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[INSTALL]: PIP errors when installing in env with cudf and cuml #3235

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1 task done
jmakov opened this issue Jan 7, 2023 · 7 comments
Closed
1 task done

[INSTALL]: PIP errors when installing in env with cudf and cuml #3235

jmakov opened this issue Jan 7, 2023 · 7 comments
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gpu installation no-issue-activity

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@jmakov
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jmakov commented Jan 7, 2023 •
edited

Just starting out with PyCaret and am wondering how to make it work on GPUs (or just install without PIP reporting errors). As per docs we only need cuml. Is there a preferred way to create the virt env?

Installation check

Platform

Ubuntu 22.04.01 LTS, linux-5.15.0-57-generic-x86_64-with-glibc2.35

Installation Method

pip install

pycaret Version

3.0.0.rc6

Python Version

3.9

Description

1st variant

mamba create -y -n test python=3.9
conda activate test
mamba install -y -c rapidsai -c conda-forge -c nvidia cudf=22.12 cuml=22.12
pip install --pre pycaret
**ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
dask-cudf 22.12.1 requires cupy-cuda11x, which is not installed.
cuml 22.12.0 requires seaborn, which is not installed.
cudf 22.12.1 requires cupy-cuda11x, which is not installed.
distributed 2022.11.1 requires tornado<6.2,>=6.0.3, but you have tornado 6.2 which is incompatible.
cuml 22.12.0 requires treelite==3.0.1, but you have treelite 3.0.0 which is incompatible.
cuml 22.12.0 requires treelite_runtime==3.0.1, but you have treelite-runtime 3.0.0 which is incompatible.**

2nd variant

mamba create -y -n test python=3.9
conda activate test
mamba install -y \
            -c conda-forge -c pyviz \
            colorcet \
            datashader dvc dvc-ssh \
            hvplot \
            nb_conda_kernels numpy numba \
            pandas pyarrow \
            statsmodels \
            ta-lib tqdm
pip install --pre pycaret
**ERROR: Cannot uninstall 'llvmlite'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.**

3rd variant

mamba create -y -n test python=3.9
conda activate test
pip install --pre pycaret
mamba install -y \
          -c rapidsai -c nvidia -c conda-forge \
          cudf=22.12 cuml=22.12
**Encountered problems while solving:
  - package libarrow-10.0.1-hd014966_3_cpu requires openssl >=3.0.7,<4.0a0, but none of the providers can be installed
  - package cudf-22.12.00-cuda_11_py38_gbaae3a6bbf_0 requires python_abi 3.8.* *_cp38, but none of the providers can be installed**

Installation Logs

(puma-lab) toaster@node2:~/workspace/repos/puma-lab$ mamba install -y \ -c rapidsai -c conda-forge -c nvidia \ cudf=22.12 __ __ __ __ ███████████████/ /██/ /██/ /██/ /████████████████████████ / / \ / \ / \ / \ \____ / / \_/ \_/ \_/ \ o \__, / _/ \_____/ ` |/ ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗ ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗ ██╔████╔██║███████║██╔████╔██║██████╔╝███████║ ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║ ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║ ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝ 
    mamba (1.0.0) supported by @QuantStack                                                   
                                                                                                      
    GitHub:  https://github.com/mamba-org/mamba                                              
    Twitter: https://twitter.com/QuantStack

█████████████████████████████████████████████████████████████

Looking for: ['cudf=22.12']

conda-forge/linux-64 Using cache
conda-forge/noarch Using cache
nvidia/linux-64 Using cache
nvidia/noarch Using cache
pkgs/main/linux-64 No change
pkgs/r/linux-64 No change
pkgs/r/noarch No change
pkgs/main/noarch No change
rapidsai/linux-64 No change
rapidsai/noarch No change
intel/noarch No change
intel/linux-64 No change

Pinned packages:

  • python 3.9.*

Transaction

Prefix: /home/toaster/mambaforge-pypy3/envs/puma-lab

Updating specs:

  • cudf=22.12
  • ca-certificates
  • certifi
  • openssl

Package Version Build Channel Size
───────────────────────────────────────────────────────────────────────────────────────────────────
Install:
───────────────────────────────────────────────────────────────────────────────────────────────────

  • arrow-cpp 9.0.0 py39hd3ccb9b_2_cpu conda-forge/linux-64 Cached

  • aws-c-cal 0.5.11 h95a6274_0 conda-forge/linux-64 Cached

  • aws-c-common 0.6.2 h7f98852_0 conda-forge/linux-64 Cached

  • aws-c-event-stream 0.2.7 h3541f99_13 conda-forge/linux-64 Cached

  • aws-c-io 0.10.5 hfb6a706_0 conda-forge/linux-64 Cached

  • aws-checksums 0.1.11 ha31a3da_7 conda-forge/linux-64 Cached

  • aws-sdk-cpp 1.8.186 hecaee15_4 conda-forge/linux-64 Cached

  • c-ares 1.18.1 h7f98852_0 conda-forge/linux-64 Cached

  • cachetools 5.2.0 pyhd8ed1ab_0 conda-forge/noarch Cached

  • cubinlinker 0.2.2 py39h11215e4_0 rapidsai/linux-64 Cached

  • cuda-python 11.8.1 py39h2405124_2 conda-forge/linux-64 Cached

  • cudatoolkit 11.8.0 h37601d7_11 conda-forge/linux-64 667MB

  • cudf 22.12.01 cuda_11_py39_gf700408e68_0 rapidsai/linux-64 Cached

  • cupy 11.4.0 py39hc3c280e_0 conda-forge/linux-64 Cached

  • dlpack 0.5 h9c3ff4c_0 conda-forge/linux-64 Cached

  • fastavro 1.7.0 py39hb9d737c_0 conda-forge/linux-64 Cached

  • fastrlock 0.8 py39h5a03fae_3 conda-forge/linux-64 Cached

  • fsspec 2022.11.0 pyhd8ed1ab_0 conda-forge/noarch Cached

  • gflags 2.2.2 he1b5a44_1004 conda-forge/linux-64 Cached

  • glog 0.6.0 h6f12383_0 conda-forge/linux-64 Cached

  • grpc-cpp 1.47.1 hbad87ad_6 conda-forge/linux-64 Cached

  • keyutils 1.6.1 h166bdaf_0 conda-forge/linux-64 Cached

  • krb5 1.20.1 hf9c8cef_0 conda-forge/linux-64 Cached

  • libabseil 20220623.0 cxx17_h05df665_6 conda-forge/linux-64 Cached

  • libblas 3.9.0 16_linux64_openblas conda-forge/linux-64 Cached

  • libbrotlicommon 1.0.9 h166bdaf_8 conda-forge/linux-64 Cached

  • libbrotlidec 1.0.9 h166bdaf_8 conda-forge/linux-64 Cached

  • libbrotlienc 1.0.9 h166bdaf_8 conda-forge/linux-64 Cached

  • libcblas 3.9.0 16_linux64_openblas conda-forge/linux-64 Cached

  • libcrc32c 1.1.2 h9c3ff4c_0 conda-forge/linux-64 Cached

  • libcudf 22.12.01 cuda11_gf700408e68_0 rapidsai/linux-64 Cached

  • libcurl 7.87.0 h6312ad2_0 conda-forge/linux-64 Cached

  • libedit 3.1.20191231 he28a2e2_2 conda-forge/linux-64 Cached + libev 4.33 h516909a_1 conda-forge/linux-64 Cached + libevent 2.1.10 h9b69904_4 conda-forge/linux-64 Cached

  • libgfortran-ng 12.2.0 h69a702a_19 conda-forge/linux-64 Cached

  • libgfortran5 12.2.0 h337968e_19 conda-forge/linux-64 Cached

  • libgoogle-cloud 2.1.0 h9ebe8e8_2 conda-forge/linux-64 Cached

  • liblapack 3.9.0 16_linux64_openblas conda-forge/linux-64 Cached

  • libllvm11 11.1.0 he0ac6c6_5 conda-forge/linux-64 Cached

  • libnghttp2 1.51.0 hdcd2b5c_0 conda-forge/linux-64 Cached

  • libopenblas 0.3.21 pthreads_h78a6416_3 conda-forge/linux-64 Cached + libprotobuf 3.20.2 h6239696_0 conda-forge/linux-64 Cached + librmm 22.12.00 cuda11_g8aae42d1_0 rapidsai/linux-64 Cached + libssh2 1.10.0 haa6b8db_3 conda-forge/linux-64 Cached

  • libstdcxx-ng 12.2.0 h46fd767_19 conda-forge/linux-64 Cached

  • libstdcxx-ng 12.2.0 h46fd767_19 conda-forge/linux-64 Cached

  • libthrift 0.16.0 h491838f_2 conda-forge/linux-64 Cached

  • libutf8proc 2.8.0 h166bdaf_0 conda-forge/linux-64 Cached

  • libzlib 1.2.13 h166bdaf_4 conda-forge/linux-64 Cached

  • llvmlite 0.39.1 py39h7d9a04d_1 conda-forge/linux-64 Cached

  • lz4-c 1.9.3 h9c3ff4c_1 conda-forge/linux-64 Cached

  • numba 0.56.4 py39h61ddf18_0 conda-forge/linux-64 Cached

  • numpy 1.23.5 py39h3d75532_0 conda-forge/linux-64 Cached

  • nvtx 0.2.3 py39hb9d737c_2 conda-forge/linux-64 Cached

  • orc 1.7.6 h6c59b99_0 conda-forge/linux-64 Cached

  • packaging 22.0 pyhd8ed1ab_0 conda-forge/noarch Cached

  • pandas 1.5.2 py39h4661b88_0 conda-forge/linux-64 Cached

  • parquet-cpp 1.5.1 2 conda-forge/noarch Cached

  • protobuf 3.20.2 py39h5a03fae_1 conda-forge/linux-64 Cached

  • ptxcompiler 0.7.0 py39h2405124_3 conda-forge/linux-64 Cached

  • pyarrow 9.0.0 py39hc0775d8_2_cpu conda-forge/linux-64 Cached

  • python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge/noarch Cached

  • python_abi 3.9 2_cp39 conda-forge/linux-64 Cached

  • pytz 2022.7 pyhd8ed1ab_0 conda-forge/noarch Cached

  • re2 2022.06.01 h27087fc_1 conda-forge/linux-64 Cached

  • rmm 22.12.00 cuda11_py39_g8aae42d1_0 rapidsai/linux-64 Cached

  • s2n 1.0.10 h9b69904_0 conda-forge/linux-64 Cached

  • six 1.16.0 pyh6c4a22f_0 conda-forge/noarch Cached

  • snappy 1.1.9 hbd366e4_2 conda-forge/linux-64 Cached

  • spdlog 1.8.5 h4bd325d_1 conda-forge/linux-64 Cached

  • typing_extensions 4.4.0 pyha770c72_0 conda-forge/noarch Cached

  • zstd 1.5.2 h6239696_4 conda-forge/linux-64 Cached

Change:
───────────────────────────────────────────────────────────────────────────────────────────────────

  • _libgcc_mutex 0.1 main intel
  • _libgcc_mutex 0.1 conda_forge conda-forge/linux-64 Cached
  • zlib 1.2.13 h5eee18b_0 intel
  • zlib 1.2.13 h166bdaf_4 conda-forge/linux-64 Cached

Upgrade:
───────────────────────────────────────────────────────────────────────────────────────────────────

  • ca-certificates 2022.10.11 h06a4308_0 intel
  • ca-certificates 2022.12.7 ha878542_0 conda-forge/linux-64 Cached
  • certifi 2022.9.24 py39h06a4308_0 intel
  • certifi 2022.12.7 pyhd8ed1ab_0 conda-forge/noarch Cached
  • libgcc-ng 11.2.0 h1234567_1 intel
  • libgcc-ng 12.2.0 h65d4601_19 conda-forge/linux-64 Cached
  • libgomp 11.2.0 h1234567_1 intel
  • libgomp 12.2.0 h65d4601_19 conda-forge/linux-64 Cached
  • openssl 1.1.1q h7f8727e_0 intel
  • openssl 1.1.1s h0b41bf4_1 conda-forge/linux-64 Cached

Downgrade:
───────────────────────────────────────────────────────────────────────────────────────────────────

  • _openmp_mutex 5.1 1_gnu intel
  • _openmp_mutex 4.5 2_gnu conda-forge/linux-64 Cached

Summary:

Install: 72 packages
Change: 2 packages
Upgrade: 5 packages
Downgrade: 1 packages

Total download: 667MB

───────────────────────────────────────────────────────────────────────────────────────────────────

cudatoolkit 666.8MB @ 53.0MB/s 12.6s
Preparing transaction: done
Verifying transaction: done

The following PRELINK MESSAGES are INCLUDED:

File nvcomp.txt:

By downloading and using the libcudf conda package, you accept the terms
and conditions of the NVIDIA NVCOMP Software License Agreement:
https://developer.download.nvidia.com/compute/nvcomp/2.3/LICENSE.txt

Executing transaction: \ By downloading and using the CUDA Toolkit conda packages, you accept the terms and conditions of the CUDA End User License Agreement (EULA): https://docs.nvidia.com/cuda/eula/index.html

/ By downloading and using the CubinLinker conda packages, you accept the terms and conditions of the CubinLinker License Agreement: https://docs.rapids.ai/licenses/CubinLinker.txt

done
(puma-lab) toaster@node2:/workspace/repos/puma-lab$ pip install --pre pycaret
Collecting pycaret Using cached pycaret-3.0.0rc6-py3-none-any.whl (501 kB) Requirement already satisfied: pandas<1.6.0,>=1.3.0 in /home/toaster/mambaforge-pypy3/envs/puma-lab/lib/python3.9/site-packages (from pycaret) (1.5.2)
Collecting scipy<2.0.0
Using cached scipy-1.10.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (34.4 MB)
Collecting pmdarima!=1.8.1,<3.0.0,>=1.8.0
Using cached pmdarima-2.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl (1.9 MB)
Collecting numpy<1.23,>=1.21
Using cached numpy-1.22.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.8 MB)
Collecting importlib-metadata>=4.12.0 Using cached importlib_metadata-6.0.0-py3-none-any.whl (21 kB) Collecting tbats>=1.1.0 Using cached tbats-1.1.2-py3-none-any.whl (43 kB)
Collecting lightgbm>=3.0.0
Collecting lightgbm>=3.0.0
Using cached lightgbm-3.3.4-py3-none-manylinux1_x86_64.whl (2.0 MB)
Collecting ipython>=5.5.0
Using cached ipython-8.8.0-py3-none-any.whl (775 kB)
Requirement already satisfied: numba>=0.55.0 in /home/toaster/mambaforge-pypy3/envs/puma-lab/lib/python3.9/site-packages (from pycaret) (0.56.4)
Collecting yellowbrick>=1.4
Using cached yellowbrick-1.5-py3-none-any.whl (282 kB)
Collecting requests>=2.27.1
Using cached requests-2.28.1-py3-none-any.whl (62 kB)
Collecting nbformat>=4.2.0
Using cached nbformat-5.7.1-py3-none-any.whl (77 kB)
Collecting category-encoders>=2.4.0
Using cached category_encoders-2.5.1.post0-py2.py3-none-any.whl (72 kB)
Collecting imbalanced-learn>=0.8.1
Using cached imbalanced_learn-0.10.1-py3-none-any.whl (226 kB)
Collecting tqdm>=4.62.0
Using cached tqdm-4.64.1-py2.py3-none-any.whl (78 kB)
Collecting plotly>=5.0.0
Using cached plotly-5.11.0-py2.py3-none-any.whl (15.3 MB)
Collecting plotly-resampler>=0.7.2.2
Using cached plotly_resampler-0.8.4rc1-cp39-cp39-manylinux_2_35_x86_64.whl
Collecting scikit-plot>=0.3.7
Using cached scikit_plot-0.3.7-py3-none-any.whl (33 kB)
Collecting deprecation>=2.1.0
Using cached deprecation-2.1.0-py2.py3-none-any.whl (11 kB)
Collecting matplotlib>=3.3.0
Using cached matplotlib-3.6.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.8 MB)
Collecting psutil>=5.9.0
Using cached psutil-5.9.4-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (280 kB)
Collecting sktime<0.15.0,>=0.14.0
Using cached sktime-0.14.1-py3-none-any.whl (15.9 MB)
Collecting schemdraw>=0.14
Using cached schemdraw-0.15-py3-none-any.whl (106 kB)
Collecting pyod>=0.9.8
Using cached pyod-1.0.7-py3-none-any.whl
Collecting markupsafe>=2.0.1
Using cached MarkupSafe-2.1.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (25 kB)
Collecting statsmodels>=0.12.1
Using cached statsmodels-0.13.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.9 MB)
Collecting scikit-learn>=1.0
Using cached scikit_learn-1.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.5 MB)
Collecting cloudpickle
Using cached cloudpickle-2.2.0-py3-none-any.whl (25 kB)
Collecting ipywidgets>=7.6.5
Using cached ipywidgets-8.0.4-py3-none-any.whl (137 kB)
Collecting joblib>=1.2.0
Using cached joblib-1.2.0-py3-none-any.whl (297 kB)
Collecting kaleido>=0.2.1
Using cached kaleido-0.2.1-py2.py3-none-manylinux1_x86_64.whl (79.9 MB)
Collecting jinja2>=1.2
Using cached Jinja2-3.1.2-py3-none-any.whl (133 kB)
Collecting patsy>=0.5.1
Using cached patsy-0.5.3-py2.py3-none-any.whl (233 kB)
Requirement already satisfied: packaging in /home/toaster/mambaforge-pypy3/envs/puma-lab/lib/python3.9/site-packages (from deprecation>=2.1.0->pycaret) (22.0)
Collecting threadpoolctl>=2.0.0
Using cached threadpoolctl-3.1.0-py3-none-any.whl (14 kB)
Collecting zipp>=0.5
Using cached zipp-3.11.0-py3-none-any.whl (6.6 kB)
Collecting pygments>=2.4.0
Using cached Pygments-2.14.0-py3-none-any.whl (1.1 MB)
Collecting pickleshare
Using cached pickleshare-0.7.5-py2.py3-none-any.whl (6.9 kB)
Collecting jedi>=0.16
Using cached jedi-0.18.2-py2.py3-none-any.whl (1.6 MB)
Collecting prompt-toolkit<3.1.0,>=3.0.11
Using cached prompt_toolkit-3.0.36-py3-none-any.whl (386 kB)
Collecting matplotlib-inline
Using cached matplotlib_inline-0.1.6-py3-none-any.whl (9.4 kB)
Collecting stack-data
Using cached stack_data-0.6.2-py3-none-any.whl (24 kB)
Collecting pexpect>4.3
Using cached pexpect-4.8.0-py2.py3-none-any.whl (59 kB)
Collecting decorator
Using cached decorator-5.1.1-py3-none-any.whl (9.1 kB)
Collecting backcall
Using cached backcall-0.2.0-py2.py3-none-any.whl (11 kB)
Collecting traitlets>=5
Using cached traitlets-5.8.0-py3-none-any.whl (116 kB)
Collecting ipykernel>=4.5.1
Using cached ipykernel-6.19.4-py3-none-any.whl (145 kB)
Collecting jupyterlab-widgets=3.0
Using cached jupyterlab_widgets-3.0.5-py3-none-any.whl (384 kB)
Collecting widgetsnbextension~=4.0
Using cached widgetsnbextension-4.0.5-py3-none-any.whl (2.0 MB)
Requirement already satisfied: wheel in /home/toaster/mambaforge-pypy3/envs/puma-lab/lib/python3.9/site-packages (from lightgbm>=3.0.0->pycaret) (0.37.1)
Collecting contourpy>=1.0.1
Using cached contourpy-1.0.6-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (296 kB)
Collecting kiwisolver>=1.0.1
Using cached kiwisolver-1.4.4-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB) Collecting cycler>=0.10 Using cached cycler-0.11.0-py3-none-any.whl (6.4 kB)
Collecting pyparsing>=2.2.1
Using cached pyparsing-3.0.9-py3-none-any.whl (98 kB)
Collecting pillow>=6.2.0
Using cached Pillow-9.4.0-cp39-cp39-manylinux_2_28_x86_64.whl (3.4 MB)
Requirement already satisfied: python-dateutil>=2.7 in /home/toaster/mambaforge-pypy3/envs/puma-lab/lib/python3.9/site-packages (from matplotlib>=3.3.0->pycaret) (2.8.2)
Collecting fonttools>=4.22.0
Using cached fonttools-4.38.0-py3-none-any.whl (965 kB) Collecting fastjsonschema Using cached fastjsonschema-2.16.2-py3-none-any.whl (22 kB)
Collecting jsonschema>=2.6
Using cached jsonschema-4.17.3-py3-none-any.whl (90 kB)
Collecting jupyter-core
Using cached jupyter_core-5.1.2-py3-none-any.whl (93 kB)
Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /home/toaster/mambaforge-pypy3/envs/puma-lab/lib/python3.9/site-packages (from numba>=0.55.0->pycaret) (0.39.1)
Requirement already satisfied: setuptools in /home/toaster/mambaforge-pypy3/envs/puma-lab/lib/python3.9/site-packages (from numba>=0.55.0->pycaret) (63.4.1)
Requirement already satisfied: pytz>=2020.1 in /home/toaster/mambaforge-pypy3/envs/puma-lab/lib/python3.9/site-packages (from pandas<1.6.0,>=1.3.0->pycaret) (2022.7)
Collecting tenacity>=6.2.0
Using cached tenacity-8.1.0-py3-none-any.whl (23 kB)
Collecting orjson<4.0.0,>=3.8.0
Using cached orjson-3.8.4-cp39-cp39-manylinux_2_28_x86_64.whl (140 kB)
Collecting trace-updater>=0.0.8
Using cached trace_updater-0.0.9-py3-none-any.whl (185 kB)
Collecting Flask-Cors<4.0.0,>=3.0.10
Using cached Flask_Cors-3.0.10-py2.py3-none-any.whl (14 kB)
Collecting dash<3.0.0,>=2.2.0
Using cached dash-2.7.1-py3-none-any.whl (9.9 MB)
Collecting jupyter-dash>=0.4.2
Using cached jupyter_dash-0.4.2-py3-none-any.whl (23 kB)
Collecting Werkzeug<=2.1.2
Using cached Werkzeug-2.1.2-py3-none-any.whl (224 kB)
Collecting Cython!=0.29.18,!=0.29.31,>=0.29
Using cached Cython-3.0.0a11-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl (2.6 MB)
Collecting urllib3
Using cached urllib3-2.0.0a2-py3-none-any.whl (123 kB)
Requirement already satisfied: six in /home/toaster/mambaforge-pypy3/envs/puma-lab/lib/python3.9/site-packages (from pyod>=0.9.8->pycaret) (1.16.0)
Using cached urllib3-1.26.13-py2.py3-none-any.whl (140 kB)
Collecting idna<4,>=2.5
Using cached idna-3.4-py3-none-any.whl (61 kB)
Requirement already satisfied: certifi>=2017.4.17 in /home/toaster/mambaforge-pypy3/envs/puma-lab/lib/python3.9/site-packages (from requests>=2.27.1->pycaret) (2022.12.7)
Collecting charset-normalizer<3,>=2
Using cached charset_normalizer-2.1.1-py3-none-any.whl (39 kB)
Collecting scikit-learn>=1.0
Using cached scikit_learn-1.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (30.8 MB)
Collecting deprecated>=1.2.13
Using cached Deprecated-1.2.13-py2.py3-none-any.whl (9.6 kB)
Collecting dash-core-components==2.0.0
Using cached dash_core_components-2.0.0-py3-none-any.whl (3.8 kB)
Collecting dash-table==5.0.0
Using cached dash_table-5.0.0-py3-none-any.whl (3.9 kB)
Collecting Flask>=1.0.4
Using cached Flask-2.2.2-py3-none-any.whl (101 kB)
Collecting dash-html-components==2.0.0
Using cached dash_html_components-2.0.0-py3-none-any.whl (4.1 kB)
Collecting wrapt<2,>=1.10
Using cached wrapt-1.14.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (77 kB)
Collecting pyzmq>=17
Using cached pyzmq-25.0.0b1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)
Collecting jupyter-client>=6.1.12
Using cached jupyter_client-8.0.0rc0-py3-none-any.whl (102 kB)
Collecting debugpy>=1.0
Using cached debugpy-1.6.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)
Collecting comm>=0.1.1
Using cached comm-0.1.2-py3-none-any.whl (6.5 kB)
Collecting tornado>=6.1
Using cached tornado-6.2-cp37-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (423 kB)
Collecting nest-asyncio
Using cached nest_asyncio-1.5.6-py3-none-any.whl (5.2 kB)
Collecting parso<0.9.0,>=0.8.0
Using cached parso-0.8.3-py2.py3-none-any.whl (100 kB)
Collecting attrs>=17.4.0
Using cached attrs-22.2.0-py3-none-any.whl (60 kB)
Collecting pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0
Using cached pyrsistent-0.19.3-py3-none-any.whl (57 kB)
Collecting retrying
Using cached retrying-1.3.4-py3-none-any.whl (11 kB)
Collecting ansi2html
Using cached ansi2html-1.8.0-py3-none-any.whl (16 kB)
Collecting ptyprocess>=0.5
Using cached ptyprocess-0.7.0-py2.py3-none-any.whl (13 kB)
Collecting wcwidth
Using cached wcwidth-0.2.5-py2.py3-none-any.whl (30 kB)
Collecting platformdirs>=2.5
Using cached platformdirs-2.6.2-py3-none-any.whl (14 kB)
Collecting asttokens>=2.1.0
Using cached asttokens-2.2.1-py2.py3-none-any.whl (26 kB)
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Collecting pure-eval
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Collecting click>=8.0
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Collecting Flask>=1.0.4
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Collecting tornado>=6.1
Using cached tornado-6.2-cp37-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (423 kB)
Installing collected packages: wcwidth, trace-updater, pure-eval, ptyprocess, pickleshare, kaleido, fastjsonschema, executing, dash-table, dash-html-components, dash-core-components, backcall, zipp, wrapt, widgetsnbextension, Werkzeug, traitlets, tqdm, tornado, threadpoolctl, tenacity, schemdraw, retrying, pyzmq, pyrsistent, pyparsing, pygments, prompt-toolkit, platformdirs, pexpect, parso, orjson, numpy, nest-asyncio, kiwisolver, jupyterlab-widgets, itsdangerous, fonttools, deprecation, decorator, debugpy, Cython, cycler, attrs, asttokens, ansi2html, stack-data, plotly, patsy, matplotlib-inline, jupyter-core, jsonschema, jedi, importlib-metadata, deprecated, contourpy, comm, statsmodels, scikit-learn, nbformat, matplotlib, jupyter-client, ipython, Flask, yellowbrick, sktime, scikit-plot, pyod, pmdarima, lightgbm, ipykernel, imbalanced-learn, Flask-Cors, dash, category-encoders, tbats, jupyter-dash, ipywidgets, plotly-resampler, pycaret
Attempting uninstall: tornado
Found existing installation: tornado 6.1
Uninstalling tornado-6.1:
Successfully uninstalled tornado-6.1
Attempting uninstall: numpy
Found existing installation: numpy 1.23.5
Uninstalling numpy-1.23.5:
Successfully uninstalled numpy-1.23.5
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
dask-cudf 22.12.1 requires cupy-cuda11x, which is not installed.
cuml 22.12.0 requires seaborn, which is not installed.
cudf 22.12.1 requires cupy-cuda11x, which is not installed.
distributed 2022.11.1 requires tornado<6.2,>=6.0.3, but you have tornado 6.2 which is incompatible.
cuml 22.12.0 requires treelite==3.0.1, but you have treelite 3.0.0 which is incompatible.
cuml 22.12.0 requires treelite_runtime==3.0.1, but you have treelite-runtime 3.0.0 which is incompatible.
Successfully installed Cython-3.0.0a11 Flask-2.1.3 Flask-Cors-3.0.10 Werkzeug-2.1.2 ansi2html-1.8.0 asttokens-2.2.1 attrs-22.2.0 backcall-0.2.0 category-encoders-2.5.1.post0 comm-0.1.2 contourpy-1.0.6 cycler-0.11.0 dash-2.7.1 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 debugpy-1.6.5 decorator-5.1.1 deprecated-1.2.13 deprecation-2.1.0 executing-1.2.0 fastjsonschema-2.16.2 fonttools-4.38.0 imbalanced-learn-0.10.1 importlib-metadata-6.0.0 ipykernel-6.19.4 ipython-8.8.0 ipywidgets-8.0.4 itsdangerous-2.1.2 jedi-0.18.2 jsonschema-4.17.3 jupyter-client-8.0.0rc0 jupyter-core-5.1.2 jupyter-dash-0.4.2 jupyterlab-widgets-3.0.5 kaleido-0.2.1 kiwisolver-1.4.4 lightgbm-3.3.4 matplotlib-3.6.2 matplotlib-inline-0.1.6 nbformat-5.7.1 nest-asyncio-1.5.6 numpy-1.22.4 orjson-3.8.4 parso-0.8.3 patsy-0.5.3 pexpect-4.8.0 pickleshare-0.7.5 platformdirs-2.6.2 plotly-5.11.0 plotly-resampler-0.8.4rc1 pmdarima-2.0.2 prompt-toolkit-3.0.36 ptyprocess-0.7.0 pure-eval-0.2.2 pycaret-3.0.0rc6 pygments-2.14.0 pyod-1.0.7 pyparsing-3.0.9 pyrsistent-0.19.3 pyzmq-25.0.0b1 retrying-1.3.4 schemdraw-0.15 scikit-learn-1.1.3 scikit-plot-0.3.7 sktime-0.14.1 stack-data-0.6.2 statsmodels-0.13.5 tbats-1.1.2 tenacity-8.1.0 threadpoolctl-3.1.0 tornado-6.2 tqdm-4.64.1 trace-updater-0.0.9 traitlets-5.8.0 wcwidth-0.2.5 widgetsnbextension-4.0.5 wrapt-1.14.1 yellowbrick-1.5 zipp-3.11.0
@jmakov jmakov changed the title [INSTALL]: cannot install cudf [INSTALL]: cannot install in env with cudf Jan 7, 2023
@jmakov jmakov changed the title [INSTALL]: cannot install in env with cudf [INSTALL]: cannot install in env with cudf and cuml Jan 7, 2023
@jmakov jmakov changed the title [INSTALL]: cannot install in env with cudf and cuml [INSTALL]: PIP errors when installing in env with cudf and cuml Jan 8, 2023
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github-actions bot commented Mar 9, 2023

Stale issue message: This issue will be automatically closed by GitHub Actions in 1 week if there is no further activity.

@ngupta23 ngupta23 added the gpu label Mar 10, 2023
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Not sure if this helps. #3075

You can try the pre-release version to check if it works.

pip install --pre pycaret

@jmakov
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jmakov commented Mar 12, 2023

I'm installing in RAPIDS Docker image and pip install --pre pycaret works whereas the pycaret[full] doesn't.

@beckernick
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Glad the pre-release version works. If you end up creating your own image, you might be interested in trying the cuML and cuDF pip packages if you're using pip for your other packages. pip install cuml-cu11 --extra-index-url=https://pypi.nvidia.com

@jmakov
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jmakov commented Mar 16, 2023 •
edited

This works:
env.yaml:

name: rapids
channels:
  - conda-forge

dependencies:
  - datashader
  - fugue
  - holoviews
  - hvplot
  - ipywidgets
  - jupyter-resource-usage
  - jupyter_contrib_nbextensions
  - jupyterlab_execute_time
  - jupyterlab_widgets
  - jupyterlab-variableinspector
  - nodejs
  - pandas
  - pip
  - polars
  - pyyaml
  - scipy
  - scikit-learn-intelex
  - pip:
    - lazypredict
    - pycaret[full]>=3.0.0rc9

mamba env update -f env.yaml

This throws AttributeError: cython_sources. As per yaml/pyyaml#601 (comment), the pyyaml dependency should be updated to resolve this issue:
env.yaml:

name: rapids
channels:
  - conda-forge

dependencies:
  - datashader
  - fugue
  - holoviews
  - hvplot
  - ipywidgets
  - jupyter-resource-usage
  - jupyter_contrib_nbextensions
  - jupyterlab_execute_time
  - jupyterlab_widgets
  - jupyterlab-variableinspector
  - nodejs
  - pandas
  - pip
  - polars
  - pyyaml
  - scipy
  - scikit-learn-intelex
  - pip:
    - lazypredict
#    - pycaret[full]>=3.0.0rc9

mamba env update -f env.yaml && pip install --pre pycaret[full] (without "full" it works).

This was referenced Mar 16, 2023
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@jmakov
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jmakov commented Mar 18, 2023 •
edited

The above example works because of yaml/pyyaml#601 (comment). Does this mean installing without the --pre flag should be preferable (need to update the docs)?

@jmakov jmakov closed this as completed Mar 18, 2023
@jmakov jmakov reopened this Mar 18, 2023
@jmakov jmakov mentioned this issue Mar 19, 2023
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Stale issue message: This issue will be automatically closed by GitHub Actions in 1 week if there is no further activity.

@github-actions github-actions bot closed this as not planned Won't fix, can't repro, duplicate, stale May 25, 2023
@github-actions github-actions bot locked as resolved and limited conversation to collaborators Jun 24, 2023
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