3 h91d86a7_1 conda-forge
azure-storage-blobs-cpp 12.10.0 h00ab1b0_0 conda-forge
azure-storage-common-cpp 12.5.0 hb858b4b_2 conda-forge
babel 2.14.0 pyhd8ed1ab_0 conda-forge
beautifulsoup4 4.12.3 pyha770c72_0 conda-forge
bleach 6.1.0 pyhd8ed1ab_0 conda-forge
blinker 1.7.0 pyhd8ed1ab_0 conda-forge
blosc 1.21.5 h0f2a231_0 conda-forge
bokeh 3.4.0 pyhd8ed1ab_0 conda-forge
branca 0.7.1 pyhd8ed1ab_0 conda-forge
brotli 1.1.0 hd590300_1 conda-forge
brotli-bin 1.1.0 hd590300_1 conda-forge
brotli-python 1.1.0 py311hb755f60_1 conda-forge
brunsli 0.1 h9c3ff4c_0 conda-forge
bzip2 1.0.8 hd590300_5 conda-forge
c-ares 1.28.1 hd590300_0 conda-forge
c-blosc2 2.14.1 hb4ffafa_0 conda-forge
ca-certificates 2024.2.2 hbcca054_0 conda-forge
cached-property 1.5.2 hd8ed1ab_1 conda-forge
cached_property 1.5.2 pyha770c72_1 conda-forge
cachetools 5.3.3 pyhd8ed1ab_0 conda-forge
cairo 1.18.0 h3faef2a_0 conda-forge
certifi 2024.2.2 pyhd8ed1ab_0 conda-forge
cffi 1.16.0 py311hb3a22ac_0 conda-forge
cfitsio 4.3.1 hbdc6101_0 conda-forge
charls 2.4.2 h59595ed_0 conda-forge
charset-normalizer 3.3.2 pyhd8ed1ab_0 conda-forge
click 8.1.7 unix_pyh707e725_0 conda-forge
click-plugins 1.1.1 py_0 conda-forge
cligj 0.7.2 pyhd8ed1ab_1 conda-forge
cloudpickle 3.0.0 pyhd8ed1ab_0 conda-forge
colorama 0.4.6 pyhd8ed1ab_0 conda-forge
colorcet 3.1.0 pyhd8ed1ab_0 conda-forge
comm 0.2.2 pyhd8ed1ab_0 conda-forge
contourpy 1.2.0 py311h9547e67_0 conda-forge
cryptography 42.0.5 py311h63ff55d_0 conda-forge
cucim 24.04.00a33 cuda12_py311_240404_g2a06d5c_33 rapidsai-nightly
cuda-cccl_linux-64 12.0.90 ha770c72_1 conda-forge
cuda-cudart 12.0.107 hd3aeb46_8 conda-forge
cuda-cudart-dev 12.0.107 hd3aeb46_8 conda-forge
cuda-cudart-dev_linux-64 12.0.107 h59595ed_8 conda-forge
cuda-cudart-static 12.0.107 hd3aeb46_8 conda-forge
cuda-cudart-static_linux-64 12.0.107 h59595ed_8 conda-forge
cuda-cudart_linux-64 12.0.107 h59595ed_8 conda-forge
cuda-nvcc-dev_linux-64 12.0.76 ha770c72_1 conda-forge
cuda-nvcc-impl 12.0.76 h59595ed_1 conda-forge
cuda-nvcc-tools 12.0.76 h59595ed_1 conda-forge
cuda-nvrtc 12.0.76 hd3aeb46_2 conda-forge
cuda-nvtx 12.0.76 h59595ed_1 conda-forge
cuda-profiler-api 12.0.76 ha770c72_0 conda-forge
cuda-python 12.4.0 py311h7f239a6_1 conda-forge
cuda-version 12.0 hffde075_3 conda-forge
cudf 24.04.00a586 cuda12_py311_240404_g35f818b3e4_586 rapidsai-nightly
cudnn 8.9.7.29 h092f7fd_3 conda-forge
cugraph 24.04.00a114 cuda12_py311_240401_gf753e5146_114 rapidsai-nightly
cuml 24.04.00a51 cuda12_py311_240404_gb9e4a6002_51 rapidsai-nightly
cuproj 24.04.00a30 cuda12_py311_240404_ga6e6fd4a_30 rapidsai-nightly
cupy 13.0.0 py311h8a5e998_3 conda-forge
cupy-core 13.0.0 py311h84cfafc_3 conda-forge
cuspatial 24.04.00a30 cuda12_py311_240404_ga6e6fd4a_30 rapidsai-nightly
cuxfilter 24.04.00a17 cuda12_py311_240404_gfee99d2_17 rapidsai-nightly
cycler 0.12.1 pyhd8ed1ab_0 conda-forge
cytoolz 0.12.3 py311h459d7ec_0 conda-forge
dash 2.16.1 pyhd8ed1ab_0 conda-forge
dask 2024.1.1 pyhd8ed1ab_0 conda-forge
dask-core 2024.1.1 pyhd8ed1ab_0 conda-forge
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dask-cudf 24.04.00a586 cuda12_py311_240404_g35f818b3e4_586 rapidsai-nightly
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dask-sql 2024.3.1a240326 py311_g7600f60_1 dask/label/dev
datashader 0.16.0 pyhd8ed1ab_0 conda-forge
dav1d 1.2.1 hd590300_0 conda-forge
debugpy 1.8.1 py311hb755f60_0 conda-forge
decorator 5.1.1 pyhd8ed1ab_0 conda-forge
defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge
distributed 2024.1.1 pyhd8ed1ab_0 conda-forge
dlpack 0.8 h59595ed_3 conda-forge
entrypoints 0.4 pyhd8ed1ab_0 conda-forge
exceptiongroup 1.2.0 pyhd8ed1ab_2 conda-forge
executing 2.0.1 pyhd8ed1ab_0 conda-forge
expat 2.6.2 h59595ed_0 conda-forge
fastapi 0.110.1 pyhd8ed1ab_0 conda-forge
fastrlock 0.8.2 py311hb755f60_2 conda-forge
filelock 3.13.3 pyhd8ed1ab_0 conda-forge
fiona 1.9.5 py311hf8e0aa6_2 conda-forge
flask 3.0.2 pyhd8ed1ab_0 conda-forge
flatbuffers 23.5.26 h59595ed_1 conda-forge
fmt 10.2.1 h00ab1b0_0 conda-forge
folium 0.16.0 pyhd8ed1ab_0 conda-forge
font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge
font-ttf-inconsolata 3.000 h77eed37_0 conda-forge
font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge
font-ttf-ubuntu 0.83 h77eed37_1 conda-forge
fontconfig 2.14.2 h14ed4e7_0 conda-forge
fonts-conda-ecosystem 1 0 conda-forge
fonts-conda-forge 1 0 conda-forge
fonttools 4.50.0 py311h459d7ec_0 conda-forge
fqdn 1.5.1 pyhd8ed1ab_0 conda-forge
freetype 2.12.1 h267a509_2 conda-forge
freexl 2.0.0 h743c826_0 conda-forge
frozenlist 1.4.1 py311h459d7ec_0 conda-forge
fsspec 2024.3.1 pyhca7485f_0 conda-forge
gast 0.5.4 pyhd8ed1ab_0 conda-forge
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hdf4 4.2.15 h2a13503_7 conda-forge
hdf5 1.14.3 nompi_h4f84152_100 conda-forge
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httpx 0.27.0 pyhd8ed1ab_0 conda-forge
hyperframe 6.0.1 pyhd8ed1ab_0 conda-forge
icu 73.2 h59595ed_0 conda-forge
idna 3.6 pyhd8ed1ab_0 conda-forge
imagecodecs 2024.1.1 py311hd0e15ba_2 conda-forge
imageio 2.34.0 pyh4b66e23_0 conda-forge
importlib-metadata 7.1.0 pyha770c72_0 conda-forge
importlib_metadata 7.1.0 hd8ed1ab_0 conda-forge
importlib_resources 6.4.0 pyhd8ed1ab_0 conda-forge
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jbig 2.1 h7f98852_2003 conda-forge
jedi 0.19.1 pyhd8ed1ab_0 conda-forge
jinja2 3.1.3 pyhd8ed1ab_0 conda-forge
joblib 1.3.2 pyhd8ed1ab_0 conda-forge
json-c 0.17 h7ab15ed_0 conda-forge
json5 0.9.24 pyhd8ed1ab_0 conda-forge
jsonpointer 2.4 py311h38be061_3 conda-forge
jsonschema 4.21.1 pyhd8ed1ab_0 conda-forge
jsonschema-specifications 2023.12.1 pyhd8ed1ab_0 conda-forge
jsonschema-with-format-nongpl 4.21.1 pyhd8ed1ab_0 conda-forge
jupyter-lsp 2.2.4 pyhd8ed1ab_0 conda-forge
jupyter-server-proxy 4.1.2 pyhd8ed1ab_0 conda-forge
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jupyter_server 2.13.0 pyhd8ed1ab_0 conda-forge
jupyter_server_terminals 0.5.3 pyhd8ed1ab_0 conda-forge
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Additional context
Add any other context about the problem here.

[dantegd]

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: #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.