【pip check】Dependencies confliction

[h1r0ml]

🐛 Bug

It appears that the Python packages in the Kaggle notebook environment are encountering numerous dependency issues. The requirements.txt file, or an equivalent tool for package management, may need to be updated to address these problems.

To Reproduce

!pip check

Expected behavior

no dependency error

Additional context

As of 13th Nov 2023, you can see the conflictions below:

apache-beam 2.46.0 has requirement dill<0.3.2,>=0.3.1.1, but you have dill 0.3.7.
beatrix-jupyterlab 2023.814.150030 has requirement jupyter-server~=1.16, but you have jupyter-server 2.9.1.
beatrix-jupyterlab 2023.814.150030 has requirement jupyterlab~=3.4, but you have jupyterlab 4.0.5.
boto3 1.26.100 has requirement botocore<1.30.0,>=1.29.100, but you have botocore 1.31.64.
cloud-tpu-client 0.10 has requirement google-api-python-client==1.8.0, but you have google-api-python-client 2.106.0.
gcsfs 2023.6.0 has requirement fsspec==2023.6.0, but you have fsspec 2023.10.0.
google-cloud-aiplatform 0.6.0a1 has requirement google-api-core[grpc]<2.0.0dev,>=1.22.2, but you have google-api-core 2.11.1.
google-cloud-automl 1.0.1 has requirement google-api-core[grpc]<2.0.0dev,>=1.14.0, but you have google-api-core 2.11.1.
jupyterlab 4.0.5 has requirement jupyter-lsp>=2.0.0, but you have jupyter-lsp 1.5.1.
jupyterlab-lsp 5.0.0 has requirement jupyter-lsp>=2.0.0, but you have jupyter-lsp 1.5.1.
jupyterlab-lsp 5.0.0 has requirement jupyterlab<5.0.0a0,>=4.0.6, but you have jupyterlab 4.0.5.
kfp 2.0.1 has requirement google-cloud-storage<3,>=2.2.1, but you have google-cloud-storage 1.44.0.
libpysal 4.9.2 has requirement packaging>=22, but you have packaging 21.3.
libpysal 4.9.2 has requirement shapely>=2.0.1, but you have shapely 1.8.5.post1.
momepy 0.6.0 has requirement shapely>=2, but you have shapely 1.8.5.post1.
pins 0.8.3 has requirement fsspec<2023.9.0,>=0.8.0, but you have fsspec 2023.10.0.
pymc3 3.11.5 has requirement numpy<1.22.2,>=1.15.0, but you have numpy 1.24.3.
pymc3 3.11.5 has requirement scipy<1.8.0,>=1.7.3, but you have scipy 1.11.3.
pytoolconfig 1.2.6 has requirement packaging>=22.0, but you have packaging 21.3.
tensorflowjs 4.12.0 has requirement packaging~=23.1, but you have packaging 21.3.
ydata-profiling 4.5.1 has requirement numpy<1.24,>=1.16.0, but you have numpy 1.24.3.

[calderjo]

Due to the large selection of ml & data science packages in our image, conflicts are more likely to happen. Some packages are not as strict and can operate with an older version (even if a warning appears). If there is a package that is broken and requires a look from the docker image team, please feel free to create new issue with more context on the package you need fixed.

closing issue due to issue's age with no follow-up