This dataset is the Python wheel package file for PyTorch Geometric external library (to install PyG just pip install torch_geometric). PyTorch Geometric is the torch implementation used to build the graph neural network. For details, please refer to torch_geometric.👋

Note: These library are not required to install PyG. I compile the wheel files because it takes a long to install them. If you want to use a specific version, please refer to this notebook.

Context

If you need to install l5kit with no internet, you can attach this dataset and use pip to install from this dataset.

How to use

!pip install --no-index -f /kaggle/input/kaggle-l5kit-110 pip==20.2.3
!pip install --no-index -f /kaggle/input/kaggle-l5kit-110 l5kit

Package to Install Autogluon 0.7.0 without internet for kaggle competitions

Add dataset and run the following command on notebook

!pip install --no-index --find-links /kaggle/input/autogluon-0-7-0-offline-setup-wheel/Autogluon_0_7_0_setup/Autogluon_0_7_0 autogluon

Reference: https://www.kaggle.com/code/anandsiva/autogluon-full-offline-setup

Install staintools 2.1.2 offline in Kaggle notebooks. The wheels were built in a Kaggle notebook in August 2022.

Just add this dataset and run: !pip install /kaggle/input/staintools-offline/spams-2.6.5.4-cp37-cp37m-linux_x86_64.whl !pip install /kaggle/input/staintools-offline/staintools-2.1.2-py3-none-any.whl

Latest polars sources. This can be included when you are on an offline notebook.

I'll try to keep this up-to-date, but if there is a newer version, please do tell me and I'll update it.

Current version: 1.31.0

To install in your notebook: jupyter !pip install -q --no-index --find-links /kaggle/input/offline-polars polars

License: MIT

This dataset contains .whl files for installing langchain on Kaggle. Compatible with Python 3.10

Examples to use (without Internet connection):

!pip install langchain --no-index --find-links=file:///kaggle/input/langchain-whl-llm/

!pip install -U /kaggle/input/langchain-whl-llm/bitsandbytes-0.43.1-py3-none-manylinux_2_24_x86_64.whl -qq

Dataset

This dataset was created by Vijayabhaskar J

Contents

Installation

!pip install --no-index ../input/cellposewheels/cellpose-0.7.2-py3-none-any.whl --find-links=../input/cellposewheels

Thanks to @slawekbiel for pioneering in his Inference307 Kernel.

License

The license for each wheel is available within their respective package.

This dataset provides the dicomsdl package version 0.109.2 for use offline.

Usage

pip install /kaggle/input/dicomsdl--0-109-2/dicomsdl-0.109.2-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl

All of the most recent releases from Hugging Face. Updated daily!

• transformers
• accelerate
• peft
• datasets
• bitsandbytes
• sentence-transformers
• tokenizers
• trl

This also includes the most updated source versions from github. These have the "dev" tag in the filename.

Install them offline in your notebook by using the following command (changing the path to whichever dataset you want):

!pip install --no-deps --no-index /kaggle/input/hf-libraries/bitsandbytes/bitsandbytes-0.43.0-py3-none-manylinux_2_24_x86_64.whl

To use datasets, you will also need to install the hotfix.

!pip install --no-deps --no-index /kaggle/input/hf-libraries/datasets/pyarrow_hotfix-0.6-py3-none-any.whl

Note

Every so often, I will delete the oldest versions of bitsandbytes because those filesizes are relatively large (100MB each)

Install files for Detecto and pascal-voc-writer. Thanks to the authors for great tools.
Project links:
* Detecto
* pascal-voc-writer

License: MIT License (MIT)

Version: 8.0.139

Usage: python !pip install /kaggle/input/ultralytics-whl/ultralytics-8.0.139-py3-none-any.whl GitHub: https://github.com/ultralytics/ultralytics PyPi: https://pypi.org/project/ultralytics/

Steps to install audio-metadata 0.11.1

Install audio-metadata 0.11.1

https://pypi.org/project/audio-metadata/

!pip install ../input/pythonpackageaudiometadata-0111/pytzdata-2020.1-py2.py3-none-any.whl

!pip install ../input/pythonpackageaudiometadata-0111/pendulum-2.1.2-cp37-cp37m-manylinux1_x86_64.whl

!pip install ../input/pythonpackageaudiometadata-0111/bidict-0.21.2-py2.py3-none-any.whl

!pip install ../input/pythonpackageaudiometadata-0111/pprintpp-0.4.0-py2.py3-none-any.whl

!pip install ../input/pythonpackageaudiometadata-0111/tbm_utils-2.6.0-py3-none-any.whl

!pip install ../input/pythonpackageaudiometadata-0111/bitstruct-8.11.0/bitstruct-8.11.0

!pip install ../input/pythonpackageaudiometadata-0111/audio_metadata-0.11.1-py3-none-any.whl

This dataset contains the Keras Self-Attention Python package and dependencies. To install in a notebook without internet.

Check the example notebook for the story: https://www.kaggle.com/donkeys/pip-install-package-from-dataset-no-internet

Dataset

This dataset was created by Đàm Trọng Tuyên

Contents

!pip install ../input/pytorchvideo015/av-9.2.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl !pip install ../input/pytorchvideo015/iopath-0.1.10-py3-none-any.whl !pip install ../input/pytorchvideo015/fvcore-0.1.5.post20220512-py3-none-any.whl !pip install ../input/pytorchvideo015/parameterized-0.8.1-py2.py3-none-any.whl !pip install ../input/pytorchvideo015/pytorchvideo-0.1.5-py3-none-any.whl

Context

For use in code competitions with no Internet. Add Data this dataset. Install with !mkdir -p /tmp/pip/cache/ !cp ../input/hdbscan0828whl/hdbscan-0.8.28-cp37-cp37m-linuxx8664.whl /tmp/pip/cache/ !pip install --no-index --find-links /tmp/pip/cache/ hdbscan

Inspiration

Latest version 0.8.28 like https://www.kaggle.com/datasets/something4kag/hdbscan0827-whl import hdbscan no longer worked in notebooks, not available in docker now. Made it work!

Acknowledgements

https://github.com/scikit-learn-contrib/hdbscan

License for HDBSCAN

scikit-learn-contrib/hdbscan is licensed under the BSD 3-Clause "New" or "Revised" License

A permissive license similar to the BSD 2-Clause License, but with a 3rd clause that prohibits others from using the name of the copyright holder or its contributors to promote derived products without written consent.

Dataset

This dataset was created by tensor choko

Contents

Dataset

This dataset was created by minwo3o

Contents

Context

This is a Python package for manipulating 2-dimensional tabular data structures (aka data frames). It is close in spirit to pandas or SFrame; however we put specific emphasis on speed and big data support. As the name suggests, the package is closely related to R's data.table and attempts to mimic its core algorithms and API.

Content

The wheel file for installing datatable v0.11.0

Installation

!pip install ../input/python-datatable/datatable-0.11.0-cp37-cp37m-manylinux2010_x86_64.whl > /dev/null

Using

import datatable as dt
data = dt.fread("filename").to_pandas()

Acknowledgements

https://github.com/h2oai/datatable

Documentation

https://datatable.readthedocs.io/en/latest/index.html

License

https://github.com/h2oai/datatable/blob/main/LICENSE