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[BUG] AttributeError: type object 'DatetimeProperties' has no attribute 'components' #15522

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pentschev opened this issue Apr 12, 2024 · 5 comments · Fixed by #15523
Closed

[BUG] AttributeError: type object 'DatetimeProperties' has no attribute 'components' #15522

pentschev opened this issue Apr 12, 2024 · 5 comments · Fixed by #15523
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@pentschev
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Describe the bug
This is likely a manifestation of the same issue from Python 3.11.9 changes that was observed in Dask which was fixed in dask/dask#11035 .

Error stack 
Traceback (most recent call last):
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py", line 889, in _fast_slow_function_call
    result = func(*fast_args, **fast_kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: type object 'DatetimeProperties' has no attribute 'components'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 3, in <module>
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/cuml/__init__.py", line 17, in <module>
    from cuml.internals.base import Base, UniversalBase
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/cuml/internals/__init__.py", line 18, in <module>
    from cuml.internals.base_helpers import BaseMetaClass, _tags_class_and_instance
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/cuml/internals/base_helpers.py", line 20, in <module>
    from cuml.internals.api_decorators import (
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/cuml/internals/api_decorators.py", line 24, in <module>
    from cuml.internals import input_utils as iu
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/cuml/internals/input_utils.py", line 19, in <module>
    from cuml.internals.array import CumlArray
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/cuml/internals/array.py", line 51, in <module>
    DaskCudfDataFrame = gpu_only_import_from("dask_cudf.core", "DataFrame")
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/cuml/internals/safe_imports.py", line 394, in gpu_only_import_from
    imported_module = importlib.import_module(module)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/dask_cudf/__init__.py", line 10, in <module>
    import dask.dataframe as dd
  File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 676, in _load_unlocked
  File "<frozen importlib._bootstrap>", line 573, in module_from_spec
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/rapids_dask_dependency/dask_loader.py", line 36, in create_module
    mod = importlib.import_module(spec.name)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/dask/dataframe/__init__.py", line 40, in <module>
    from dask.dataframe import backends, dispatch, methods, rolling
  File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 676, in _load_unlocked
  File "<frozen importlib._bootstrap>", line 573, in module_from_spec
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/rapids_dask_dependency/dask_loader.py", line 36, in create_module
    mod = importlib.import_module(spec.name)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/dask/dataframe/backends.py", line 15, in <module>
    from dask.dataframe.core import DataFrame, Index, Scalar, Series, _Frame
  File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 676, in _load_unlocked
  File "<frozen importlib._bootstrap>", line 573, in module_from_spec
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/rapids_dask_dependency/dask_loader.py", line 36, in create_module
    mod = importlib.import_module(spec.name)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/dask/dataframe/core.py", line 36, in <module>
    from dask.dataframe import methods
  File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 676, in _load_unlocked
  File "<frozen importlib._bootstrap>", line 573, in module_from_spec
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/rapids_dask_dependency/dask_loader.py", line 36, in create_module
    mod = importlib.import_module(spec.name)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/dask/dataframe/methods.py", line 34, in <module>
    from dask.dataframe.utils import is_dataframe_like, is_index_like, is_series_like
  File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 676, in _load_unlocked
  File "<frozen importlib._bootstrap>", line 573, in module_from_spec
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/rapids_dask_dependency/dask_loader.py", line 36, in create_module
    mod = importlib.import_module(spec.name)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/dask/dataframe/utils.py", line 20, in <module>
    from dask.dataframe import (  # noqa: F401 register pandas extension types
  File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 676, in _load_unlocked
  File "<frozen importlib._bootstrap>", line 573, in module_from_spec
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/rapids_dask_dependency/dask_loader.py", line 36, in create_module
    mod = importlib.import_module(spec.name)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/dask/dataframe/_dtypes.py", line 9, in <module>
    from dask.dataframe.extensions import make_array_nonempty, make_scalar
  File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 676, in _load_unlocked
  File "<frozen importlib._bootstrap>", line 573, in module_from_spec
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/rapids_dask_dependency/dask_loader.py", line 36, in create_module
    mod = importlib.import_module(spec.name)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/dask/dataframe/extensions.py", line 8, in <module>
    from dask.dataframe.accessor import (
  File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 676, in _load_unlocked
  File "<frozen importlib._bootstrap>", line 573, in module_from_spec
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/rapids_dask_dependency/dask_loader.py", line 36, in create_module
    mod = importlib.import_module(spec.name)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/dask/dataframe/accessor.py", line 134, in <module>
    class DatetimeAccessor(Accessor):
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/dask/dataframe/accessor.py", line 89, in __init_subclass__
    _bind_property(cls, pd_cls, attr, min_version)
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/dask/dataframe/accessor.py", line 32, in _bind_property
    original_prop = getattr(pd_cls, attr)
                    ^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py", line 474, in __getattr__
    return attr.__get__(None, owner=self)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py", line 417, in __get__
    result, _ = _fast_slow_function_call(getattr, obj, self._name)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/envs/cudf/lib/python3.11/site-packages/cudf/pandas/fast_slow_proxy.py", line 902, in _fast_slow_function_call
    result = func(*slow_args, **slow_kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: type object 'DatetimeProperties' has no attribute 'components'

Steps/Code to reproduce bug

docker run --gpus all --pull always --rm -it --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 rapidsai/ci-conda:cuda12.2.2-ubuntu22.04-py3.11

# Inside the container
mamba create -n cudf python=3.11 cudatoolkit=11.8 cudf=24.06 cuml=24.06

conda activate cudf

cat << EOF | python
import cudf.pandas
cudf.pandas.install()
from cuml import KMeans
import pandas as pd
import cupy as cp
a = cp.asarray([[1.0, 1.0], [1.0, 2.0], [3.0, 2.0], [4.0, 3.0]],
               dtype=cp.float32)
b = pd.DataFrame(a)
# Calling fit
kmeans_float = KMeans(n_clusters=2)
kmeans_float.fit(b)
EOF

Expected behavior
The code should complete without errors.

Environment overview (please complete the following information)

  • Environment location: Docker
  • Method of cuDF install: conda

Environment details

Click here to see environment details
 **git***
 commit f19d4eb9f2ccbe1833aa8112c053e622bc138301 (HEAD -> branch-24.06, origin/branch-24.06, origin/HEAD)
 Author: Karthikeyan <[email protected]>
 Date:   Thu Apr 11 23:07:23 2024 -0500

 Fix async synchronization issues in json_column.cu (#15497)

 Fixes #15390
 This change fixes async synchronization issues in json_column.cu.
 Related file json_tree.cu does not have async synchronization issues.

 Summary of changes:
 changed debug print async to sync,
 added synchronize after multiple async calls
 changed h_chars to async since subsequent call is sync (it will also help because chars array is usually large).
 changed is_str_column_all_nulls to sync.

 Authors:
 - Karthikeyan (https://github.com/karthikeyann)

 Approvers:
 - David Wendt (https://github.com/davidwendt)
 - Robert (Bobby) Evans (https://github.com/revans2)
 - Nghia Truong (https://github.com/ttnghia)

 URL: https://github.com/rapidsai/cudf/pull/15497
 **git submodules***

 ***OS Information***
 DISTRIB_ID=Ubuntu
 DISTRIB_RELEASE=22.04
 DISTRIB_CODENAME=jammy
 DISTRIB_DESCRIPTION="Ubuntu 22.04.4 LTS"
 PRETTY_NAME="Ubuntu 22.04.4 LTS"
 NAME="Ubuntu"
 VERSION_ID="22.04"
 VERSION="22.04.4 LTS (Jammy Jellyfish)"
 VERSION_CODENAME=jammy
 ID=ubuntu
 ID_LIKE=debian
 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"
 UBUNTU_CODENAME=jammy
 Linux b986deaf397f 5.4.0-159-generic #176-Ubuntu SMP Mon Aug 14 12:04:20 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux

 ***GPU Information***
 Fri Apr 12 09:04:15 2024
 +-----------------------------------------------------------------------------+
 | NVIDIA-SMI 525.125.06   Driver Version: 525.125.06   CUDA Version: 12.2     |
 |-------------------------------+----------------------+----------------------+
 | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
 | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
 |                               |                      |               MIG M. |
 |===============================+======================+======================|
 |   0  Tesla V100-SXM2...  On   | 00000000:06:00.0 Off |                    0 |
 | N/A   33C    P0    57W / 300W |    309MiB / 32768MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   1  Tesla V100-SXM2...  On   | 00000000:07:00.0 Off |                    0 |
 | N/A   30C    P0    42W / 300W |      3MiB / 32768MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   2  Tesla V100-SXM2...  On   | 00000000:0A:00.0 Off |                    0 |
 | N/A   31C    P0    41W / 300W |      3MiB / 32768MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   3  Tesla V100-SXM2...  On   | 00000000:0B:00.0 Off |                    0 |
 | N/A   28C    P0    41W / 300W |      3MiB / 32768MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   4  Tesla V100-SXM2...  On   | 00000000:85:00.0 Off |                    0 |
 | N/A   31C    P0    42W / 300W |      3MiB / 32768MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   5  Tesla V100-SXM2...  On   | 00000000:86:00.0 Off |                    0 |
 | N/A   29C    P0    41W / 300W |      3MiB / 32768MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   6  Tesla V100-SXM2...  On   | 00000000:89:00.0 Off |                    0 |
 | N/A   33C    P0    42W / 300W |      5MiB / 32768MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   7  Tesla V100-SXM2...  On   | 00000000:8A:00.0 Off |                    0 |
 | N/A   29C    P0    40W / 300W |      3MiB / 32768MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+

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

 ***CPU***
 Architecture:                       x86_64
 CPU op-mode(s):                     32-bit, 64-bit
 Address sizes:                      46 bits physical, 48 bits virtual
 Byte Order:                         Little Endian
 CPU(s):                             80
 On-line CPU(s) list:                0-79
 Vendor ID:                          GenuineIntel
 Model name:                         Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz
 CPU family:                         6
 Model:                              79
 Thread(s) per core:                 2
 Core(s) per socket:                 20
 Socket(s):                          2
 Stepping:                           1
 CPU max MHz:                        3600.0000
 CPU min MHz:                        1200.0000
 BogoMIPS:                           4389.94
 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 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 ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts md_clear flush_l1d
 Virtualization:                     VT-x
 L1d cache:                          1.3 MiB (40 instances)
 L1i cache:                          1.3 MiB (40 instances)
 L2 cache:                           10 MiB (40 instances)
 L3 cache:                           100 MiB (2 instances)
 NUMA node(s):                       2
 NUMA node0 CPU(s):                  0-19,40-59
 NUMA node1 CPU(s):                  20-39,60-79
 Vulnerability Gather data sampling: Not affected
 Vulnerability Itlb multihit:        KVM: Mitigation: Split huge pages
 Vulnerability L1tf:                 Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
 Vulnerability Mds:                  Mitigation; Clear CPU buffers; SMT vulnerable
 Vulnerability Meltdown:             Mitigation; PTI
 Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
 Vulnerability Retbleed:             Not affected
 Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp
 Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
 Vulnerability Spectre v2:           Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
 Vulnerability Srbds:                Not affected
 Vulnerability Tsx async abort:      Mitigation; Clear CPU buffers; SMT vulnerable

 ***CMake***

 ***g++***

 ***nvcc***

 ***Python***
 /opt/conda/envs/cudf/bin/python
 Python 3.11.8

 ***Environment Variables***
 PATH                            : /opt/conda/envs/cudf/bin:/opt/conda/condabin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
 LD_LIBRARY_PATH                 : /usr/local/nvidia/lib:/usr/local/nvidia/lib64
 NUMBAPRO_NVVM                   :
 NUMBAPRO_LIBDEVICE              :
 CONDA_PREFIX                    : /opt/conda/envs/cudf
 PYTHON_PATH                     :

 ***conda packages***
 /opt/conda/condabin/conda
 # packages in environment at /opt/conda/envs/cudf:
 #
 # Name                    Version                   Build  Channel
 _libgcc_mutex             0.1                 conda_forge    conda-forge
 _openmp_mutex             4.5                       2_gnu    conda-forge
 aws-c-auth                0.7.17               he0b1f16_2    conda-forge
 aws-c-cal                 0.6.11               heb1d5e4_0    conda-forge
 aws-c-common              0.9.15               hd590300_0    conda-forge
 aws-c-compression         0.2.18               hce8ee76_3    conda-forge
 aws-c-event-stream        0.4.2                h01f5eca_8    conda-forge
 aws-c-http                0.8.1               hdb68c23_10    conda-forge
 aws-c-io                  0.14.7               h76a7424_3    conda-forge
 aws-c-mqtt                0.10.3               h50844eb_4    conda-forge
 aws-c-s3                  0.5.7                hb7bd14b_1    conda-forge
 aws-c-sdkutils            0.1.15               hce8ee76_3    conda-forge
 aws-checksums             0.1.18               hce8ee76_3    conda-forge
 aws-crt-cpp               0.26.6               hf567797_4    conda-forge
 aws-sdk-cpp               1.11.267             hbf3e495_6    conda-forge
 bokeh                     3.4.1              pyhd8ed1ab_0    conda-forge
 brotli-python             1.1.0           py311hb755f60_1    conda-forge
 bzip2                     1.0.8                hd590300_5    conda-forge
 c-ares                    1.28.1               hd590300_0    conda-forge
 ca-certificates           2024.2.2             hbcca054_0    conda-forge
 cachetools                5.3.3              pyhd8ed1ab_0    conda-forge
 click                     8.1.7           unix_pyh707e725_0    conda-forge
 cloudpickle               3.0.0              pyhd8ed1ab_0    conda-forge
 contourpy                 1.2.1           py311h9547e67_0    conda-forge
 cubinlinker               0.3.0           py311h4b55656_1    rapidsai
 cuda-profiler-api         11.8.86                       0    nvidia
 cuda-python               11.8.3          py311h8bf0e4b_1    conda-forge
 cuda-version              11.8                 h70ddcb2_3    conda-forge
 cudatoolkit               11.8.0              h4ba93d1_13    conda-forge
 cudf                      24.06.00a96     cuda11_py311_240412_gf19d4eb9f2_96    rapidsai-nightly
 cuml                      24.06.00a18     cuda11_py311_240411_gd14d7e93e_18    rapidsai-nightly
 cupy                      13.0.0          py311h878bca4_3    conda-forge
 cupy-core                 13.0.0          py311heecd119_3    conda-forge
 cytoolz                   0.12.3          py311h459d7ec_0    conda-forge
 dask                      2024.4.2a240411  py_g66ced13b_1    dask/label/dev
 dask-core                 2024.4.2a240408 py_gb2ec1e1a2_4    dask/label/dev
 dask-cuda                 24.06.00a10     py311_240412_g63198ea_10    rapidsai-nightly
 dask-cudf                 24.06.00a96     cuda11_py311_240412_gf19d4eb9f2_96    rapidsai-nightly
 dask-expr                 1.0.12a240409     py_gad4d195_1    dask/label/dev
 distributed               2024.4.2a240411  py_g66ced13b_1    dask/label/dev
 dlpack                    0.8                  h59595ed_3    conda-forge
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 libcudf                   24.06.00a96     cuda11_240412_gf19d4eb9f2_96    rapidsai-nightly
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 libcufile-dev             1.4.0.31                      0    nvidia
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 libcusolver-dev           11.4.1.48                     0    nvidia
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 libcusparse-dev           11.7.5.86                     0    nvidia
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 libnl                     3.9.0                hd590300_0    conda-forge
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 libre2-11                 2023.09.01           h5a48ba9_2    conda-forge
 librmm                    24.06.00a11     cuda11_240412_g9e02f34a_11    rapidsai-nightly
 libsqlite                 3.45.2               h2797004_0    conda-forge
 libssh2                   1.11.0               h0841786_0    conda-forge
 libstdcxx-ng              13.2.0               h7e041cc_5    conda-forge
 libthrift                 0.19.0               hb90f79a_1    conda-forge
 libtiff                   4.6.0                h1dd3fc0_3    conda-forge
 libutf8proc               2.8.0                h166bdaf_0    conda-forge
 libuuid                   2.38.1               h0b41bf4_0    conda-forge
 libwebp-base              1.3.2                hd590300_1    conda-forge
 libxcb                    1.15                 h0b41bf4_0    conda-forge
 libxcrypt                 4.4.36               hd590300_1    conda-forge
 libxml2                   2.12.6               h232c23b_1    conda-forge
 libzlib                   1.2.13               hd590300_5    conda-forge
 llvmlite                  0.42.0          py311ha6695c7_1    conda-forge
 locket                    1.0.0              pyhd8ed1ab_0    conda-forge
 lz4                       4.3.3           py311h38e4bf4_0    conda-forge
 lz4-c                     1.9.4                hcb278e6_0    conda-forge
 markdown-it-py            3.0.0              pyhd8ed1ab_0    conda-forge
 markupsafe                2.1.5           py311h459d7ec_0    conda-forge
 mdurl                     0.1.2              pyhd8ed1ab_0    conda-forge
 msgpack-python            1.0.7           py311h9547e67_0    conda-forge
 nccl                      2.21.5.1             h6103f9b_0    conda-forge
 ncurses                   6.4.20240210         h59595ed_0    conda-forge
 numba                     0.59.1          py311h96b013e_0    conda-forge
 numpy                     1.26.4          py311h64a7726_0    conda-forge
 nvcomp                    3.0.6                h56904bc_0    conda-forge
 nvtx                      0.2.10          py311h459d7ec_0    conda-forge
 openjpeg                  2.5.2                h488ebb8_0    conda-forge
 openssl                   3.2.1                hd590300_1    conda-forge
 orc                       2.0.0                h17fec99_1    conda-forge
 packaging                 24.0               pyhd8ed1ab_0    conda-forge
 pandas                    2.2.1           py311h320fe9a_0    conda-forge
 partd                     1.4.1              pyhd8ed1ab_0    conda-forge
 pillow                    10.3.0          py311h18e6fac_0    conda-forge
 pip                       24.0               pyhd8ed1ab_0    conda-forge
 protobuf                  4.25.3          py311h7b78aeb_0    conda-forge
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 pthread-stubs             0.4               h36c2ea0_1001    conda-forge
 ptxcompiler               0.8.1           py311h2c5c81a_2    conda-forge
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 pynvml                    11.4.1             pyhd8ed1ab_0    conda-forge
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 python                    3.11.8          hab00c5b_0_cpython    conda-forge
 python-dateutil           2.9.0              pyhd8ed1ab_0    conda-forge
 python-tzdata             2024.1             pyhd8ed1ab_0    conda-forge
 python_abi                3.11                    4_cp311    conda-forge
 pytz                      2024.1             pyhd8ed1ab_0    conda-forge
 pyyaml                    6.0.1           py311h459d7ec_1    conda-forge
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 rapids-dask-dependency    24.06.00a20                py_0    rapidsai-nightly
 rdma-core                 51.0                 hd3aeb46_0    conda-forge
 re2                       2023.09.01           h7f4b329_2    conda-forge
 readline                  8.2                  h8228510_1    conda-forge
 rich                      13.7.1             pyhd8ed1ab_0    conda-forge
 rmm                       24.06.00a11     cuda11_py311_240412_g9e02f34a_11    rapidsai-nightly
 s2n                       1.4.9                h06160fa_0    conda-forge
 scipy                     1.13.0          py311h64a7726_0    conda-forge
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 six                       1.16.0             pyh6c4a22f_0    conda-forge
 snappy                    1.2.0                hdb0a2a9_1    conda-forge
 sortedcontainers          2.4.0              pyhd8ed1ab_0    conda-forge
 spdlog                    1.12.0               hd2e6256_2    conda-forge
 tblib                     3.0.0              pyhd8ed1ab_0    conda-forge
 tk                        8.6.13          noxft_h4845f30_101    conda-forge
 toolz                     0.12.1             pyhd8ed1ab_0    conda-forge
 tornado                   6.4             py311h459d7ec_0    conda-forge
 treelite                  4.1.2           py311hd96da90_0    conda-forge
 typing_extensions         4.11.0             pyha770c72_0    conda-forge
 tzdata                    2024a                h0c530f3_0    conda-forge
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 ucx-proc                  1.0.0                       gpu    rapidsai
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 urllib3                   2.2.1              pyhd8ed1ab_0    conda-forge
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 yaml                      0.2.5                h7f98852_2    conda-forge
 zict                      3.0.0              pyhd8ed1ab_0    conda-forge
 zipp                      3.17.0             pyhd8ed1ab_0    conda-forge
 zstd                      1.5.5                hfc55251_0    conda-forge

@pentschev pentschev added the bug Something isn't working label Apr 12, 2024
@wence-
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wence- commented Apr 12, 2024

Let me look, it should be possible to fix this in the cudf.pandas wrapper magic.

@wence- wence- self-assigned this Apr 12, 2024
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wence- commented Apr 12, 2024

Simpler repro:

import cudf.pandas
cudf.pandas.install()
import dask.dataframe

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wence- commented Apr 12, 2024 •
edited
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Can you try:

diff --git a/python/cudf/cudf/pandas/_wrappers/pandas.py b/python/cudf/cudf/pandas/_wrappers/pandas.py
index b7c8e92e8d..3c82d57193 100644
--- a/python/cudf/cudf/pandas/_wrappers/pandas.py
+++ b/python/cudf/cudf/pandas/_wrappers/pandas.py
@@ -174,7 +174,7 @@ Series = make_final_proxy_type(
         "__arrow_array__": arrow_array_method,
         "__cuda_array_interface__": cuda_array_interface,
         "__iter__": custom_iter,
-        "dt": _AccessorAttr(DatetimeProperties),
+        "dt": _AccessorAttr(CombinedDatetimelikeProperties),
         "str": _AccessorAttr(StringMethods),
         "cat": _AccessorAttr(_CategoricalAccessor),
         "_constructor": _FastSlowAttribute("_constructor"),
@@ -208,7 +208,7 @@ Index = make_final_proxy_type(
         "__array_function__": array_function_method,
         "__arrow_array__": arrow_array_method,
         "__cuda_array_interface__": cuda_array_interface,
-        "dt": _AccessorAttr(DatetimeProperties),
+        "dt": _AccessorAttr(CombinedDatetimelikeProperties),
         "str": _AccessorAttr(StringMethods),
         "cat": _AccessorAttr(_CategoricalAccessor),
         "__iter__": custom_iter,

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wence- commented Apr 12, 2024

This showed up a problem in our pandas wrappers in cudf.pandas (which the above patch fixes), but when we merged dask/dask#11035 we accidentally dropped some exception handling, that I reinstate in dask/dask#11049

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Thanks @wence- , yes, using the patch from #15522 (comment) the code completes without errors in my docker+conda environment. Could you submit a PR for that?

wence- added a commit to wence-/cudf that referenced this issue Apr 12, 2024
@wence-
cudf.pandas: Series dt accessor is CombinedDatetimelikeProperties
7798468 
On the pandas Series type (not an instance) the dt attribute returns a
CombinedDatetimelikeProperties object, which advertises the attributes
of all possible datetime like dtypes. Previously we were proxying this
with a DatatimeProperties object, which doesn't advertise as many
properties. To allow wrapping libraries like dask that introspect the
object to work correctly, advertise like pandas on the type. The
instance still produces an object of the correct type due to dynamic
lookup and/or metaclass magic in cudf.pandas and pandas respectively.

- Closes rapidsai#15522
@wence- wence- mentioned this issue Apr 12, 2024
Merged
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rapids-bot bot pushed a commit that referenced this issue Apr 12, 2024
@wence-
cudf.pandas: Series dt accessor is CombinedDatetimelikeProperties (#1…
2e00cb1 
…5523)

On the pandas Series type (not an instance) the dt attribute returns a CombinedDatetimelikeProperties object, which advertises the attributes of all possible datetime like dtypes. Previously we were proxying this with a DatatimeProperties object, which doesn't advertise as many properties. To allow wrapping libraries like dask that introspect the object to work correctly, advertise like pandas on the type. The instance still produces an object of the correct type due to dynamic lookup and/or metaclass magic in cudf.pandas and pandas respectively.

- Closes #15522

Authors:
  - Lawrence Mitchell (https://github.com/wence-)

Approvers:
  - Peter Andreas Entschev (https://github.com/pentschev)
  - GALI PREM SAGAR (https://github.com/galipremsagar)

URL: #15523
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cudf.pandas: Series dt accessor is CombinedDatetimelikeProperties wence-/cudf
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