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rapidsai / cudf

cuDF - GPU DataFrame Library
https://docs.rapids.ai/api/cudf/stable/
Apache License 2.0
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RuntimeError: radix_sort: failed on 2nd step: cudaErrorInvalidValue: invalid argument #8721

Closed pseudotensor closed 1 year ago

pseudotensor commented 3 years ago

context: https://github.com/rapidsai/cuml/issues/4048#issuecomment-878747900

import cudf
import cupy
import pandas as pd

X = pd.read_csv("df.csv")
y = X['target'].copy()

X = cudf.from_pandas(X).fillna(0.0)
y = cudf.from_pandas(y).fillna(0.0)

ar = cupy.asarray(y).flatten()
ar.sort()

Traceback (most recent call last):
  File "testradix.py", line 12, in <module>
    ar.sort()
  File "cupy/_core/core.pyx", line 695, in cupy._core.core.ndarray.sort
  File "cupy/_core/core.pyx", line 713, in cupy._core.core.ndarray.sort
  File "cupy/_core/_routines_sorting.pyx", line 43, in cupy._core._routines_sorting._ndarray_sort
  File "cupy/cuda/thrust.pyx", line 75, in cupy.cuda.thrust.sort
RuntimeError: radix_sort: failed on 2nd step: cudaErrorInvalidValue: invalid argument
pseudotensor commented 3 years ago

But if I remove the fill, then no issues:

import cudf
import cupy
import pandas as pd

X = pd.read_csv("df.csv")
y = X['target'].copy()

X = cudf.from_pandas(X)
y = cudf.from_pandas(y)

ar = cupy.asarray(y).flatten()
ar.sort()

or if I remove the X from_pandas:

import cudf
import cupy
import pandas as pd

X = pd.read_csv("df.csv")
y = X['target'].copy()

#X = cudf.from_pandas(X).fillna(0.0)
y = cudf.from_pandas(y).fillna(0.0)

ar = cupy.asarray(y).flatten()
ar.sort()

also no problems

pseudotensor commented 3 years ago

This bug makes CUML stuff nearly useless.

pseudotensor commented 3 years ago 
import cudf
import cupy
import pandas as pd

X = pd.read_csv("df.csv")
y = X['target'].copy()

X = cudf.from_pandas(X).astype('float32')
y = cudf.from_pandas(y).astype('float32')

ar = cupy.asarray(y).flatten()
ar.sort()

This also fails. So seems like any modification in cudf-land leads to failure.

harrism commented 3 years ago

Can you provide information about your environment, cuDF version, etc?

pseudotensor commented 3 years ago

Hi, Please see the CUML issue I referenced already in the first comment, where I provide that information: https://github.com/rapidsai/cuml/issues/4048#issue-942382933

beckernick commented 3 years ago

In the nightly as of 2021-07-12, I am unable to reproduce this issue on my system.

import cudf
import cupy
import pandas as pd

X = pd.read_csv("df.csv")
y = X['target']
X = X.drop('target', axis=1)

gpu_id = 0
cupy.cuda.Device(gpu_id).use()

X = cudf.from_pandas(X).fillna(0.0)
y = cudf.from_pandas(y).fillna(0.0)
ar = cupy.asarray(y).flatten()
ar.sort()
print(ar) # my addition
CUDA_VISIBLE_DEVICES="0" python radixtest.py
[0 0 0 ... 1 1 1]

./print_env.sh

Click here to see environment details

     **git***
     commit b0d86d2bfe8814cc7b717156b8b7fa9d5d2198bd (HEAD -> branch-21.08, origin/branch-21.08, origin/HEAD)
     Author: Michael Wang 
     Date:   Mon Jul 12 22:43:07 2021 -0700

     Add `datetime::is_leap_year` (#8711)

     Part 1 of #8677

     This PR adds `datetime::is_leap_year`. The function returns `true` for datetime column rows with leap year; `false` for rows with non leap years, and `null` for null rows.

     Authors:
     - Michael Wang (https://github.com/isVoid)

     Approvers:
     - Mark Harris (https://github.com/harrism)
     - Karthikeyan (https://github.com/karthikeyann)

     URL: https://github.com/rapidsai/cudf/pull/8711
     **git submodules***

     ***OS Information***
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     DGX_PRETTY_NAME="NVIDIA DGX Server"
     DGX_SWBUILD_DATE="2020-03-04"
     DGX_SWBUILD_VERSION="4.4.0"
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     |-------------------------------+----------------------+----------------------+
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     | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
     |                               |                      |               MIG M. |
     |===============================+======================+======================|
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     | N/A   35C    P0    67W / 350W |    736MiB / 32510MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   1  Tesla V100-SXM3...  On   | 00000000:36:00.0 Off |                    0 |
     | N/A   31C    P0    50W / 350W |      5MiB / 32510MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   2  Tesla V100-SXM3...  On   | 00000000:39:00.0 Off |                    0 |
     | N/A   38C    P0    50W / 350W |      5MiB / 32510MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   3  Tesla V100-SXM3...  On   | 00000000:3B:00.0 Off |                    0 |
     | N/A   38C    P0    52W / 350W |      5MiB / 32510MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   4  Tesla V100-SXM3...  On   | 00000000:57:00.0 Off |                    0 |
     | N/A   31C    P0    50W / 350W |      5MiB / 32510MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   5  Tesla V100-SXM3...  On   | 00000000:59:00.0 Off |                    0 |
     | N/A   37C    P0    52W / 350W |      5MiB / 32510MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   6  Tesla V100-SXM3...  On   | 00000000:5C:00.0 Off |                    0 |
     | N/A   32C    P0    51W / 350W |      5MiB / 32510MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   7  Tesla V100-SXM3...  On   | 00000000:5E:00.0 Off |                    0 |
     | N/A   37C    P0    50W / 350W |      5MiB / 32510MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   8  Tesla V100-SXM3...  On   | 00000000:B7:00.0 Off |                    0 |
     | N/A   32C    P0    51W / 350W |      5MiB / 32510MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   9  Tesla V100-SXM3...  On   | 00000000:B9:00.0 Off |                    0 |
     | N/A   35C    P0    49W / 350W |      5MiB / 32510MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |  10  Tesla V100-SXM3...  On   | 00000000:BC:00.0 Off |                    0 |
     | N/A   36C    P0    50W / 350W |      5MiB / 32510MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |  11  Tesla V100-SXM3...  On   | 00000000:BE:00.0 Off |                    0 |
     | N/A   38C    P0    50W / 350W |      5MiB / 32510MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |  12  Tesla V100-SXM3...  On   | 00000000:E0:00.0 Off |                    0 |
     | N/A   32C    P0    48W / 350W |      5MiB / 32510MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |  13  Tesla V100-SXM3...  On   | 00000000:E2:00.0 Off |                    0 |
     | N/A   34C    P0    49W / 350W |      5MiB / 32510MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |  14  Tesla V100-SXM3...  On   | 00000000:E5:00.0 Off |                    0 |
     | N/A   38C    P0    53W / 350W |      5MiB / 32510MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |  15  Tesla V100-SXM3...  On   | 00000000:E7:00.0 Off |                    0 |
     | N/A   37C    P0    48W / 350W |      5MiB / 32510MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+

     +-----------------------------------------------------------------------------+
     | Processes:                                                                  |
     |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
     |        ID   ID                                                   Usage      |
     |=============================================================================|
     |    0   N/A  N/A     74437      C   ...s/rapids-21.08/bin/python      729MiB |
     +-----------------------------------------------------------------------------+
     WARNING: infoROM is corrupted at gpu 0000:39:00.0
     WARNING: infoROM is corrupted at gpu 0000:5C:00.0

     ***CPU***
     Architecture:        x86_64
     CPU op-mode(s):      32-bit, 64-bit
     Byte Order:          Little Endian
     CPU(s):              96
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     Model name:          Intel(R) Xeon(R) Platinum 8168 CPU @ 2.70GHz
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     ***g++***
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     g++ (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
     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) 2005-2021 NVIDIA Corporation
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     Cuda compilation tools, release 11.2, V11.2.152
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     /raid/nicholasb/miniconda3/envs/rapids-21.08/bin/python
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     LD_LIBRARY_PATH                 :
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     CONDA_PREFIX                    : /raid/nicholasb/miniconda3/envs/rapids-21.08
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     ***conda packages***
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     # packages in environment at /raid/nicholasb/miniconda3/envs/rapids-21.08:
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     jupyterlab_server         2.6.1              pyhd8ed1ab_0    conda-forge
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     jxrlib                    1.1                  h7f98852_2    conda-forge
     kealib                    1.4.14               hcc255d8_2    conda-forge
     kiwisolver                1.3.1            py38h1fd1430_1    conda-forge
     krb5                      1.19.1               hcc1bbae_0    conda-forge
     lcms2                     2.12                 hddcbb42_0    conda-forge
     ld_impl_linux-64          2.36.1               hea4e1c9_1    conda-forge
     lerc                      2.2.1                h9c3ff4c_0    conda-forge
     libaec                    1.0.5                h9c3ff4c_0    conda-forge
     libblas                   3.9.0                9_openblas    conda-forge
     libbrotlicommon           1.0.9                h7f98852_5    conda-forge
     libbrotlidec              1.0.9                h7f98852_5    conda-forge
     libbrotlienc              1.0.9                h7f98852_5    conda-forge
     libcblas                  3.9.0                9_openblas    conda-forge
     libcucim                  21.08.00a210712 cuda11.2_g8da6ca9_11    rapidsai-nightly
     libcudf                   21.08.00a210712 cuda11.2_g0b9ea0176c_264    rapidsai-nightly
     libcudf_kafka             21.08.00a210711 g8320a15cda_263    rapidsai-nightly
     libcugraph                21.08.00a210712 cuda11.2_g1a636029_76    rapidsai-nightly
     libcuml                   21.08.00a210712 cuda11.2_gc9abba1a4_113    rapidsai-nightly
     libcumlprims              21.08.00a210605 cuda11.2_g8d4e6b0_2    rapidsai-nightly
     libcurl                   7.77.0               h2574ce0_0    conda-forge
     libcuspatial              21.08.00a210712 cuda11.2_g8c31c2c_22    rapidsai-nightly
     libdap4                   3.20.6               hd7c4107_2    conda-forge
     libdeflate                1.7                  h7f98852_5    conda-forge
     libedit                   3.1.20191231         he28a2e2_2    conda-forge
     libev                     4.33                 h516909a_1    conda-forge
     libevent                  2.1.10               hcdb4288_3    conda-forge
     libfaiss                  1.7.0           cuda112h5bea7ad_8_cuda    conda-forge
     libffi                    3.3                  h58526e2_2    conda-forge
     libgcc-ng                 9.3.0               h2828fa1_19    conda-forge
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     libgsasl                  1.8.0                         2    conda-forge
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     libkml                    1.3.0             h238a007_1013    conda-forge
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     libprotobuf               3.16.0               h780b84a_0    conda-forge
     librdkafka                1.6.1                hc49e61c_1    conda-forge
     librmm                    21.08.00a210712 cuda11.2_geb2b991_34    rapidsai-nightly
     librttopo                 1.1.0                h1185371_6    conda-forge
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     libxgboost                1.4.2dev.rapidsai21.08      cuda11.2_0    rapidsai-nightly
     libxml2                   2.9.12               h72842e0_0    conda-forge
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     poppler                   21.03.0              h93df280_0    conda-forge
     poppler-data              0.4.10                        0    conda-forge
     postgresql                13.3                 h2510834_0    conda-forge
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     prometheus_client         0.11.0             pyhd8ed1ab_0    conda-forge
     prompt-toolkit            3.0.19             pyha770c72_0    conda-forge
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     py-xgboost                1.4.2dev.rapidsai21.08  cuda11.2py38_0    rapidsai-nightly
     py4j                      0.10.9             pyh9f0ad1d_0    conda-forge
     pyarrow                   4.0.1           py38hb53058b_4_cuda    conda-forge
     pycparser                 2.20               pyh9f0ad1d_2    conda-forge
     pyct                      0.4.6                      py_0    conda-forge
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     pydeck                    0.5.0              pyh9f0ad1d_0    conda-forge
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     python                    3.8.10          h49503c6_1_cpython    conda-forge
     python-confluent-kafka    1.6.0            py38h497a2fe_1    conda-forge
     python-dateutil           2.8.1                      py_0    conda-forge
     python_abi                3.8                      2_cp38    conda-forge
     pytz                      2021.1             pyhd8ed1ab_0    conda-forge
     pyviz_comms               2.1.0              pyhd8ed1ab_0    conda-forge
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     pyzmq                     22.1.0           py38h2035c66_0    conda-forge
     rapids                    21.08.00a210709 cuda11.2_py38_g6430beb_23    rapidsai-nightly
     rapids-xgboost            21.08.00a210709 cuda11.2_py38_g6430beb_23    rapidsai-nightly
     re2                       2021.06.01           h9c3ff4c_0    conda-forge
     readline                  8.1                  h46c0cb4_0    conda-forge
     requests                  2.25.1             pyhd3deb0d_0    conda-forge
     requests-unixsocket       0.2.0                      py_0    conda-forge
     rmm                       21.08.00a210712 cuda_11.2_py38_geb2b991_34    rapidsai-nightly
     rtree                     0.9.7            py38h02d302b_1    conda-forge
     s2n                       1.0.10               h9b69904_0    conda-forge
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     snappy                    1.1.8                he1b5a44_3    conda-forge
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     threadpoolctl             2.2.0              pyh8a188c0_0    conda-forge
     tifffile                  2021.7.2           pyhd8ed1ab_0    conda-forge
     tiledb                    2.3.1                he87e0bf_0    conda-forge
     tk                        8.6.10               h21135ba_1    conda-forge
     toolz                     0.11.1                     py_0    conda-forge
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     treelite                  1.3.0            py38hd08a91b_0    conda-forge
     treelite-runtime          1.3.0                    pypi_0    pypi
     typing-extensions         3.10.0.0             hd8ed1ab_0    conda-forge
     typing_extensions         3.10.0.0           pyha770c72_0    conda-forge
     tzcode                    2021a                h7f98852_2    conda-forge
     tzdata                    2021a                he74cb21_1    conda-forge
     ucx                       1.9.0+gcd9efd3       cuda11.2_0    rapidsai-nightly
     ucx-proc                  1.0.0                       gpu    rapidsai-nightly
     ucx-py                    0.21.0a210712   py38_gcd9efd3_29    rapidsai-nightly
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     websockets                8.1              py38h497a2fe_3    conda-forge
     wheel                     0.36.2             pyhd3deb0d_0    conda-forge
     widgetsnbextension        3.5.1            py38h578d9bd_4    conda-forge
     xarray                    0.18.2             pyhd8ed1ab_0    conda-forge
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     xgboost                   1.4.2dev.rapidsai21.08  cuda11.2py38_0    rapidsai-nightly
     xorg-kbproto              1.0.7             h7f98852_1002    conda-forge
     xorg-libice               1.0.10               h7f98852_0    conda-forge
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     xorg-libx11               1.7.2                h7f98852_0    conda-forge
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     xorg-xextproto            7.3.0             h7f98852_1002    conda-forge
     xorg-xproto               7.0.31            h7f98852_1007    conda-forge
     xz                        5.2.5                h516909a_1    conda-forge
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pseudotensor commented 3 years ago

It doesn't happen on my 1 GPU system, only my 2 GPU 1080ti system, as I explained in the original cuml issue. So perhaps having so many GPUs also leads to differences.

pseudotensor commented 3 years ago

This is on 210713 nightly, same problem:

https://github.com/rapidsai/cudf/issues/8721#issuecomment-878792961

still fails.

And something like this still fails:

import cudf
import cupy
import pandas as pd
import numpy as np

import pickle
X, y = pickle.load(open("doo.pkl", "rb"))

gpu_id = 0
cupy.cuda.Device(gpu_id).use()

X = cudf.from_pandas(X.to_pandas()).fillna(0.0)
y = cudf.from_pandas(pd.Series(y)).fillna(0.0)

from cuml.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X, y)

doo.pkl.zip

Traceback (most recent call last):
  File "testradix10.py", line 18, in <module>
    model.fit(X, y)
  File "/home/jon/minicondadai_py38/lib/python3.8/site-packages/cuml/internals/api_decorators.py", line 409, in inner_with_setters
    return func(*args, **kwargs)
  File "cuml/ensemble/randomforestclassifier.pyx", line 456, in cuml.ensemble.randomforestclassifier.RandomForestClassifier.fit
  File "/home/jon/minicondadai_py38/lib/python3.8/site-packages/cuml/internals/api_decorators.py", line 567, in inner_set
    ret_val = func(*args, **kwargs)
  File "cuml/ensemble/randomforest_common.pyx", line 263, in cuml.ensemble.randomforest_common.BaseRandomForestModel._dataset_setup_for_fit
  File "/home/jon/minicondadai_py38/lib/python3.8/site-packages/cupy/_manipulation/add_remove.py", line 179, in unique
    ar.sort()
  File "cupy/_core/core.pyx", line 695, in cupy._core.core.ndarray.sort
  File "cupy/_core/core.pyx", line 713, in cupy._core.core.ndarray.sort
  File "cupy/_core/_routines_sorting.pyx", line 43, in cupy._core._routines_sorting._ndarray_sort
  File "cupy/cuda/thrust.pyx", line 75, in cupy.cuda.thrust.sort
RuntimeError: radix_sort: failed on 2nd step: cudaErrorInvalidValue: invalid argument

I'm giving more examples in case one of them works for you.

beckernick commented 3 years ago

Do you by chance have an example that doesn't depend on datatable?

We have not been able to reproduce this behavior on V100 or P100 machines (cc @galipremsagar )

RishatZagidullin commented 3 years ago

Hello!

I kind of have the same issue. At least the error seems to be similar. But with dask_cudf. Since dask_cudf is located in this repository I hope that I'm allowed to post this here:

import numpy as np
import pandas as pd
import cudf
import dask_cudf

def ginic_gpu(actual_pred):
    #this is not a reliable method to find `n`
    n = actual_pred.divisions[-1]+1
    a_s = actual_pred.set_index(actual_pred.columns[-1])[actual_pred.columns[0]]
    a_c = a_s.cumsum()
    gini_sum = a_c.sum() / a_s.sum() - (n+1) / 2.0
    gini_sum = gini_sum.compute()
    return gini_sum / n

if __name__ == "__main__":
    np_data = np.random.random((100000,2))
    pd_data = pd.DataFrame(data=np_data)
    cudf_data = cudf.from_pandas(pd_data)
    dask_cudf_data = dask_cudf.from_cudf(cudf_data, npartitions=4)

    print(ginic_gpu(dask_cudf_data))

The error occurs only for sufficiently large sizes of the dataset.

Traceback (most recent call last):
  File "radix_sort_bug.py", line 21, in <module>
    print(ginic_gpu(dask_cudf_data))
  File "radix_sort_bug.py", line 9, in ginic_gpu
    a_s = actual_pred.set_index(actual_pred.columns[-1])[actual_pred.columns[0]]
  File "lama_venv/lib/python3.8/site-packages/dask_cudf/core.py", line 237, in set_index
    return super().set_index(
  File "lama_venv/lib/python3.8/site-packages/dask/dataframe/core.py", line 4131, in set_index
    return set_index(
  File "lama_venv/lib/python3.8/site-packages/dask/dataframe/shuffle.py", line 162, in set_index
    divisions, mins, maxes = _calculate_divisions(
  File "lama_venv/lib/python3.8/site-packages/dask/dataframe/shuffle.py", line 35, in _calculate_divisions
    divisions, sizes, mins, maxes = base.compute(divisions, sizes, mins, maxes)
  File "lama_venv/lib/python3.8/site-packages/dask/base.py", line 567, in compute
    results = schedule(dsk, keys, **kwargs)
  File "lama_venv/lib/python3.8/site-packages/dask/threaded.py", line 79, in get
    results = get_async(
  File "ama_venv/lib/python3.8/site-packages/dask/local.py", line 514, in get_async
    raise_exception(exc, tb)
  File "lama_venv/lib/python3.8/site-packages/dask/local.py", line 325, in reraise
    raise exc
  File "lama_venv/lib/python3.8/site-packages/dask/local.py", line 223, in execute_task
    result = _execute_task(task, data)
  File "lama_venv/lib/python3.8/site-packages/dask/core.py", line 121, in _execute_task
    return func(*(_execute_task(a, cache) for a in args))
  File "lama_venv/lib/python3.8/site-packages/dask/dataframe/partitionquantiles.py", line 420, in percentiles_summary
    vals, n = _percentile(data, qs, interpolation=interpolation)
  File "lama_venv/lib/python3.8/site-packages/dask/array/percentile.py", line 34, in _percentile
    return np.percentile(a, q, interpolation=interpolation), n
  File "<__array_function__ internals>", line 5, in percentile
  File "cupy/_core/core.pyx", line 1488, in cupy._core.core.ndarray.__array_function__
  File "lama_venv/lib/python3.8/site-packages/cupy/_statistics/order.py", line 308, in percentile
    return _quantile_unchecked(a, q, axis=axis, out=out,
  File "lama_venv/lib/python3.8/site-packages/cupy/_statistics/order.py", line 212, in _quantile_unchecked
    ap.sort(axis=axis)
  File "cupy/_core/core.pyx", line 695, in cupy._core.core.ndarray.sort
  File "cupy/_core/core.pyx", line 713, in cupy._core.core.ndarray.sort
  File "cupy/_core/_routines_sorting.pyx", line 43, in cupy._core._routines_sorting._ndarray_sort
  File "cupy/cuda/thrust.pyx", line 75, in cupy.cuda.thrust.sort
RuntimeError: radix_sort: failed on 2nd step: cudaErrorInvalidValue: invalid argument

The version of RAPIDS is '21.06.01+2.g101fc0fda4' Launching this on Ubuntu 20.04

I wonder if you can recreate it.

galipremsagar commented 3 years ago

@RishatZagidullin What is the GPU that you ran into this error with?

RishatZagidullin commented 3 years ago

GTX1050Ti

RishatZagidullin commented 3 years ago

For me this error turned out to have to do with cuda 11+ toolkit not working properly on sm35 gpus. Current workaround is uninstalling cupy that gets installed with rapids then install cuda 10.2 (alongside with cuda 11.+) then install cupy-cuda10.2 while having rapids venv active.

github-actions[bot] commented 2 years ago

This issue has been labeled inactive-90d due to no recent activity in the past 90 days. Please close this issue if no further response or action is needed. Otherwise, please respond with a comment indicating any updates or changes to the original issue and/or confirm this issue still needs to be addressed.

GregoryKimball commented 1 year ago

Please feel free to open a new issue if you have trouble with cuDF/cupy interoperability on the latest branch.