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

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

Dataset

This dataset was created by FabienDaniel

Contents

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

transformers-4.44.0.dev0 and all dependencies.

Install pip install --no-index --find-links=../input/transformers-4.19.0.dev0 transformers

For creating clone transformers repository git clone https://github.com/huggingface/transformers

Build wheel for transformer and all dependencies pip wheel --no-cache-dir --pre --wheel-dir=./transformers-4.44.0.dev0/ ./transformers

Dataset

This dataset was created by rstark

Contents

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

Dataset

This dataset was created by minwo3o

Contents

Dataset

This dataset was created by cuyler72

Contents

Dataset

This dataset was created by awxlong

Released under Apache 2.0

Contents

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)

Context

For offline installation of textstat package.

Content

Pyphen needs to be installed first and then Textstat. Both using pip install, offline.

Acknowledgements

https://pypi.org/project/textstat/

Dataset

This dataset was created by Dmytro Budašnyj

Contents

It contains the following files:

mordred is a Python library for molecular descriptor calculation (online documentation). This dataset is intended for use in internet-disabled (i.e., offline) Kaggle notebooks.

# install mordred 1.2.0 and networkx 2.8.8
!pip install mordred --no-index --find-links=file:///kaggle/input/mordred-1-2-0-py3-none-any/

How to download weights for an older Yolov5 version

1. Go to this location: https://github.com/ultralytics/yolov5/releases
2. Scroll down to v4.0.
3. Scroll down and click on Assets.
4. Click on the weights to download.

How to download a Yolo model. This downloads the latest Yolo version: https://www.kaggle.com/code/vbookshelf/exp-90-covid-downloading-yolo-v5/notebook?scriptVersionId=66895104

Dataset

This dataset was created by Slawek Biel

Contents

It contains the following files:

Dataset

This dataset was created by Arppegian Legato

Contents

Dataset

This dataset was created by Doomsday

Contents

Dataset

This dataset was created by Mahmoud Abdelshafy

Released under Apache 2.0

Contents