[QST] Best practices to achieve greater max_depth and n_trees parameters in RandomForestRegressor #1467

rmccorm4 · 2019-12-10T02:58:31Z

What is your question?

Hi, I have a sample script here that reads in a DF of 10k rows and 74 columns. This is just a toy example to mimic some real data that is being used.

The desire is to have large values for max_depth / n_trees on something like a DGX-1 / DGX-2, but on this toy example the system is hitting GPU OOM errors.

import numpy as np
import sklearn

import pandas as pd
import cudf
import cuml

from sklearn import model_selection, datasets

from cuml.dask.common import utils as dask_utils
from dask.distributed import Client, wait
from dask_cuda import LocalCUDACluster
import dask_cudf

from sklearn.metrics import mean_squared_error
from cuml.dask.ensemble import RandomForestRegressor as cumlDaskRF
from sklearn.ensemble import RandomForestRegressor as sklRF

if __name__ == '__main__':
    # Desired parameters
    max_depth = 20
    n_trees = 30
    rows, cols = 10000, 74

    cluster = LocalCUDACluster(threads_per_worker=1)
    if 'c' in globals():
        c.close()
    c = Client(cluster)

    workers = c.has_what().keys()
    n_workers = len(workers)
    n_streams = 8

    # Generate fake data for example's sake
    x = np.random.random((rows, cols))
    df = pd.DataFrame(x, columns=["C{}".format(i) for i in range(cols)])

    X = df.drop(['C2'],1).to_numpy().astype(np.float, 32)
    y = df['C2'].astype(np.float, 32)
    X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2)

    n_partitions = n_workers
    X_train_cudf = cudf.DataFrame.from_pandas(pd.DataFrame(X_train))
    y_train_cudf = cudf.Series(y_train)
    X_train_dask = dask_cudf.from_cudf(X_train_cudf, npartitions=n_partitions)
    y_train_dask = dask_cudf.from_cudf(y_train_cudf, npartitions=n_partitions)
    X_train_dask, y_train_dask = \
      dask_utils.persist_across_workers(c, [X_train_dask, y_train_dask], workers=workers)

    skl_model = sklRF(max_depth=max_depth, n_estimators=n_trees, n_jobs=-1)
    skl_model.fit(X_train, y_train)

    cuml_model = cumlDaskRF(max_depth=max_depth, n_estimators=n_trees,
                            n_streams=n_streams,
                            workers = workers
                           )
    cuml_model.fit(X_train_dask, y_train_dask)

    wait(cuml_model.rfs)

    skl_y_pred = skl_model.predict(X_test)
    print("SKLearn accuracy:  ", mean_squared_error(y_test, skl_y_pred))

    cuml_y_pred = cuml_model.predict(X_test)
    print("CuML accuracy:     ", mean_squared_error(y_test, cuml_y_pred))

The goal is to use parameters such as these on large datasets:

    max_depth = 20
    n_trees = 30

Are there any tips/tricks that can be done here to better manage the memory to work with large datasets without running OOM?

rmccorm4 · 2019-12-10T03:04:42Z

I believe some work was done by @miguelangel here (max_depth18.pdf) to try some optimizations to the code and squeeze out some memory, achieving:

max_depth = 18
n_trees = 30

But more is still left to be desired.

nikiforov-sm · 2019-12-10T04:31:04Z

Hi.

Output for max_depth=20:

SKLearn accuracy:   0.08841762935540724

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
 in 
     61 print("SKLearn accuracy:  ", mean_squared_error(y_test, skl_y_pred))
     62 
---> 63 cuml_y_pred = cuml_model.predict(X_test)
     64 print("CuML accuracy:     ", mean_squared_error(y_test, cuml_y_pred))

/opt/anaconda/lib/python3.6/site-packages/cuml/dask/ensemble/randomforestregressor.py in predict(self, X)
    397         rslts = list()
    398         for d in range(len(f)):
--> 399             rslts.append(f[d].result())
    400             indexes.append(0)
    401 

/opt/anaconda/lib/python3.6/site-packages/distributed/client.py in result(self, timeout)
    225         result = self.client.sync(self._result, callback_timeout=timeout, raiseit=False)
    226         if self.status == "error":
--> 227             six.reraise(*result)
    228         elif self.status == "cancelled":
    229             raise result

/opt/anaconda/lib/python3.6/site-packages/six.py in reraise(tp, value, tb)
    693                 value = tp()
    694             if value.__traceback__ is not tb:
--> 695                 raise value.with_traceback(tb)
    696             raise value
    697         finally:

/opt/anaconda/lib/python3.6/site-packages/cuml/dask/ensemble/randomforestregressor.py in _predict()
    286     @staticmethod
    287     def _predict(model, X, r):
--> 288         return model.predict(X)
    289 
    290     def fit(self, X, y):

cuml/ensemble/randomforestregressor.pyx in cuml.ensemble.randomforestregressor.RandomForestRegressor.predict()

RuntimeError: ('Long error message', 'Exception occured! file=/conda/conda-bld/libcuml_1566588242169/work/cpp/src/decisiontree/decisiontree_impl.cuh line=392: Cannot predict w/ empty tree!\nObtained 37 stack frames\n#0 in /opt/anaconda/lib/python3.6/site-packages/cuml/common/../../../../libcuml++.so(_ZN8MLCommon9Exception16collectCallStackEv+0x3e) [0x7f9d9806556e]\n#1 in /opt/anaconda/lib/python3.6/site-packages/cuml/common/../../../../libcuml++.so(_ZN8MLCommon9ExceptionC2ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE+0x80) [0x7f9d98066080]\n#2 in /opt/anaconda/lib/python3.6/site-packages/cuml/common/../../../../libcuml++.so(_ZNK2ML12DecisionTree16DecisionTreeBaseIddE7predictERKNS_10cumlHandleEPKNS0_16TreeMetaDataNodeIddEEPKdiiPdb+0x20b) [0x7f9d9809d27b]\n#3 in /opt/anaconda/lib/python3.6/site-packages/cuml/common/../../../../libcuml++.so(_ZNK2ML11rfRegressorIdE7predictERKNS_10cumlHandleEPKdiiPdPKNS_20RandomForestMetaDataIddEEb+0x221) [0x7f9d9823e5a1]\n#4 in /opt/anaconda/lib/python3.6/site-packages/cuml/common/../../..')

vishalmehta1991 · 2019-12-10T10:11:25Z

No of trees should not affect the memory consumption. So you can bump those up say 100 trees at depth 16

nikiforov-sm · 2019-12-11T11:09:58Z

Ok, but how to get deeper trees?

teju85 · 2019-12-11T11:34:23Z

We are trying to address these and other issues with RF via 2 parallel approaches:

Short term: @vishalmehta1991 will update the existing algo to be more efficient wrt memory footprint. This should be available in 0.12 branch soon.
Long term: myself and Vishal are also designing a more robust (and also potentially faster) implementation. ETA for this is currently unknown.

vishalmehta1991 · 2019-12-11T12:30:36Z

@nikiforov-sm I have an implementation for classification. I can give you regression for deep trees, can you manage building cuml from source ? or you would need to wait util we integrate in the 0.12 nightly

nikiforov-sm · 2019-12-11T13:30:36Z

@vishalmehta1991 Yes, please.
I'm ready to try build cuml from source.

vishalmehta1991 · 2019-12-12T10:56:44Z

Hi @nikiforov-sm
Here is a branch you can use https://github.com/vishalmehta1991/cuml/tree/gather-tree-builder

I have tested it to depths of 50. Hopefully works for you as well.
Feel free to write me back if you see issues.

nikiforov-sm · 2019-12-12T11:06:16Z

Great news! Thank you!

nikiforov-sm · 2019-12-18T04:14:59Z

Currently we have issues with building cuml from source.
We are trying to build and run it in docker-container.

(cuml_dev) root@nvidia-MLT:/opt/jupyter/cuml/cpp/build# cmake .. -DCMAKE_IGNORE_PATH=$CONDA_PREFIX/lib -DCMAKE_INSTALL_PREFIX=/opt/anaconda
-- The CXX compiler identification is GNU 7.4.0
-- The CUDA compiler identification is NVIDIA 10.1.243
-- Check for working CXX compiler: /usr/bin/c++
-- Check for working CXX compiler: /usr/bin/c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Check for working CUDA compiler: /usr/local/cuda/bin/nvcc
-- Check for working CUDA compiler: /usr/local/cuda/bin/nvcc -- works
-- Detecting CUDA compiler ABI info
-- Detecting CUDA compiler ABI info - done
-- Setting build type to 'Release' since none specified.
-- Looking for C++ include pthread.h
-- Looking for C++ include pthread.h - found
-- Looking for pthread_create
-- Looking for pthread_create - not found
-- Looking for pthread_create in pthreads
-- Looking for pthread_create in pthreads - not found
-- Looking for pthread_create in pthread
-- Looking for pthread_create in pthread - found
-- Found Threads: TRUE
-- Found CUDA: /usr/local/cuda (found suitable version "10.1", minimum required is "9.0")
-- Found OpenMP_CXX: -fopenmp (found version "4.5")
-- Found OpenMP: TRUE (found version "4.5")
-- OpenMP found in
-- Found ZLIB: /usr/lib/x86_64-linux-gnu/libz.so (found version "1.2.11")
-- ZLib found in /usr/include
-- Manually setting BLAS to
-- Found Protobuf: /usr/local/lib/libprotobuf.a;-lpthread (found version "3.8.0")
-- Found ClangFormat: /opt/anaconda/envs/cuml_dev/bin/clang-format (found suitable exact version "8.0.0")
-- Building with OpenMP support
Auto detection of gpu-archs: 70
-- Building for GPU_ARCHS = 70
-- Enabling the GLIBCXX11 ABI
-- Found NCCL: /usr/lib/x86_64-linux-gnu/libnccl.so (found version "2.5.6")
-- Found UCX: /usr/local/lib/libucp.so
-- Configuring done
-- Generating done
-- Build files have been written to: /opt/jupyter/cuml/cpp/build

Error in make:

(cuml_dev) root@nvidia-MLT:/opt/jupyter/cuml/cpp/build# make [ 0%] Performing update step for 'cub'
[ 0%] No configure step for 'cub'
[ 1%] No build step for 'cub'
[ 1%] No install step for 'cub'
[ 2%] Completed 'cub'
[ 4%] Built target cub
[ 4%] Performing update step for 'cutlass'
[ 4%] No configure step for 'cutlass'
[ 5%] No build step for 'cutlass'
[ 6%] No install step for 'cutlass'
[ 6%] Completed 'cutlass'
[ 8%] Built target cutlass
[ 12%] Built target faiss
[ 13%] Run clang-format on the cpp source files [ 13%] Built target format [ 17%] Built target treelite [ 32%] Built target cuml++ [ 32%] Linking CXX shared library libcuml.so
/usr/bin/ld: /usr/local/lib/libprotobuf.a(arena.o): relocation R_X86_64_TPOFF32 against symbol _ZN6google8protobuf8internal9ArenaImpl13thread_cache_E' can not be used when making a shared object; recompile with -fPIC /usr/bin/ld: /usr/local/lib/libprotobuf.a(descriptor.o): relocation R_X86_64_PC32 against symbol ZZN6google8protobuf8internal16OnShutdownDeleteINS0_25EncodedDescriptorDatabaseEEEPT_S5_ENUlPKvE_4_FUNES7' can not be used when making a shared object; recompile with -fPIC
/usr/bin/ld: final link failed: Bad value
collect2: error: ld returned 1 exit status
CMakeFiles/cuml.dir/build.make:157: recipe for target 'libcuml.so' failed
make[2]: *** [libcuml.so] Error 1
CMakeFiles/Makefile2:330: recipe for target 'CMakeFiles/cuml.dir/all' failed
make[1]: *** [CMakeFiles/cuml.dir/all] Error 2
Makefile:129: recipe for target 'all' failed
make: *** [all] Error 2

protobuf gathered from here:
https://github.com/protocolbuffers/protobuf/releases/tag/v3.8.0
https://github.com/protocolbuffers/protobuf/releases/download/v3.8.0/protobuf-all-3.8.0.tar.gz

vishalmehta1991 · 2019-12-18T10:02:49Z

@nikiforov-sm Hmm, i dont see this. To build from source i recommend use anaconda. Make sure you update the code.

git clone https://github.com/vishalmehta1991/cuml.git -b gather-tree-builder
conda activate && conda env update --name base --file cuml/conda/environments/cuml_dev_cuda10.1.yml
cd cuml && ./build.sh

I use this approach and works well

nikiforov-sm · 2019-12-18T15:33:28Z

Are we need to build in anaconda using Python 3.7?

nikiforov-sm · 2019-12-18T15:52:56Z

Anaconda python 3.6:

Comparing specs that have this dependency: 27%|██████████████████████████▌ | 18/67 [23:06<1:02:55, 77.04s/it]
Finding shortest conflict path for setuptools[version='>=40.0']: 50%|███████████████████████████████████████▌ | 14/28 [08:12<05:24, 23.18s/it]

Finding shortest conflict path for setuptools: 0%| | 0/1 [00:00<?, ?it/s]
Finding shortest conflict path for setuptools: 0%| | 0/1 [00:00<?, ?it/s]
Finding shortest conflict path for libnvstrings[version='>=0.10.0a.1191022,<0.11.0a0']: 91%|██████████████████████████████████████████████████▊ | 39/43 [00:15<00:01, 2.62it/s]
Finding shortest conflict path for libnvstrings[version='>=0.12.0b.191213,<0.13.0a0']: 87%|█████████████████████████████████████████████████▌ | 20/23 [02:11<00:20, 6.93s/it]
Finding shortest conflict path for cudf=0.12: 53%|████████████████████████████████████████████████████▏ | 25/47 [00:07<00:08, 2.69it/s]

vishalmehta1991 · 2019-12-18T17:13:35Z

not sure if we need 3.7, but i typically use 3.7,
Also if the base env does not update due to depedency,
you can always create a new env, which is very quick.

conda env create --name cuml_dev --file cuml/conda/environments/cuml_dev_cuda10.1.yml
conda activate cuml_dev

nikiforov-sm · 2019-12-19T09:16:30Z

Thank you!
We've made a build.
With test-script from the top we have errors:

SKLearn accuracy:   0.08639243151504108
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
 in 
     61 print("SKLearn accuracy:  ", mean_squared_error(y_test, skl_y_pred))
     62 
---> 63 cuml_y_pred = cuml_model.predict(X_test)
     64 print("CuML accuracy:     ", mean_squared_error(y_test, cuml_y_pred))

/opt/anaconda/lib/python3.7/site-packages/cuml/dask/ensemble/randomforestregressor.py in predict(self, X)
    401 
    402         wait(futures)
--> 403         raise_exception_from_futures(futures)
    404 
    405         indexes = list()

/opt/anaconda/lib/python3.7/site-packages/cuml/dask/common/utils.py in raise_exception_from_futures(futures)
    129     if errs:
    130         raise RuntimeError("%d of %d worker jobs failed: %s" % (
--> 131             len(errs), len(futures), ", ".join(map(str, errs))
    132             ))
    133 

RuntimeError: 8 of 8 worker jobs failed: Exception occured! file=/opt/jupyter/cuml/cpp/src/randomforest/randomforest.cu line=324: TREELITE FAIL: call='TreeliteModelBuilderCommitModel(model_builder, model)'. Reason:[12:15:45] /opt/jupyter/cuml/cpp/build/treelite/src/treelite/src/frontend/builder.cc:440: Impossible thing happened: model has no leaf node!

Stack trace returned 10 entries:
[bt] (0) /opt/anaconda/lib/libcuml++.so(dmlc::StackTrace[abi:cxx11]()+0x17f) [0x7f0aa1565d3f]
[bt] (1) /opt/anaconda/lib/libcuml++.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x39) [0x7f0aa15670b9]
[bt] (2) /opt/anaconda/lib/libcuml++.so(treelite::frontend::ModelBuilder::CommitModel(treelite::Model*)+0x3a0d) [0x7f0aa15830cd]
[bt] (3) /opt/anaconda/lib/libcuml++.so(TreeliteModelBuilderCommitModel+0x146) [0x7f0aa1562926]
[bt] (4) /opt/anaconda/lib/libcuml++.so(void ML::build_treelite_forest(void**, ML::RandomForestMetaData const*, int, int, std::vector >&)+0x2fa) [0x7f0aa138a49a]
[bt] (5) /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x1d328) [0x7f0aacc29328]
[bt] (6) /opt/anaconda/bin/python(_PyObject_FastCallDict+0x9f) [0x56328c5359cf]
[bt] (7) /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
[bt] (8) /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x18072) [0x7f0aacc24072]
[bt] (9) /opt/anaconda/bin/python(_PyObject_FastCallKeywords+0x49b) [0x56328c597d2b]



Obtained 28 stack frames
#0 in /opt/anaconda/lib/libcuml++.so(_ZN8MLCommon9Exception16collectCallStackEv+0x3e) [0x7f0aa11611be]
#1 in /opt/anaconda/lib/libcuml++.so(_ZN8MLCommon9ExceptionC2ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE+0x71) [0x7f0aa1162011]
#2 in /opt/anaconda/lib/libcuml++.so(_ZN2ML21build_treelite_forestIffEEvPPvPKNS_20RandomForestMetaDataIT_T0_EEiiRSt6vectorIhSaIhEE+0x7ab) [0x7f0aa138a94b]
#3 in /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x1d328) [0x7f0aacc29328]
#4 in /opt/anaconda/bin/python(_PyObject_FastCallDict+0x9f) [0x56328c5359cf]
#5 in /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
#6 in /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x18072) [0x7f0aacc24072]
#7 in /opt/anaconda/bin/python(_PyObject_FastCallKeywords+0x49b) [0x56328c597d2b]
#8 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x537e) [0x56328c5f37ae]
#9 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#10 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#11 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#12 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#13 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#14 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#15 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#16 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#17 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#18 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#19 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#20 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#21 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#22 in /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
#23 in /opt/anaconda/bin/python(PyObject_Call+0x6e) [0x56328c54195e]
#24 in /opt/anaconda/bin/python(+0x223037) [0x56328c641037]
#25 in /opt/anaconda/bin/python(+0x1e3468) [0x56328c601468]
#26 in /lib/x86_64-linux-gnu/libpthread.so.0(+0x76db) [0x7f0c771626db]
#27 in /lib/x86_64-linux-gnu/libc.so.6(clone+0x3f) [0x7f0c76e8b88f]
, Exception occured! file=/opt/jupyter/cuml/cpp/src/randomforest/randomforest.cu line=324: TREELITE FAIL: call='TreeliteModelBuilderCommitModel(model_builder, model)'. Reason:[12:15:45] /opt/jupyter/cuml/cpp/build/treelite/src/treelite/src/frontend/builder.cc:440: Impossible thing happened: model has no leaf node!

Stack trace returned 10 entries:
[bt] (0) /opt/anaconda/lib/libcuml++.so(dmlc::StackTrace[abi:cxx11]()+0x17f) [0x7f0aa1565d3f]
[bt] (1) /opt/anaconda/lib/libcuml++.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x39) [0x7f0aa15670b9]
[bt] (2) /opt/anaconda/lib/libcuml++.so(treelite::frontend::ModelBuilder::CommitModel(treelite::Model*)+0x3a0d) [0x7f0aa15830cd]
[bt] (3) /opt/anaconda/lib/libcuml++.so(TreeliteModelBuilderCommitModel+0x146) [0x7f0aa1562926]
[bt] (4) /opt/anaconda/lib/libcuml++.so(void ML::build_treelite_forest(void**, ML::RandomForestMetaData const*, int, int, std::vector >&)+0x2fa) [0x7f0aa138a49a]
[bt] (5) /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x1d328) [0x7f0a93ac1328]
[bt] (6) /opt/anaconda/bin/python(_PyObject_FastCallDict+0x9f) [0x56328c5359cf]
[bt] (7) /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
[bt] (8) /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x18072) [0x7f0a93abc072]
[bt] (9) /opt/anaconda/bin/python(_PyObject_FastCallKeywords+0x49b) [0x56328c597d2b]



Obtained 28 stack frames
#0 in /opt/anaconda/lib/libcuml++.so(_ZN8MLCommon9Exception16collectCallStackEv+0x3e) [0x7f0aa11611be]
#1 in /opt/anaconda/lib/libcuml++.so(_ZN8MLCommon9ExceptionC2ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE+0x71) [0x7f0aa1162011]
#2 in /opt/anaconda/lib/libcuml++.so(_ZN2ML21build_treelite_forestIffEEvPPvPKNS_20RandomForestMetaDataIT_T0_EEiiRSt6vectorIhSaIhEE+0x7ab) [0x7f0aa138a94b]
#3 in /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x1d328) [0x7f0a93ac1328]
#4 in /opt/anaconda/bin/python(_PyObject_FastCallDict+0x9f) [0x56328c5359cf]
#5 in /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
#6 in /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x18072) [0x7f0a93abc072]
#7 in /opt/anaconda/bin/python(_PyObject_FastCallKeywords+0x49b) [0x56328c597d2b]
#8 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x537e) [0x56328c5f37ae]
#9 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#10 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#11 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#12 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#13 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#14 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#15 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#16 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#17 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#18 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#19 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#20 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#21 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#22 in /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
#23 in /opt/anaconda/bin/python(PyObject_Call+0x6e) [0x56328c54195e]
#24 in /opt/anaconda/bin/python(+0x223037) [0x56328c641037]
#25 in /opt/anaconda/bin/python(+0x1e3468) [0x56328c601468]
#26 in /lib/x86_64-linux-gnu/libpthread.so.0(+0x76db) [0x7f0c771626db]
#27 in /lib/x86_64-linux-gnu/libc.so.6(clone+0x3f) [0x7f0c76e8b88f]
, Exception occured! file=/opt/jupyter/cuml/cpp/src/randomforest/randomforest.cu line=324: TREELITE FAIL: call='TreeliteModelBuilderCommitModel(model_builder, model)'. Reason:[12:15:45] /opt/jupyter/cuml/cpp/build/treelite/src/treelite/src/frontend/builder.cc:440: Impossible thing happened: model has no leaf node!

Stack trace returned 10 entries:
[bt] (0) /opt/anaconda/lib/libcuml++.so(dmlc::StackTrace[abi:cxx11]()+0x17f) [0x7f0aa1565d3f]
[bt] (1) /opt/anaconda/lib/libcuml++.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x39) [0x7f0aa15670b9]
[bt] (2) /opt/anaconda/lib/libcuml++.so(treelite::frontend::ModelBuilder::CommitModel(treelite::Model*)+0x3a0d) [0x7f0aa15830cd]
[bt] (3) /opt/anaconda/lib/libcuml++.so(TreeliteModelBuilderCommitModel+0x146) [0x7f0aa1562926]
[bt] (4) /opt/anaconda/lib/libcuml++.so(void ML::build_treelite_forest(void**, ML::RandomForestMetaData const*, int, int, std::vector >&)+0x2fa) [0x7f0aa138a49a]
[bt] (5) /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x1d328) [0x7f0a93ac7328]
[bt] (6) /opt/anaconda/bin/python(_PyObject_FastCallDict+0x9f) [0x56328c5359cf]
[bt] (7) /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
[bt] (8) /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x18072) [0x7f0a93ac2072]
[bt] (9) /opt/anaconda/bin/python(_PyObject_FastCallKeywords+0x49b) [0x56328c597d2b]



Obtained 28 stack frames
#0 in /opt/anaconda/lib/libcuml++.so(_ZN8MLCommon9Exception16collectCallStackEv+0x3e) [0x7f0aa11611be]
#1 in /opt/anaconda/lib/libcuml++.so(_ZN8MLCommon9ExceptionC2ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE+0x71) [0x7f0aa1162011]
#2 in /opt/anaconda/lib/libcuml++.so(_ZN2ML21build_treelite_forestIffEEvPPvPKNS_20RandomForestMetaDataIT_T0_EEiiRSt6vectorIhSaIhEE+0x7ab) [0x7f0aa138a94b]
#3 in /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x1d328) [0x7f0a93ac7328]
#4 in /opt/anaconda/bin/python(_PyObject_FastCallDict+0x9f) [0x56328c5359cf]
#5 in /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
#6 in /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x18072) [0x7f0a93ac2072]
#7 in /opt/anaconda/bin/python(_PyObject_FastCallKeywords+0x49b) [0x56328c597d2b]
#8 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x537e) [0x56328c5f37ae]
#9 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#10 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#11 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#12 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#13 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#14 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#15 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#16 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#17 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#18 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#19 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#20 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#21 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#22 in /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
#23 in /opt/anaconda/bin/python(PyObject_Call+0x6e) [0x56328c54195e]
#24 in /opt/anaconda/bin/python(+0x223037) [0x56328c641037]
#25 in /opt/anaconda/bin/python(+0x1e3468) [0x56328c601468]
#26 in /lib/x86_64-linux-gnu/libpthread.so.0(+0x76db) [0x7f0c771626db]
#27 in /lib/x86_64-linux-gnu/libc.so.6(clone+0x3f) [0x7f0c76e8b88f]
, Exception occured! file=/opt/jupyter/cuml/cpp/src/randomforest/randomforest.cu line=324: TREELITE FAIL: call='TreeliteModelBuilderCommitModel(model_builder, model)'. Reason:[12:15:45] /opt/jupyter/cuml/cpp/build/treelite/src/treelite/src/frontend/builder.cc:440: Impossible thing happened: model has no leaf node!

Stack trace returned 10 entries:
[bt] (0) /opt/anaconda/lib/libcuml++.so(dmlc::StackTrace[abi:cxx11]()+0x17f) [0x7f0a99565d3f]
[bt] (1) /opt/anaconda/lib/libcuml++.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x39) [0x7f0a995670b9]
[bt] (2) /opt/anaconda/lib/libcuml++.so(treelite::frontend::ModelBuilder::CommitModel(treelite::Model*)+0x3a0d) [0x7f0a995830cd]
[bt] (3) /opt/anaconda/lib/libcuml++.so(TreeliteModelBuilderCommitModel+0x146) [0x7f0a99562926]
[bt] (4) /opt/anaconda/lib/libcuml++.so(void ML::build_treelite_forest(void**, ML::RandomForestMetaData const*, int, int, std::vector >&)+0x2fa) [0x7f0a9938a49a]
[bt] (5) /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x1d328) [0x7f0aa4a71328]
[bt] (6) /opt/anaconda/bin/python(_PyObject_FastCallDict+0x9f) [0x56328c5359cf]
[bt] (7) /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
[bt] (8) /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x18072) [0x7f0aa4a6c072]
[bt] (9) /opt/anaconda/bin/python(_PyObject_FastCallKeywords+0x49b) [0x56328c597d2b]



Obtained 28 stack frames
#0 in /opt/anaconda/lib/libcuml++.so(_ZN8MLCommon9Exception16collectCallStackEv+0x3e) [0x7f0a991611be]
#1 in /opt/anaconda/lib/libcuml++.so(_ZN8MLCommon9ExceptionC2ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE+0x71) [0x7f0a99162011]
#2 in /opt/anaconda/lib/libcuml++.so(_ZN2ML21build_treelite_forestIffEEvPPvPKNS_20RandomForestMetaDataIT_T0_EEiiRSt6vectorIhSaIhEE+0x7ab) [0x7f0a9938a94b]
#3 in /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x1d328) [0x7f0aa4a71328]
#4 in /opt/anaconda/bin/python(_PyObject_FastCallDict+0x9f) [0x56328c5359cf]
#5 in /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
#6 in /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x18072) [0x7f0aa4a6c072]
#7 in /opt/anaconda/bin/python(_PyObject_FastCallKeywords+0x49b) [0x56328c597d2b]
#8 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x537e) [0x56328c5f37ae]
#9 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#10 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#11 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#12 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#13 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#14 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#15 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#16 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#17 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#18 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#19 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#20 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#21 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#22 in /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
#23 in /opt/anaconda/bin/python(PyObject_Call+0x6e) [0x56328c54195e]
#24 in /opt/anaconda/bin/python(+0x223037) [0x56328c641037]
#25 in /opt/anaconda/bin/python(+0x1e3468) [0x56328c601468]
#26 in /lib/x86_64-linux-gnu/libpthread.so.0(+0x76db) [0x7f0c771626db]
#27 in /lib/x86_64-linux-gnu/libc.so.6(clone+0x3f) [0x7f0c76e8b88f]
, Exception occured! file=/opt/jupyter/cuml/cpp/src/randomforest/randomforest.cu line=324: TREELITE FAIL: call='TreeliteModelBuilderCommitModel(model_builder, model)'. Reason:[12:15:45] /opt/jupyter/cuml/cpp/build/treelite/src/treelite/src/frontend/builder.cc:440: Impossible thing happened: model has no leaf node!

Stack trace returned 10 entries:
[bt] (0) /opt/anaconda/lib/libcuml++.so(dmlc::StackTrace[abi:cxx11]()+0x17f) [0x7f0a99565d3f]
[bt] (1) /opt/anaconda/lib/libcuml++.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x39) [0x7f0a995670b9]
[bt] (2) /opt/anaconda/lib/libcuml++.so(treelite::frontend::ModelBuilder::CommitModel(treelite::Model*)+0x3a0d) [0x7f0a995830cd]
[bt] (3) /opt/anaconda/lib/libcuml++.so(TreeliteModelBuilderCommitModel+0x146) [0x7f0a99562926]
[bt] (4) /opt/anaconda/lib/libcuml++.so(void ML::build_treelite_forest(void**, ML::RandomForestMetaData const*, int, int, std::vector >&)+0x2fa) [0x7f0a9938a49a]
[bt] (5) /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x1d328) [0x7f0aa4b6d328]
[bt] (6) /opt/anaconda/bin/python(_PyObject_FastCallDict+0x9f) [0x56328c5359cf]
[bt] (7) /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
[bt] (8) /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x18072) [0x7f0aa4b68072]
[bt] (9) /opt/anaconda/bin/python(_PyObject_FastCallKeywords+0x49b) [0x56328c597d2b]



Obtained 28 stack frames
#0 in /opt/anaconda/lib/libcuml++.so(_ZN8MLCommon9Exception16collectCallStackEv+0x3e) [0x7f0a991611be]
#1 in /opt/anaconda/lib/libcuml++.so(_ZN8MLCommon9ExceptionC2ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE+0x71) [0x7f0a99162011]
#2 in /opt/anaconda/lib/libcuml++.so(_ZN2ML21build_treelite_forestIffEEvPPvPKNS_20RandomForestMetaDataIT_T0_EEiiRSt6vectorIhSaIhEE+0x7ab) [0x7f0a9938a94b]
#3 in /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x1d328) [0x7f0aa4b6d328]
#4 in /opt/anaconda/bin/python(_PyObject_FastCallDict+0x9f) [0x56328c5359cf]
#5 in /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
#6 in /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x18072) [0x7f0aa4b68072]
#7 in /opt/anaconda/bin/python(_PyObject_FastCallKeywords+0x49b) [0x56328c597d2b]
#8 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x537e) [0x56328c5f37ae]
#9 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#10 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#11 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#12 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#13 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#14 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#15 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#16 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#17 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#18 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#19 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#20 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#21 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#22 in /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
#23 in /opt/anaconda/bin/python(PyObject_Call+0x6e) [0x56328c54195e]
#24 in /opt/anaconda/bin/python(+0x223037) [0x56328c641037]
#25 in /opt/anaconda/bin/python(+0x1e3468) [0x56328c601468]
#26 in /lib/x86_64-linux-gnu/libpthread.so.0(+0x76db) [0x7f0c771626db]
#27 in /lib/x86_64-linux-gnu/libc.so.6(clone+0x3f) [0x7f0c76e8b88f]
, Exception occured! file=/opt/jupyter/cuml/cpp/src/randomforest/randomforest.cu line=324: TREELITE FAIL: call='TreeliteModelBuilderCommitModel(model_builder, model)'. Reason:[12:15:45] /opt/jupyter/cuml/cpp/build/treelite/src/treelite/src/frontend/builder.cc:440: Impossible thing happened: model has no leaf node!

Stack trace returned 10 entries:
[bt] (0) /opt/anaconda/lib/libcuml++.so(dmlc::StackTrace[abi:cxx11]()+0x17f) [0x7f0a9d565d3f]
[bt] (1) /opt/anaconda/lib/libcuml++.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x39) [0x7f0a9d5670b9]
[bt] (2) /opt/anaconda/lib/libcuml++.so(treelite::frontend::ModelBuilder::CommitModel(treelite::Model*)+0x3a0d) [0x7f0a9d5830cd]
[bt] (3) /opt/anaconda/lib/libcuml++.so(TreeliteModelBuilderCommitModel+0x146) [0x7f0a9d562926]
[bt] (4) /opt/anaconda/lib/libcuml++.so(void ML::build_treelite_forest(void**, ML::RandomForestMetaData const*, int, int, std::vector >&)+0x2fa) [0x7f0a9d38a49a]
[bt] (5) /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x1d328) [0x7f0aa8b33328]
[bt] (6) /opt/anaconda/bin/python(_PyObject_FastCallDict+0x9f) [0x56328c5359cf]
[bt] (7) /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
[bt] (8) /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x18072) [0x7f0aa8b2e072]
[bt] (9) /opt/anaconda/bin/python(_PyObject_FastCallKeywords+0x49b) [0x56328c597d2b]



Obtained 28 stack frames
#0 in /opt/anaconda/lib/libcuml++.so(_ZN8MLCommon9Exception16collectCallStackEv+0x3e) [0x7f0a9d1611be]
#1 in /opt/anaconda/lib/libcuml++.so(_ZN8MLCommon9ExceptionC2ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE+0x71) [0x7f0a9d162011]
#2 in /opt/anaconda/lib/libcuml++.so(_ZN2ML21build_treelite_forestIffEEvPPvPKNS_20RandomForestMetaDataIT_T0_EEiiRSt6vectorIhSaIhEE+0x7ab) [0x7f0a9d38a94b]
#3 in /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x1d328) [0x7f0aa8b33328]
#4 in /opt/anaconda/bin/python(_PyObject_FastCallDict+0x9f) [0x56328c5359cf]
#5 in /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
#6 in /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x18072) [0x7f0aa8b2e072]
#7 in /opt/anaconda/bin/python(_PyObject_FastCallKeywords+0x49b) [0x56328c597d2b]
#8 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x537e) [0x56328c5f37ae]
#9 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#10 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#11 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#12 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#13 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#14 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#15 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#16 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#17 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#18 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#19 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#20 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#21 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#22 in /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
#23 in /opt/anaconda/bin/python(PyObject_Call+0x6e) [0x56328c54195e]
#24 in /opt/anaconda/bin/python(+0x223037) [0x56328c641037]
#25 in /opt/anaconda/bin/python(+0x1e3468) [0x56328c601468]
#26 in /lib/x86_64-linux-gnu/libpthread.so.0(+0x76db) [0x7f0c771626db]
#27 in /lib/x86_64-linux-gnu/libc.so.6(clone+0x3f) [0x7f0c76e8b88f]
, Exception occured! file=/opt/jupyter/cuml/cpp/src/randomforest/randomforest.cu line=324: TREELITE FAIL: call='TreeliteModelBuilderCommitModel(model_builder, model)'. Reason:[12:15:45] /opt/jupyter/cuml/cpp/build/treelite/src/treelite/src/frontend/builder.cc:440: Impossible thing happened: model has no leaf node!

Stack trace returned 10 entries:
[bt] (0) /opt/anaconda/lib/libcuml++.so(dmlc::StackTrace[abi:cxx11]()+0x17f) [0x7f0a9d565d3f]
[bt] (1) /opt/anaconda/lib/libcuml++.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x39) [0x7f0a9d5670b9]
[bt] (2) /opt/anaconda/lib/libcuml++.so(treelite::frontend::ModelBuilder::CommitModel(treelite::Model*)+0x3a0d) [0x7f0a9d5830cd]
[bt] (3) /opt/anaconda/lib/libcuml++.so(TreeliteModelBuilderCommitModel+0x146) [0x7f0a9d562926]
[bt] (4) /opt/anaconda/lib/libcuml++.so(void ML::build_treelite_forest(void**, ML::RandomForestMetaData const*, int, int, std::vector >&)+0x2fa) [0x7f0a9d38a49a]
[bt] (5) /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x1d328) [0x7f0aa8cb0328]
[bt] (6) /opt/anaconda/bin/python(_PyObject_FastCallDict+0x9f) [0x56328c5359cf]
[bt] (7) /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
[bt] (8) /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x18072) [0x7f0aa8cab072]
[bt] (9) /opt/anaconda/bin/python(_PyObject_FastCallKeywords+0x49b) [0x56328c597d2b]



Obtained 28 stack frames
#0 in /opt/anaconda/lib/libcuml++.so(_ZN8MLCommon9Exception16collectCallStackEv+0x3e) [0x7f0a9d1611be]
#1 in /opt/anaconda/lib/libcuml++.so(_ZN8MLCommon9ExceptionC2ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE+0x71) [0x7f0a9d162011]
#2 in /opt/anaconda/lib/libcuml++.so(_ZN2ML21build_treelite_forestIffEEvPPvPKNS_20RandomForestMetaDataIT_T0_EEiiRSt6vectorIhSaIhEE+0x7ab) [0x7f0a9d38a94b]
#3 in /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x1d328) [0x7f0aa8cb0328]
#4 in /opt/anaconda/bin/python(_PyObject_FastCallDict+0x9f) [0x56328c5359cf]
#5 in /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
#6 in /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x18072) [0x7f0aa8cab072]
#7 in /opt/anaconda/bin/python(_PyObject_FastCallKeywords+0x49b) [0x56328c597d2b]
#8 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x537e) [0x56328c5f37ae]
#9 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#10 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#11 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#12 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#13 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#14 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#15 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#16 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#17 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#18 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#19 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#20 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#21 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#22 in /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
#23 in /opt/anaconda/bin/python(PyObject_Call+0x6e) [0x56328c54195e]
#24 in /opt/anaconda/bin/python(+0x223037) [0x56328c641037]
#25 in /opt/anaconda/bin/python(+0x1e3468) [0x56328c601468]
#26 in /lib/x86_64-linux-gnu/libpthread.so.0(+0x76db) [0x7f0c771626db]
#27 in /lib/x86_64-linux-gnu/libc.so.6(clone+0x3f) [0x7f0c76e8b88f]
, Exception occured! file=/opt/jupyter/cuml/cpp/src/randomforest/randomforest.cu line=324: TREELITE FAIL: call='TreeliteModelBuilderCommitModel(model_builder, model)'. Reason:[12:15:45] /opt/jupyter/cuml/cpp/build/treelite/src/treelite/src/frontend/builder.cc:440: Impossible thing happened: model has no leaf node!

Stack trace returned 10 entries:
[bt] (0) /opt/anaconda/lib/libcuml++.so(dmlc::StackTrace[abi:cxx11]()+0x17f) [0x7f0a9d565d3f]
[bt] (1) /opt/anaconda/lib/libcuml++.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x39) [0x7f0a9d5670b9]
[bt] (2) /opt/anaconda/lib/libcuml++.so(treelite::frontend::ModelBuilder::CommitModel(treelite::Model*)+0x3a0d) [0x7f0a9d5830cd]
[bt] (3) /opt/anaconda/lib/libcuml++.so(TreeliteModelBuilderCommitModel+0x146) [0x7f0a9d562926]
[bt] (4) /opt/anaconda/lib/libcuml++.so(void ML::build_treelite_forest(void**, ML::RandomForestMetaData const*, int, int, std::vector >&)+0x2fa) [0x7f0a9d38a49a]
[bt] (5) /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x1d328) [0x7f0a8fb07328]
[bt] (6) /opt/anaconda/bin/python(_PyObject_FastCallDict+0x9f) [0x56328c5359cf]
[bt] (7) /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
[bt] (8) /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x18072) [0x7f0a8fb02072]
[bt] (9) /opt/anaconda/bin/python(_PyObject_FastCallKeywords+0x49b) [0x56328c597d2b]



Obtained 28 stack frames
#0 in /opt/anaconda/lib/libcuml++.so(_ZN8MLCommon9Exception16collectCallStackEv+0x3e) [0x7f0a9d1611be]
#1 in /opt/anaconda/lib/libcuml++.so(_ZN8MLCommon9ExceptionC2ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE+0x71) [0x7f0a9d162011]
#2 in /opt/anaconda/lib/libcuml++.so(_ZN2ML21build_treelite_forestIffEEvPPvPKNS_20RandomForestMetaDataIT_T0_EEiiRSt6vectorIhSaIhEE+0x7ab) [0x7f0a9d38a94b]
#3 in /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x1d328) [0x7f0a8fb07328]
#4 in /opt/anaconda/bin/python(_PyObject_FastCallDict+0x9f) [0x56328c5359cf]
#5 in /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
#6 in /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x18072) [0x7f0a8fb02072]
#7 in /opt/anaconda/bin/python(_PyObject_FastCallKeywords+0x49b) [0x56328c597d2b]
#8 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x537e) [0x56328c5f37ae]
#9 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#10 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#11 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#12 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#13 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#14 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#15 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#16 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x1e20) [0x56328c5f0250]
#17 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#18 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#19 in /opt/anaconda/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x56328c59679b]
#20 in /opt/anaconda/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x56328c5eead0]
#21 in /opt/anaconda/bin/python(_PyFunction_FastCallDict+0x10b) [0x56328c53550b]
#22 in /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x56328c54cc43]
#23 in /opt/anaconda/bin/python(PyObject_Call+0x6e) [0x56328c54195e]
#24 in /opt/anaconda/bin/python(+0x223037) [0x56328c641037]
#25 in /opt/anaconda/bin/python(+0x1e3468) [0x56328c601468]
#26 in /lib/x86_64-linux-gnu/libpthread.so.0(+0x76db) [0x7f0c771626db]
#27 in /lib/x86_64-linux-gnu/libc.so.6(clone+0x3f) [0x7f0c76e8b88f]

nikiforov-sm · 2019-12-19T16:00:53Z

(base) root@nvidia-MLT:/opt/jupyter/cudf# ./print_env.sh

Click here to see environment details

 **git***
 commit 724e237f28bef239fb55b3c30a98215ec9380c7e (HEAD -> branch-0.12, origin/branch-0.12, origin/HEAD)
 Merge: fbb273b15 a31a57332
 Author: Mark Harris <mharris@nvidia.com>
 Date:   Thu Dec 19 13:13:52 2019 +1100

 Merge pull request #3629 from rgsl888prabhu/hash_map_test_fail

 [REVIEW] Fix hash map test failure
 **git submodules***
 -b165e1fb11eeea64ccf95053e40f2424312599cc thirdparty/cub
 -63f644be44201467e3938d59ed9d89cc8725c35d thirdparty/jitify
 -39125e0e476b960c2001f1ec76a3441335ff91b2 thirdparty/libcudacxx

 ***OS Information***
 DISTRIB_ID=Ubuntu
 DISTRIB_RELEASE=18.04
 DISTRIB_CODENAME=bionic
 DISTRIB_DESCRIPTION="Ubuntu 18.04.3 LTS"
 NAME="Ubuntu"
 VERSION="18.04.3 LTS (Bionic Beaver)"
 ID=ubuntu
 ID_LIKE=debian
 PRETTY_NAME="Ubuntu 18.04.3 LTS"
 VERSION_ID="18.04"
 HOME_URL="https://www.ubuntu.com/"
 SUPPORT_URL="https://help.ubuntu.com/"
 BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
 PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
 VERSION_CODENAME=bionic
 UBUNTU_CODENAME=bionic
 Linux nvidia-MLT.gksm.local 4.15.0-65-generic #74-Ubuntu SMP Tue Sep 17 17:06:04 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux

 ***GPU Information***
 Thu Dec 19 18:56:17 2019
 +-----------------------------------------------------------------------------+
 | NVIDIA-SMI 410.129      Driver Version: 410.129      CUDA Version: 10.1     |
 |-------------------------------+----------------------+----------------------+
 | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
 | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
 |===============================+======================+======================|
 |   0  Tesla V100-SXM3...  On   | 00000000:34:00.0 Off |                    0 |
 | N/A   32C    P0    66W / 350W |   1016MiB / 32480MiB |      0%      Default |
 +-------------------------------+----------------------+----------------------+
 |   1  Tesla V100-SXM3...  On   | 00000000:36:00.0 Off |                    0 |
 | N/A   34C    P0    66W / 350W |   1014MiB / 32480MiB |      0%      Default |
 +-------------------------------+----------------------+----------------------+
 |   2  Tesla V100-SXM3...  On   | 00000000:39:00.0 Off |                    0 |
 | N/A   37C    P0    66W / 350W |   1014MiB / 32480MiB |      0%      Default |
 +-------------------------------+----------------------+----------------------+
 |   3  Tesla V100-SXM3...  On   | 00000000:3B:00.0 Off |                    0 |
 | N/A   39C    P0    65W / 350W |   1014MiB / 32480MiB |      0%      Default |
 +-------------------------------+----------------------+----------------------+
 |   4  Tesla V100-SXM3...  On   | 00000000:57:00.0 Off |                    0 |
 | N/A   34C    P0    68W / 350W |   1014MiB / 32480MiB |      0%      Default |
 +-------------------------------+----------------------+----------------------+
 |   5  Tesla V100-SXM3...  On   | 00000000:59:00.0 Off |                    0 |
 | N/A   38C    P0    67W / 350W |   1014MiB / 32480MiB |      0%      Default |
 +-------------------------------+----------------------+----------------------+
 |   6  Tesla V100-SXM3...  On   | 00000000:5C:00.0 Off |                    0 |
 | N/A   34C    P0    67W / 350W |   1014MiB / 32480MiB |      0%      Default |
 +-------------------------------+----------------------+----------------------+
 |   7  Tesla V100-SXM3...  On   | 00000000:5E:00.0 Off |                    0 |
 | N/A   39C    P0    68W / 350W |   1014MiB / 32480MiB |      0%      Default |
 +-------------------------------+----------------------+----------------------+
 |   8  Tesla V100-SXM3...  On   | 00000000:B7:00.0 Off |                    0 |
 | N/A   33C    P0    78W / 350W |   6454MiB / 32480MiB |      0%      Default |
 +-------------------------------+----------------------+----------------------+
 |   9  Tesla V100-SXM3...  On   | 00000000:B9:00.0 Off |                    0 |
 | N/A   34C    P0    80W / 350W |   6611MiB / 32480MiB |      0%      Default |
 +-------------------------------+----------------------+----------------------+
 |  10  Tesla V100-SXM3...  On   | 00000000:BC:00.0 Off |                    0 |
 | N/A   41C    P0    82W / 350W |   6043MiB / 32480MiB |      0%      Default |
 +-------------------------------+----------------------+----------------------+
 |  11  Tesla V100-SXM3...  On   | 00000000:BE:00.0 Off |                    0 |
 | N/A   41C    P0    81W / 350W |   6303MiB / 32480MiB |      0%      Default |
 +-------------------------------+----------------------+----------------------+
 |  12  Tesla V100-SXM3...  On   | 00000000:E0:00.0 Off |                    0 |
 | N/A   34C    P0    78W / 350W |   6303MiB / 32480MiB |      0%      Default |
 +-------------------------------+----------------------+----------------------+
 |  13  Tesla V100-SXM3...  On   | 00000000:E2:00.0 Off |                    0 |
 | N/A   33C    P0    82W / 350W |   6045MiB / 32480MiB |      0%      Default |
 +-------------------------------+----------------------+----------------------+
 |  14  Tesla V100-SXM3...  On   | 00000000:E5:00.0 Off |                    0 |
 | N/A   41C    P0    82W / 350W |   6127MiB / 32480MiB |      1%      Default |
 +-------------------------------+----------------------+----------------------+
 |  15  Tesla V100-SXM3...  On   | 00000000:E7:00.0 Off |                    0 |
 | N/A   42C    P0    82W / 350W |   7755MiB / 32480MiB |      1%      Default |
 +-------------------------------+----------------------+----------------------+

 +-----------------------------------------------------------------------------+
 | Processes:                                                       GPU Memory |
 |  GPU       PID   Type   Process name                             Usage      |
 |=============================================================================|
 +-----------------------------------------------------------------------------+

 ***CPU***
 Architecture:        x86_64
 CPU op-mode(s):      32-bit, 64-bit
 Byte Order:          Little Endian
 CPU(s):              96
 On-line CPU(s) list: 0-95
 Thread(s) per core:  2
 Core(s) per socket:  24
 Socket(s):           2
 NUMA node(s):        2
 Vendor ID:           GenuineIntel
 CPU family:          6
 Model:               85
 Model name:          Intel(R) Xeon(R) Platinum 8168 CPU @ 2.70GHz
 Stepping:            4
 CPU MHz:             2274.754
 CPU max MHz:         3700.0000
 CPU min MHz:         1200.0000
 BogoMIPS:            5400.00
 Virtualization:      VT-x
 L1d cache:           32K
 L1i cache:           32K
 L2 cache:            1024K
 L3 cache:            33792K
 NUMA node0 CPU(s):   0-23,48-71
 NUMA node1 CPU(s):   24-47,72-95
 Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req pku ospke md_clear flush_l1d

 ***CMake***
 /opt/anaconda/bin/cmake
 cmake version 3.14.5

 CMake suite maintained and supported by Kitware (kitware.com/cmake).

 ***g++***
 /usr/bin/g++
 g++ (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0
 Copyright (C) 2017 Free Software Foundation, Inc.
 This is free software; see the source for copying conditions.  There is NO
 warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.


 ***nvcc***
 /usr/local/cuda/bin/nvcc
 nvcc: NVIDIA (R) Cuda compiler driver
 Copyright (c) 2005-2019 NVIDIA Corporation
 Built on Sun_Jul_28_19:07:16_PDT_2019
 Cuda compilation tools, release 10.1, V10.1.243

 ***Python***
 /opt/anaconda/bin/python
 Python 3.7.3

 ***Environment Variables***
 PATH                            : /opt/anaconda/bin:/opt/anaconda/condabin:/opt/anaconda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
 LD_LIBRARY_PATH                 : /opt/oracle/instantclient_19_3
 NUMBAPRO_NVVM                   :
 NUMBAPRO_LIBDEVICE              :
 CONDA_PREFIX                    : /opt/anaconda
 PYTHON_PATH                     :

 ***conda packages***
 /opt/anaconda/bin/conda
 # packages in environment at /opt/anaconda:
 #
 # Name                    Version                   Build  Channel
 _anaconda_depends         2019.03                  py37_0
 _ipyw_jlab_nb_ext_conf    0.1.0                    py37_0
 _libgcc_mutex             0.1                        main    conda-forge
 alabaster                 0.7.12                   py37_0
 anaconda                  custom                   py37_1
 anaconda-client           1.7.2                    py37_0
 anaconda-navigator        1.9.7                    py37_0
 anaconda-project          0.8.2                    py37_0
 arrow-cpp                 0.15.0           py37h5ac5442_0    conda-forge
 asn1crypto                0.24.0                   py37_0
 astroid                   2.2.5                    py37_0
 astropy                   3.1.2            py37h7b6447c_0
 atomicwrites              1.3.0                    py37_1
 attrs                     19.1.0                   py37_1
 babel                     2.6.0                    py37_0
 backcall                  0.1.0                    py37_0
 backports                 1.0                      py37_1
 backports.os              0.1.1                    py37_0
 backports.shutil_get_terminal_size 1.0.0                    py37_2
 beautifulsoup4            4.7.1                    py37_1
 bitarray                  0.8.3            py37h14c3975_0
 bkcharts                  0.2                      py37_0
 blas                      2.14                   openblas    conda-forge
 bleach                    3.1.0                    py37_0
 blosc                     1.15.0               hd408876_0
 bokeh                     1.0.4                    py37_0
 boost-cpp                 1.70.0               ha2d47e9_1    conda-forge
 boto                      2.49.0                   py37_0
 bottleneck                1.2.1            py37h035aef0_1
 brotli                    1.0.7             he1b5a44_1000    conda-forge
 bzip2                     1.0.8                h516909a_2    conda-forge
 c-ares                    1.15.0            h516909a_1001    conda-forge
 ca-certificates           2019.11.28           hecc5488_0    conda-forge
 cairo                     1.14.12              h8948797_3
 certifi                   2019.11.28               py37_0    conda-forge
 cffi                      1.12.2           py37h2e261b9_1
 chardet                   3.0.4                    py37_1
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 conda-build               3.17.8                   py37_0
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 cx_oracle                 7.3.0            py37h516909a_0    conda-forge
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vishalmehta1991 · 2019-12-20T09:52:09Z

@nikiforov-sm sorry am not able to understand the issue here ? are you building cudf ??

For building cuml you dont need to build cudf. you can use the one from conda

nikiforov-sm · 2019-12-20T14:16:49Z

We have no errors at build routines.
Currently we have a problem with script from the top of page:

Traceback (most recent call last):
  File "", line 1, in 
  File "/opt/anaconda/lib/python3.7/site-packages/cuml/dask/ensemble/randomforestregressor.py", line 403, in predict
    raise_exception_from_futures(futures)
  File "/opt/anaconda/lib/python3.7/site-packages/cuml/dask/common/utils.py", line 131, in raise_exception_from_futures
    len(errs), len(futures), ", ".join(map(str, errs))
RuntimeError: 8 of 8 worker jobs failed: Exception occured! file=/opt/jupyter/cuml/cpp/src/randomforest/randomforest.cu line=324: TREELITE FAIL: call='TreeliteModelBuilderCommitModel(model_builder, model)'. Reason:[15:35:18] /opt/jupyter/cuml/cpp/build/treelite/src/treelite/src/frontend/builder.cc:440: Impossible thing happened: model has no leaf node!

Stack trace returned 10 entries:
[bt] (0) /opt/anaconda/lib/libcuml++.so(dmlc::StackTrace[abi:cxx11]()+0x17f) [0x7f173d565d3f]
[bt] (1) /opt/anaconda/lib/libcuml++.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x39) [0x7f173d5670b9]
[bt] (2) /opt/anaconda/lib/libcuml++.so(treelite::frontend::ModelBuilder::CommitModel(treelite::Model*)+0x3a0d) [0x7f173d5830cd]
[bt] (3) /opt/anaconda/lib/libcuml++.so(TreeliteModelBuilderCommitModel+0x146) [0x7f173d562926]
[bt] (4) /opt/anaconda/lib/libcuml++.so(void ML::build_treelite_forest(void**, ML::RandomForestMetaData const*, int, int, std::vector >&)+0x2fa) [0x7f173d38a49a]
[bt] (5) /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x1d328) [0x7f1749093328]
[bt] (6) /opt/anaconda/bin/python(_PyObject_FastCallDict+0x9f) [0x55ce6e2c59cf]
[bt] (7) /opt/anaconda/bin/python(_PyObject_Call_Prepend+0x63) [0x55ce6e2dcc43]
[bt] (8) /opt/anaconda/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x18072) [0x7f174908e072]
[bt] (9) /opt/anaconda/bin/python(_PyObject_FastCallKeywords+0x49b) [0x55ce6e327d2b]

vishalmehta1991 · 2019-12-20T14:22:12Z

can you try without dask ? like this. I was able to train at depth of 30-50.

import numpy as np
import cudf as pd

max_depth = 30
n_trees = 100
n_streams = 10

df = pd.read_csv('test.csv').drop('Unnamed: 0', axis=1)
X = df.drop(['C2'],1).astype(np.float32)
y = df['C2'].astype(np.int32)

X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y,
test_size=0.2)

from cuml.ensemble import RandomForestRegressor as cumlRFR
cuml_model = cumlRFR(max_depth=max_depth, n_estimators=n_trees,
n_streams=n_streams,n_bins=16,split_algo=0)
cuml_model.fit(X_train, y_train)
cuml_y_pred = cuml_model.predict(X_test,predict_model='GPU')
print("CuML accuracy: ", mean_squared_error(y_test, cuml_y_pred))

nikiforov-sm · 2019-12-22T03:57:28Z

@vishalmehta1991 Thank you.
Without dask works fine.
How can we use dask now?

With dask we have errors:

distributed.nanny - ERROR - Failed while trying to start worker process: Could not serialize object of type RandomForestRegressor.
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 38, in dumps
    result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/serialize.py", line 191, in serialize
    header, frames = dumps(x, context=context) if wants_context else dumps(x)
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/serialize.py", line 58, in pickle_dumps
    return {"serializer": "pickle"}, [pickle.dumps(x)]
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 51, in dumps
    return cloudpickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "/opt/anaconda/lib/python3.7/site-packages/cloudpickle/cloudpickle.py", line 952, in dumps
    cp.dump(obj)
  File "/opt/anaconda/lib/python3.7/site-packages/cloudpickle/cloudpickle.py", line 267, in dump
    return Pickler.dump(self, obj)
  File "/opt/anaconda/lib/python3.7/pickle.py", line 437, in dump
    self.save(obj)
  File "/opt/anaconda/lib/python3.7/pickle.py", line 524, in save
    rv = reduce(self.proto)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

distributed.nanny - ERROR - Failed while trying to start worker process: Could not serialize object of type RandomForestRegressor.
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 38, in dumps
    result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/serialize.py", line 191, in serialize
    header, frames = dumps(x, context=context) if wants_context else dumps(x)
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/serialize.py", line 58, in pickle_dumps
    return {"serializer": "pickle"}, [pickle.dumps(x)]
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 51, in dumps
    return cloudpickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "/opt/anaconda/lib/python3.7/site-packages/cloudpickle/cloudpickle.py", line 952, in dumps
    cp.dump(obj)
  File "/opt/anaconda/lib/python3.7/site-packages/cloudpickle/cloudpickle.py", line 267, in dump
    return Pickler.dump(self, obj)
  File "/opt/anaconda/lib/python3.7/pickle.py", line 437, in dump
    self.save(obj)
  File "/opt/anaconda/lib/python3.7/pickle.py", line 524, in save
    rv = reduce(self.proto)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

distributed.nanny - ERROR - Failed while trying to start worker process: Could not serialize object of type RandomForestRegressor.
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 38, in dumps
    result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/serialize.py", line 191, in serialize
    header, frames = dumps(x, context=context) if wants_context else dumps(x)
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/serialize.py", line 58, in pickle_dumps
    return {"serializer": "pickle"}, [pickle.dumps(x)]
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 51, in dumps
    return cloudpickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "/opt/anaconda/lib/python3.7/site-packages/cloudpickle/cloudpickle.py", line 952, in dumps
    cp.dump(obj)
  File "/opt/anaconda/lib/python3.7/site-packages/cloudpickle/cloudpickle.py", line 267, in dump
    return Pickler.dump(self, obj)
  File "/opt/anaconda/lib/python3.7/pickle.py", line 437, in dump
    self.save(obj)
  File "/opt/anaconda/lib/python3.7/pickle.py", line 524, in save
    rv = reduce(self.proto)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

distributed.nanny - ERROR - Failed while trying to start worker process: Could not serialize object of type RandomForestRegressor.
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 38, in dumps
    result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/serialize.py", line 191, in serialize
    header, frames = dumps(x, context=context) if wants_context else dumps(x)
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/serialize.py", line 58, in pickle_dumps
    return {"serializer": "pickle"}, [pickle.dumps(x)]
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 51, in dumps
    return cloudpickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "/opt/anaconda/lib/python3.7/site-packages/cloudpickle/cloudpickle.py", line 952, in dumps
    cp.dump(obj)
  File "/opt/anaconda/lib/python3.7/site-packages/cloudpickle/cloudpickle.py", line 267, in dump
    return Pickler.dump(self, obj)
  File "/opt/anaconda/lib/python3.7/pickle.py", line 437, in dump
    self.save(obj)
  File "/opt/anaconda/lib/python3.7/pickle.py", line 524, in save
    rv = reduce(self.proto)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

distributed.nanny - ERROR - Failed while trying to start worker process: Could not serialize object of type RandomForestRegressor.
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 38, in dumps
    result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/serialize.py", line 191, in serialize
    header, frames = dumps(x, context=context) if wants_context else dumps(x)
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/serialize.py", line 58, in pickle_dumps
    return {"serializer": "pickle"}, [pickle.dumps(x)]
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 51, in dumps
    return cloudpickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "/opt/anaconda/lib/python3.7/site-packages/cloudpickle/cloudpickle.py", line 952, in dumps
    cp.dump(obj)
  File "/opt/anaconda/lib/python3.7/site-packages/cloudpickle/cloudpickle.py", line 267, in dump
    return Pickler.dump(self, obj)
  File "/opt/anaconda/lib/python3.7/pickle.py", line 437, in dump
    self.save(obj)
  File "/opt/anaconda/lib/python3.7/pickle.py", line 524, in save
    rv = reduce(self.proto)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

distributed.nanny - ERROR - Failed while trying to start worker process: Could not serialize object of type RandomForestRegressor.
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 38, in dumps
    result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/serialize.py", line 191, in serialize
    header, frames = dumps(x, context=context) if wants_context else dumps(x)
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/serialize.py", line 58, in pickle_dumps
    return {"serializer": "pickle"}, [pickle.dumps(x)]
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 51, in dumps
    return cloudpickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "/opt/anaconda/lib/python3.7/site-packages/cloudpickle/cloudpickle.py", line 952, in dumps
    cp.dump(obj)
  File "/opt/anaconda/lib/python3.7/site-packages/cloudpickle/cloudpickle.py", line 267, in dump
    return Pickler.dump(self, obj)
  File "/opt/anaconda/lib/python3.7/pickle.py", line 437, in dump
    self.save(obj)
  File "/opt/anaconda/lib/python3.7/pickle.py", line 524, in save
    rv = reduce(self.proto)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

distributed.nanny - ERROR - Failed while trying to start worker process: Could not serialize object of type RandomForestRegressor.
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 38, in dumps
    result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/serialize.py", line 191, in serialize
    header, frames = dumps(x, context=context) if wants_context else dumps(x)
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/serialize.py", line 58, in pickle_dumps
    return {"serializer": "pickle"}, [pickle.dumps(x)]
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 51, in dumps
    return cloudpickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "/opt/anaconda/lib/python3.7/site-packages/cloudpickle/cloudpickle.py", line 952, in dumps
    cp.dump(obj)
  File "/opt/anaconda/lib/python3.7/site-packages/cloudpickle/cloudpickle.py", line 267, in dump
    return Pickler.dump(self, obj)
  File "/opt/anaconda/lib/python3.7/pickle.py", line 437, in dump
    self.save(obj)
  File "/opt/anaconda/lib/python3.7/pickle.py", line 524, in save
    rv = reduce(self.proto)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

distributed.nanny - ERROR - Failed while trying to start worker process: Could not serialize object of type RandomForestRegressor.
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 38, in dumps
    result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/serialize.py", line 191, in serialize
    header, frames = dumps(x, context=context) if wants_context else dumps(x)
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/serialize.py", line 58, in pickle_dumps
    return {"serializer": "pickle"}, [pickle.dumps(x)]
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 51, in dumps
    return cloudpickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "/opt/anaconda/lib/python3.7/site-packages/cloudpickle/cloudpickle.py", line 952, in dumps
    cp.dump(obj)
  File "/opt/anaconda/lib/python3.7/site-packages/cloudpickle/cloudpickle.py", line 267, in dump
    return Pickler.dump(self, obj)
  File "/opt/anaconda/lib/python3.7/pickle.py", line 437, in dump
    self.save(obj)
  File "/opt/anaconda/lib/python3.7/pickle.py", line 524, in save
    rv = reduce(self.proto)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

distributed.nanny - WARNING - Restarting worker
distributed.nanny - WARNING - Restarting worker
distributed.nanny - WARNING - Restarting worker
distributed.nanny - WARNING - Restarting worker
distributed.nanny - WARNING - Restarting worker
ERROR:Task exception was never retrieved
future:  exception=TypeError('exceptions must derive from BaseException')>
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/asyncio/tasks.py", line 603, in _wrap_awaitable
    return (yield from awaitable.__await__())
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 251, in start
    response = await self.instantiate()
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 334, in instantiate
    result = await self.process.start()
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 528, in start
    msg = await self._wait_until_connected(uid)
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 642, in _wait_until_connected
    raise msg
TypeError: exceptions must derive from BaseException
ERROR:Task exception was never retrieved
future:  exception=TypeError('exceptions must derive from BaseException')>
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/asyncio/tasks.py", line 603, in _wrap_awaitable
    return (yield from awaitable.__await__())
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 251, in start
    response = await self.instantiate()
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 334, in instantiate
    result = await self.process.start()
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 528, in start
    msg = await self._wait_until_connected(uid)
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 642, in _wait_until_connected
    raise msg
TypeError: exceptions must derive from BaseException
ERROR:Task exception was never retrieved
future:  exception=TypeError('exceptions must derive from BaseException')>
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/asyncio/tasks.py", line 603, in _wrap_awaitable
    return (yield from awaitable.__await__())
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 251, in start
    response = await self.instantiate()
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 334, in instantiate
    result = await self.process.start()
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 528, in start
    msg = await self._wait_until_connected(uid)
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 642, in _wait_until_connected
    raise msg
TypeError: exceptions must derive from BaseException
ERROR:Task exception was never retrieved
future:  exception=TypeError('exceptions must derive from BaseException')>
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/asyncio/tasks.py", line 603, in _wrap_awaitable
    return (yield from awaitable.__await__())
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 251, in start
    response = await self.instantiate()
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 334, in instantiate
    result = await self.process.start()
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 528, in start
    msg = await self._wait_until_connected(uid)
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 642, in _wait_until_connected
    raise msg
TypeError: exceptions must derive from BaseException
ERROR:Task exception was never retrieved
future:  exception=TypeError('exceptions must derive from BaseException')>
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/asyncio/tasks.py", line 603, in _wrap_awaitable
    return (yield from awaitable.__await__())
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 251, in start
    response = await self.instantiate()
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 334, in instantiate
    result = await self.process.start()
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 528, in start
    msg = await self._wait_until_connected(uid)
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 642, in _wait_until_connected
    raise msg
TypeError: exceptions must derive from BaseException
ERROR:Task exception was never retrieved
future:  exception=TypeError('exceptions must derive from BaseException')>
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/asyncio/tasks.py", line 603, in _wrap_awaitable
    return (yield from awaitable.__await__())
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 251, in start
    response = await self.instantiate()
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 334, in instantiate
    result = await self.process.start()
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 528, in start
    msg = await self._wait_until_connected(uid)
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 642, in _wait_until_connected
    raise msg
TypeError: exceptions must derive from BaseException
ERROR:Task exception was never retrieved
future:  exception=TypeError('exceptions must derive from BaseException')>
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/asyncio/tasks.py", line 603, in _wrap_awaitable
    return (yield from awaitable.__await__())
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 251, in start
    response = await self.instantiate()
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 334, in instantiate
    result = await self.process.start()
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 528, in start
    msg = await self._wait_until_connected(uid)
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 642, in _wait_until_connected
    raise msg
TypeError: exceptions must derive from BaseException
ERROR:Task exception was never retrieved
future:  exception=TypeError('exceptions must derive from BaseException')>
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/asyncio/tasks.py", line 603, in _wrap_awaitable
    return (yield from awaitable.__await__())
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 251, in start
    response = await self.instantiate()
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 334, in instantiate
    result = await self.process.start()
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 528, in start
    msg = await self._wait_until_connected(uid)
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/nanny.py", line 642, in _wait_until_connected
    raise msg
TypeError: exceptions must derive from BaseException
distributed.nanny - WARNING - Restarting worker
distributed.nanny - WARNING - Restarting worker
distributed.nanny - WARNING - Restarting worker

nikiforov-sm · 2019-12-27T09:14:40Z

Hi,

After rebuild from sources

from dask_cuda import LocalCUDACluster
from dask.distributed import Client
cluster = LocalCUDACluster(threads_per_worker=1)
if 'c' in globals():
    c.close()
c = Client(cluster)
c.close()

return errors:

distributed.nanny - ERROR - Failed while trying to start worker process: Could not serialize object of type RandomForestRegressor.
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 38, in dumps
    result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

Could you help us?

nikiforov-sm · 2019-12-27T09:25:33Z

dask-cuda from rapidsai-nightly:

(base) root@nvidia-MLT:/opt/jupyter/cuml/python# conda list dask-cuda # packages in environment at /opt/anaconda:

Name Version Build Channel

dask-cuda 0.12.0a191218 py37_36 rapidsai-nightly

vishalmehta1991 · 2020-01-22T08:59:07Z

@nikiforov-sm @oyilmaz-nvidia
I was able to run dask model like this

Shard the data across all workers

X_train_dask = dask_cudf.from_cudf(X_train, npartitions=n_partitions)
y_train_dask = dask_cudf.from_cudf(y_train, npartitions=n_partitions)
X_train_dask, y_train_dask = dask_utils.persist_across_workers(c, [X_train_dask, y_train_dask], workers=workers)

Build and train the model

dask_model = daskRFR(max_depth=max_depth, n_estimators=n_trees,
n_streams=n_streams,n_bins=16,split_algo=0,split_criterion=2)

dask_model.fit(X_train_dask, y_train_dask)
cuml_y_pred_dask = dask_model.predict(X_test.as_matrix()) #X_test is a cudf dataframe

nikiforov-sm · 2020-01-22T09:28:03Z

@vishalmehta1991
Is daskRFR an alias for RandomForestRegressor from cuml.dask.ensemble in your example?
Could you, please, provide full script?

vishalmehta1991 · 2020-01-22T10:36:13Z

yes daskRFR is rf regressor from cuml.dask.ensemble
here is the full:

Dask RF

from cuml.dask.ensemble import RandomForestRegressor as daskRFR
from cuml.dask.common import utils as dask_utils
from dask.distributed import Client, wait
from dask_cuda import LocalCUDACluster
import dask_cudf

Start cluster

cluster = LocalCUDACluster(threads_per_worker=1, n_workers=n_partitions)
c = Client(cluster)
workers = c.has_what().keys()

Shared the data across all workers

X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y,
test_size=0.2)
X_train_dask = dask_cudf.from_cudf(X_train, npartitions=n_partitions)
y_train_dask = dask_cudf.from_cudf(y_train, npartitions=n_partitions)
X_train_dask, y_train_dask = dask_utils.persist_across_workers(c, [X_train_dask, y_train_dask], workers=workers)

Build and train the model

dask_model = daskRFR(max_depth=max_depth, n_estimators=n_trees,
n_streams=n_streams,n_bins=16,split_algo=0,split_criterion=2)

dask_model.fit(X_train_dask, y_train_dask)
cuml_y_pred_dask = dask_model.predict(X_test.as_matrix()) #X_test is a cudf dataframe

nikiforov-sm · 2020-01-27T11:28:46Z

Hi, thank you for update.
We have errors after these few rows:

from dask_cuda import LocalCUDACluster
from dask.distributed import Client
n_partitions = 8
cluster = LocalCUDACluster(threads_per_worker=1, n_workers=n_partitions)
c = Client(cluster)

distributed.nanny - WARNING - Restarting worker
distributed.nanny - ERROR - Failed while trying to start worker process: Could not serialize object of type RandomForestRegressor.
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 38, in dumps
    result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

vishalmehta1991 · 2020-01-27T16:56:20Z

@nikiforov-sm did you try with the code-freezed 0.12 branch ?
Also seem like some pickle error @Salonijain27 ?

nikiforov-sm · 2020-01-28T05:54:47Z

@vishalmehta1991 No, I didn't.
We've installed libraries from file of environment from the repository only without changing any version.

rmccorm4 · 2020-01-31T19:53:52Z

@vishalmehta1991 I'm trying to help get this resolved, but doesn't seem to work for me on 0.12 branch either. Can you provide detailed steps of what you're doing if you're able to run this code successfully?

I tried building from CUML branch-0.12 per #1467 (comment), but I'm still hitting errors on the code from the original post: #1467 (comment) and also on Vishal's snippet here: #1467 (comment)

Building 0.12 branch

# Use RAPIDS container for easier reproducibility
nvidia-docker run -it -v `pwd`:/mnt --workdir=/mnt nvcr.io/nvidia/rapidsai/rapidsai:0.11-cuda10.0-runtime-ubuntu18.04
# Clone CUML source
git clone https://github.com/rapidsai/cuml
# Switch to 0.12 branch
git checkout branch-0.12
# Build dev env for 0.12 branch
conda env create --name cuml_dev --file /mnt/cuml/conda/environments/cuml_dev_cuda10.0.yml
conda activate cuml_dev
# Install cuml 0.12
conda install cuml

Verify version:

(cuml_dev) root@f99f97486476:/mnt# python -c "import cuml; print(cuml.__version__)"
0.12.0a+773.ge764252

Original Code (Error)

There's an error like this from each of the 8 workers:

(cuml_dev) $ python original.py
...
SKLearn accuracy:   0.0856196149694842
distributed.worker - WARNING -  Compute Failed
Function:  _predict
args:      (RandomForestRegressor(n_estimators=4, max_depth=20, handle=<cuml.common.handle.Handle object at 0x7f212eb9b770>, max_features='auto', n_bins=8, n_streams=8, split_algo=1, split_criterion=2, bootstrap=True, bootstrap_features=False, verbose=False, min_rows_per_node=2, rows_sample=1.0, max_leaves=-1, accuracy_metric='mse', min_impurity_decrease=0.0, quantile_per_tree=False, seed=0), array([[0.9405078 , 0.5626202 , 0.82645825, ..., 0.45711115, 0.80462799,
        0.92827166],
       [0.85446839, 0.53313684, 0.85503426, ..., 0.96904807, 0.66100485,
        0.93062759],
       [0.1721737 , 0.73070096, 0.23297057, ..., 0.57237385, 0.33928629,
        0.13889585],
       ...,
       [0.65332578, 0.74442212, 0.20840173, ..., 0.08406665, 0.33502194,
        0.94996344],
       [0.10918214, 0.75263554, 0.56264772, ..., 0.65330256, 0.0200657 ,
        0.02755087],
       [0.781481  , 0.73409352, 0.2241026 , ..., 0.55295024, 0.51877916,
        0.88945637]]), 0.507484466418739)
kwargs:    {}
Exception: RuntimeError("Exception occured! file=/conda/conda-bld/libcuml_1580378874315/work/cpp/src/randomforest/randomforest.cu line=324: TREELITE FAIL: call='TreeliteModelBuilderCommitModel(model_builder, model)'. Reason:[19:30:28] /conda/conda-bld/libcuml_1580378874315/work/cpp/build/treelite/src/treelite/src/frontend/builder.cc:440: Impossible thing happened: model has no leaf node!\n\nStack trace returned 10 entries:\n[bt] (0) /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/common/../../../../libcuml++.so(dmlc::StackTrace[abi:cxx11]()+0x1bc) [0x7f203f2794bc]\n[bt] (1) /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/common/../../../../libcuml++.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x28) [0x7f203f27a818]\n[bt] (2) /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/common/../../../../libcuml++.so(treelite::frontend::ModelBuilder::CommitModel(treelite::Model*)+0x4136) [0x7f203f297fa6]\n[bt] (3) /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/common/../../../../libcuml++.so(TreeliteModelBuilderCommitModel+0x13b) [0x7f203f27626b]\n[bt] (4) /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/common/../../../../libcuml++.so(void ML::build_treelite_forest<float, float>(void**, ML::RandomForestMetaData<float, float> const*, int, int, std::vector<unsigned char, std::allocator<unsigned char> >&)+0x49f) [0x7f203f083c1f]\n[bt] (5) /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x17c7f) [0x7f2138027c7f]\n[bt] (6) /opt/conda/envs/cuml_dev/bin/python(_PyObject_FastCallDict+0x9f) [0x556fafc50c6f]\n[bt] (7) /opt/conda/envs/cuml_dev/bin/python(_PyObject_Call_Prepend+0x63) [0x556fafc70313]\n[bt] (8) /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x19388) [0x7f2138029388]\n[bt] (9) /opt/conda/envs/cuml_dev/bin/python(_PyObject_FastCallKeywords+0x49b) [0x556fafcbb85b]\n\n\n\nObtained 28 stack frames\n#0 in /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/common/../../../../libcuml++.so(_ZN8MLCommon9Exception16collectCallStackEv+0x3e) [0x7f203ee5a32e]\n#1 in /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/common/../../../../libcuml++.so(_ZN8MLCommon9ExceptionC2ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE+0x80) [0x7f203ee5ae40]\n#2 in /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/common/../../../../libcuml++.so(_ZN2ML21build_treelite_forestIffEEvPPvPKNS_20RandomForestMetaDataIT_T0_EEiiRSt6vectorIhSaIhEE+0x909) [0x7f203f084089]\n#3 in /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x17c7f) [0x7f2138027c7f]\n#4 in /opt/conda/envs/cuml_dev/bin/python(_PyObject_FastCallDict+0x9f) [0x556fafc50c6f]\n#5 in /opt/conda/envs/cuml_dev/bin/python(_PyObject_Call_Prepend+0x63) [0x556fafc70313]\n#6 in /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x19388) [0x7f2138029388]\n#7 in /opt/conda/envs/cuml_dev/bin/python(_PyObject_FastCallKeywords+0x49b) [0x556fafcbb85b]\n#8 in /opt/conda/envs/cuml_dev/bin/python(_PyEval_EvalFrameDefault+0x5379) [0x556fafd100b9]\n#9 in /opt/conda/envs/cuml_dev/bin/python(_PyFunction_FastCallDict+0x10b) [0x556fafc5079b]\n#10 in /opt/conda/envs/cuml_dev/bin/python(_PyEval_EvalFrameDefault+0x1f4f) [0x556fafd0cc8f]\n#11 in /opt/conda/envs/cuml_dev/bin/python(_PyFunction_FastCallDict+0x10b) [0x556fafc5079b]\n#12 in /opt/conda/envs/cuml_dev/bin/python(_PyEval_EvalFrameDefault+0x1f4f) [0x556fafd0cc8f]\n#13 in /opt/conda/envs/cuml_dev/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x556fafca2f7b]\n#14 in /opt/conda/envs/cuml_dev/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x556fafd0b3e0]\n#15 in /opt/conda/envs/cuml_dev/bin/python(_PyFunction_FastCallDict+0x10b) [0x556fafc5079b]\n#16 in /opt/conda/envs/cuml_dev/bin/python(_PyEval_EvalFrameDefault+0x1f4f) [0x556fafd0cc8f]\n#17 in /opt/conda/envs/cuml_dev/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x556fafca2f7b]\n#18 in /opt/conda/envs/cuml_dev/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x556fafd0b3e0]\n#19 in /opt/conda/envs/cuml_dev/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x556fafca2f7b]\n#20 in /opt/conda/envs/cuml_dev/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x556fafd0b3e0]\n#21 in /opt/conda/envs/cuml_dev/bin/python(_PyFunction_FastCallDict+0x10b) [0x556fafc5079b]\n#22 in /opt/conda/envs/cuml_dev/bin/python(_PyObject_Call_Prepend+0x63) [0x556fafc70313]\n#23 in /opt/conda/envs/cuml_dev/bin/python(PyObject_Call+0x6e) [0x556fafc6206e]\n#24 in /opt/conda/envs/cuml_dev/bin/python(+0x224917) [0x556fafd5f917]\n#25 in /opt/conda/envs/cuml_dev/bin/python(+0x1e3368) [0x556fafd1e368]\n#26 in /lib/x86_64-linux-gnu/libpthread.so.0(+0x76db) [0x7f2177b786db]\n#27 in /lib/x86_64-linux-gnu/libc.so.6(clone+0x3f) [0x7f21778a188f]\n")

Vishal's Code Snippet (error)

import cuml
import cudf
import dask_cudf
import numpy as np
import pandas as pd
from dask_cuda import LocalCUDACluster
from dask.distributed import Client, wait
from cuml.dask.common import utils as dask_utils
from cuml.dask.ensemble import RandomForestRegressor as daskRFR

if __name__ == '__main__':
    # Start cluster
    n_partitions = 4
    cluster = LocalCUDACluster(threads_per_worker=1, n_workers=n_partitions)
    c = Client(cluster)
    workers = c.has_what().keys()

    # Desired parameters
    max_depth = 20
    n_trees = 30
    rows, cols = 10000, 74
    n_streams = len(workers)

    # Generate fake data for example's sake
    x = np.random.random((rows, cols))
    df = pd.DataFrame(x, columns=["C{}".format(i) for i in range(cols)])
    X = df.drop(['C2'],1).astype(np.float, 32) #.to_numpy().astype(np.float, 32)
    y = pd.DataFrame(df['C2'].astype(np.float, 32))
    """
    File "/opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/preprocessing/model_selection.py", line 251, in train_test_split
        return X_train, X_test, y_train, y_test
    UnboundLocalError: local variable 'X_train' referenced before assignment
    """
    X = cudf.DataFrame.from_pandas(X)
    y = cudf.DataFrame.from_pandas(y)

    # Shared the data across all workers
    X_train, X_test, y_train, y_test = cuml.preprocessing.model_selection.train_test_split(X, y,
            test_size=0.2)
    X_train_dask = dask_cudf.from_cudf(X_train, npartitions=n_partitions)
    y_train_dask = dask_cudf.from_cudf(y_train, npartitions=n_partitions)
    X_train_dask, y_train_dask = dask_utils.persist_across_workers(c, [X_train_dask, y_train_dask], workers=workers)

    # Build and train the model
    dask_model = daskRFR(max_depth=max_depth, n_estimators=n_trees,
            n_streams=n_streams,n_bins=16,split_algo=0,split_criterion=2)

    dask_model.fit(X_train_dask, y_train_dask)
    cuml_y_pred_dask = dask_model.predict(X_test.as_matrix()) #X_test is a cudf dataframe

I also get similar errors on each worker when running a slightly modified version of Vishal's example above:

(cuml_dev) $ python vishal.py
...
distributed.worker - WARNING -  Compute Failed
Function:  _predict
args:      (RandomForestRegressor(n_estimators=8, max_depth=20, handle=<cuml.common.handle.Handle object at 0x7f6da56ddb90>, max_features='auto', n_bins=16, n_streams=4, split_algo=0, split_criterion=2, bootstrap=True, bootstrap_features=False, verbose=False, min_rows_per_node=2, rows_sample=1.0, max_leaves=-1, accuracy_metric='mse', min_impurity_decrease=0.0, quantile_per_tree=False, seed=0), array([[0.51445416, 0.46609572, 0.40908901, ..., 0.801352  , 0.8571766 ,
        0.97171996],
       [0.52920684, 0.57777807, 0.38054712, ..., 0.02989455, 0.84592101,
        0.51838237],
       [0.77971376, 0.39299739, 0.53437106, ..., 0.65130207, 0.33598852,
        0.42132736],
       ...,
       [0.34358309, 0.06082903, 0.33059765, ..., 0.82576507, 0.36605432,
        0.81006008],
       [0.17678472, 0.55156779, 0.33741966, ..., 0.66669902, 0.11728904,
        0.61993128],
       [0.6684218 , 0.1548672 , 0.14697938, ..., 0.68550973, 0.75773336,
        0.66218533]]), 0.47515081126142533)
kwargs:    {}
Exception: RuntimeError("Exception occured! file=/conda/conda-bld/libcuml_1580378874315/work/cpp/src/randomforest/randomforest.cu line=324: TREELITE FAIL: call='TreeliteModelBuilderCommitModel(model_builder, model)'. Reason:[19:47:54] /conda/conda-bld/libcuml_1580378874315/work/cpp/build/treelite/src/treelite/src/frontend/builder.cc:440: Impossible thing happened: model has no leaf node!\n\nStack trace returned 10 entries:\n[bt] (0) /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/common/../../../../libcuml++.so(dmlc::StackTrace[abi:cxx11]()+0x1bc) [0x7f6e08dd94bc]\n[bt] (1) /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/common/../../../../libcuml++.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x28) [0x7f6e08dda818]\n[bt] (2) /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/common/../../../../libcuml++.so(treelite::frontend::ModelBuilder::CommitModel(treelite::Model*)+0x4136) [0x7f6e08df7fa6]\n[bt] (3) /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/common/../../../../libcuml++.so(TreeliteModelBuilderCommitModel+0x13b) [0x7f6e08dd626b]\n[bt] (4) /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/common/../../../../libcuml++.so(void ML::build_treelite_forest<float, float>(void**, ML::RandomForestMetaData<float, float> const*, int, int, std::vector<unsigned char, std::allocator<unsigned char> >&)+0x49f) [0x7f6e08be3c1f]\n[bt] (5) /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x17c7f) [0x7f6dbd0a6c7f]\n[bt] (6) /opt/conda/envs/cuml_dev/bin/python(_PyObject_FastCallDict+0x9f) [0x561c9d537c6f]\n[bt] (7) /opt/conda/envs/cuml_dev/bin/python(_PyObject_Call_Prepend+0x63) [0x561c9d557313]\n[bt] (8) /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x19388) [0x7f6dbd0a8388]\n[bt] (9) /opt/conda/envs/cuml_dev/bin/python(_PyObject_FastCallKeywords+0x49b) [0x561c9d5a285b]\n\n\n\nObtained 28 stack frames\n#0 in /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/common/../../../../libcuml++.so(_ZN8MLCommon9Exception16collectCallStackEv+0x3e) [0x7f6e089ba32e]\n#1 in /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/common/../../../../libcuml++.so(_ZN8MLCommon9ExceptionC2ERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE+0x80) [0x7f6e089bae40]\n#2 in /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/common/../../../../libcuml++.so(_ZN2ML21build_treelite_forestIffEEvPPvPKNS_20RandomForestMetaDataIT_T0_EEiiRSt6vectorIhSaIhEE+0x909) [0x7f6e08be4089]\n#3 in /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x17c7f) [0x7f6dbd0a6c7f]\n#4 in /opt/conda/envs/cuml_dev/bin/python(_PyObject_FastCallDict+0x9f) [0x561c9d537c6f]\n#5 in /opt/conda/envs/cuml_dev/bin/python(_PyObject_Call_Prepend+0x63) [0x561c9d557313]\n#6 in /opt/conda/envs/cuml_dev/lib/python3.7/site-packages/cuml/ensemble/randomforestregressor.cpython-37m-x86_64-linux-gnu.so(+0x19388) [0x7f6dbd0a8388]\n#7 in /opt/conda/envs/cuml_dev/bin/python(_PyObject_FastCallKeywords+0x49b) [0x561c9d5a285b]\n#8 in /opt/conda/envs/cuml_dev/bin/python(_PyEval_EvalFrameDefault+0x5379) [0x561c9d5f70b9]\n#9 in /opt/conda/envs/cuml_dev/bin/python(_PyFunction_FastCallDict+0x10b) [0x561c9d53779b]\n#10 in /opt/conda/envs/cuml_dev/bin/python(_PyEval_EvalFrameDefault+0x1f4f) [0x561c9d5f3c8f]\n#11 in /opt/conda/envs/cuml_dev/bin/python(_PyFunction_FastCallDict+0x10b) [0x561c9d53779b]\n#12 in /opt/conda/envs/cuml_dev/bin/python(_PyEval_EvalFrameDefault+0x1f4f) [0x561c9d5f3c8f]\n#13 in /opt/conda/envs/cuml_dev/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x561c9d589f7b]\n#14 in /opt/conda/envs/cuml_dev/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x561c9d5f23e0]\n#15 in /opt/conda/envs/cuml_dev/bin/python(_PyFunction_FastCallDict+0x10b) [0x561c9d53779b]\n#16 in /opt/conda/envs/cuml_dev/bin/python(_PyEval_EvalFrameDefault+0x1f4f) [0x561c9d5f3c8f]\n#17 in /opt/conda/envs/cuml_dev/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x561c9d589f7b]\n#18 in /opt/conda/envs/cuml_dev/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x561c9d5f23e0]\n#19 in /opt/conda/envs/cuml_dev/bin/python(_PyFunction_FastCallKeywords+0xfb) [0x561c9d589f7b]\n#20 in /opt/conda/envs/cuml_dev/bin/python(_PyEval_EvalFrameDefault+0x6a0) [0x561c9d5f23e0]\n#21 in /opt/conda/envs/cuml_dev/bin/python(_PyFunction_FastCallDict+0x10b) [0x561c9d53779b]\n#22 in /opt/conda/envs/cuml_dev/bin/python(_PyObject_Call_Prepend+0x63) [0x561c9d557313]\n#23 in /opt/conda/envs/cuml_dev/bin/python(PyObject_Call+0x6e) [0x561c9d54906e]\n#24 in /opt/conda/envs/cuml_dev/bin/python(+0x224917) [0x561c9d646917]\n#25 in /opt/conda/envs/cuml_dev/bin/python(+0x1e3368) [0x561c9d605368]\n#26 in /lib/x86_64-linux-gnu/libpthread.so.0(+0x76db) [0x7f6e2bdb06db]\n#27 in /lib/x86_64-linux-gnu/libc.so.6(clone+0x3f) [0x7f6e2bad988f]\n")

vishalmehta1991 · 2020-01-31T21:12:29Z

@rmccorm4 here is a full train code using dask as well as RF single gpu
dask_example.pdf

rmccorm4 · 2020-02-04T00:12:55Z

@nikiforov-sm does this above snippet work well for you? #1467 (comment)

If you're having trouble setting up env / building from source, using containers should make your life easier, for example the "Building 0.12 Branch" section of this comment: #1467 (comment)

Building 0.12 branch

# Use RAPIDS container for easier reproducibility
nvidia-docker run -it -v `pwd`:/mnt --workdir=/mnt nvcr.io/nvidia/rapidsai/rapidsai:0.11-cuda10.0-runtime-ubuntu18.04
# Clone CUML source
git clone https://github.com/rapidsai/cuml
# Switch to 0.12 branch
git checkout branch-0.12
# Build dev env for 0.12 branch
conda env create --name cuml_dev --file /mnt/cuml/conda/environments/cuml_dev_cuda10.0.yml
conda activate cuml_dev
# Install cuml 0.12
conda install cuml

This way you can try various configurations without messing up your host environment.

nikiforov-sm · 2020-02-04T05:38:37Z

@rmccorm4 We have multiple errors on row

cluster = LocalCUDACluster(threads_per_worker=1, n_workers=n_partitions)
c = Client(cluster)

like this:

distributed.nanny - WARNING - Restarting worker
distributed.nanny - ERROR - Failed while trying to start worker process: Could not serialize object of type RandomForestRegressor.
Traceback (most recent call last):
  File "/opt/anaconda/lib/python3.7/site-packages/distributed/protocol/pickle.py", line 38, in dumps
    result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
  File "cuml/ensemble/randomforestregressor.pyx", line 373, in cuml.ensemble.randomforestregressor.RandomForestRegressor.__getstate__
  File "cuml/ensemble/randomforestregressor.pyx", line 441, in cuml.ensemble.randomforestregressor.RandomForestRegressor._get_model_info
TypeError: an integer is required

rmccorm4 · 2020-02-04T22:43:20Z

@nikiforov-sm I didn't encounter this error when trying the scripts above. Can you reproduce this issue in a container?

nikiforov-sm · 2020-02-05T11:02:15Z

@rmccorm4

Can you reproduce this issue in a container?

We can't reproduce this issue in a container :) Thank you!

We have successfully trained model with max_depth<=28.
We have warnings like "distributed.nanny - WARNING - Restarting worker" with script hanging and without any train-result on max_depth > 28.

rmccorm4 · 2020-02-06T21:34:16Z

@vishalmehta1991 said he's tested depths up to 50 here: #1467 (comment)

So what's the root cause of this gap? Upper bound of max_depth=28 as opposed to >= 50?

vishalmehta1991 · 2020-02-07T10:11:17Z

@rmccorm4 I don't think its an RF issue. Seems to me its more of a dask thing.

nikiforov-sm · 2020-02-10T10:03:03Z

@rmccorm4 @vishalmehta1991 It looks like OOM error on train.
I can train model on 180GB dataset (36 features) with max_depth=19, but with max_depth=20 there are errors like:
RuntimeError: 12 of 16 worker jobs failed: RMM_ERROR_OUT_OF_MEMORY

If I use max_features='sqrt' to decrease max_features - I can train model with bigger max_depth (<=30) without any errors.

And by the way - there is no API to save trained RandomForestRegressor model like XXX.save_model(path)?

vishalmehta1991 · 2020-02-10T10:33:20Z

There is pickle support for RF. check example here
https://github.com/rapidsai/cuml/blob/branch-0.12/python/cuml/test/test_pickle.py

nikiforov-sm · 2020-02-10T14:18:06Z

@vishalmehta1991 Is pickle working for 12th version?

I have an error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
 in 
      5     with open(model_path, 'wb') as pf:
----> 6         pickle.dump(fitted, pf)
      7 except (TypeError, ValueError) as e:

TypeError: can't pickle dict_keys objects

rmccorm4 · 2020-02-24T20:50:46Z

Hi @nikiforov-sm ,

Can you share the full script that's causing the failure above? i.e. What is the fitted variable?

It looks like per vishal's post above, that the models can be pickled. It seems that your fitted variable is a dict_keys object, and maybe not a model object.

nikiforov-sm · 2020-02-26T05:17:48Z

Hi @rmccorm4 ,

raw_df = dask_cudf.read_csv(train_csv,dtype=['float32']*ncols, partitions=10000000)
raw_df = raw_df.persist()

df = raw_df[x_cols + ['KPI_0']]
X_train_dask, y_train_dask = dask_utils.persist_across_workers(c, [df[x_cols], df['KPI_0']], workers=workers)

cuml_model = cumlDaskRF(
                        max_depth=30, n_estimators=100,
                        n_streams=1,
                        n_bins=16,split_algo=0,split_criterion=2
                       )
fitted = cuml_model.fit(X_train_dask, y_train_dask)
wait(cuml_model.rfs)

I've tested with fitted model (variable fitted) and exact model (variable cuml_model) as in the example https://github.com/rapidsai/cuml/blob/branch-0.12/python/cuml/test/test_pickle.py:

model_path='/data/cuml.model'

with open(model_path, 'wb') as pf:
            pickle.dump(cuml_model, pf)

raise:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
 in 
      1 with open(model_path, 'wb') as pf:
----> 2     pickle.dump(cuml_model, pf)

TypeError: can't pickle dict_keys objects

Salonijain27 · 2020-02-26T05:37:58Z

We currently do not have the option to pickle dask RF models.

nikiforov-sm · 2020-03-13T10:16:37Z

Thank you!
We have successfully train dask cuml RF model with depth more than 30!
We can close this issue.

rmccorm4 · 2020-03-13T18:04:54Z

I checked out the issue and I believe the problem was that in 0.12 the option to build sparse representation of the cuML RF in FIL was not available to the user and the code would by default create a dense representation of cuML forest in FIL. This would cause the system to run out of memory when max_depth > 16.

This issue has been addressed and the fix has been merged into cuml-0.13. We now provide the user with the option of either creating a sparse or dense representation of the cuML forest in FIL by using the variable, fil_sparse_format. Furthermore, by default the sparse representation is used provided the value of the algo variable is right. If algo=auto or if algo=naïve then the sparse implementation is created else the dense implementation is created in FIL.

I ran the code that was provided in the file: repo.py and was able to successfully run it by using the nightly release of cuml.
In order to install cuml-0.13 nightly please run:

conda install -c rapidsai-nightly -c nvidia -c conda-forge \
    -c defaults cuml=0.13 python=3.7 cudatoolkit=10.0

FYI, I believe upgrading to CuML 0.13 as mentioned above was the solution, thanks @Salonijain27 !

rmccorm4 added ? - Needs Triage Need team to review and classify question Further information is requested labels Dec 10, 2019

teju85 mentioned this issue Dec 19, 2019

[BUG] Ridiculously huge effort needed for building cuML from source on a non-docker env! #1503

Closed

rmccorm4 mentioned this issue Jan 31, 2020

[FEA] Feature request: train_test_split to accept numpy inputs #1619

Closed

roclark mentioned this issue Feb 5, 2020

[QST] Debugging Random Forest model with no leaf nodes #1642

Closed

rmccorm4 closed this as completed Mar 13, 2020

[QST] Best practices to achieve greater max_depth and n_trees parameters in RandomForestRegressor #1467

[QST] Best practices to achieve greater max_depth and n_trees parameters in RandomForestRegressor #1467

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nikiforov-sm commented Dec 27, 2019

Name Version Build Channel

vishalmehta1991 commented Jan 22, 2020

Shard the data across all workers

Build and train the model

nikiforov-sm commented Jan 22, 2020

vishalmehta1991 commented Jan 22, 2020 • edited Loading

Dask RF

Start cluster

Shared the data across all workers

Build and train the model

nikiforov-sm commented Jan 27, 2020 • edited Loading

vishalmehta1991 commented Jan 27, 2020

nikiforov-sm commented Jan 28, 2020

rmccorm4 commented Jan 31, 2020 • edited Loading

Building 0.12 branch

Verify version:

Original Code (Error)

Vishal's Code Snippet (error)

vishalmehta1991 commented Jan 31, 2020 • edited Loading

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Building 0.12 branch

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