[Kaggle] Kaggle installs PyG | Extract the library files you installed

1. Problem description

Although you can directly use pip to download and install, but Kaggle's BUG causes some libraries to be slow to install online.

Taking PyG as an example, online installation often requires 30-60分钟left and right, but if you upload the required wheel file to Kaggle and use local installation, the 1分钟installation can be completed.

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2. Resolution steps

2.1 Uninstall the default environment

First of all, pyg has requirements for torch and cuda versions, but the default environment of kaggle does not support pyg, so first uninstall the torch that comes with kaggle

pip uninstall torch torchvision torchaudio --y

2.2 Install the appropriate version

Install torch, the recommended version is 1.12.1-cu113

pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113

2.3 Install PyG

install pyg

For the first time, turn on the GPU acceleration of kaggle, and then run

pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv torch_geometric -f https://data.pyg.org/whl/torch-1.12.0+cu113.html

After waiting for a long time, it shows that the installation is successful

Test it, the output Data(edge_index=[2, 4], x=[3, 1])is that the installation is successful

import torch
from torch_geometric.data import Data

edge_index = torch.tensor([[0, 1, 1, 2],
                           [1, 0, 2, 1]], dtype=torch.long)
x = torch.tensor([[-1], [0], [1]], dtype=torch.float)

data = Data(x=x, edge_index=edge_index)
print(data)

2.4 Extract the installation cache wheel file

Create save directory

import os
os.mkdir('files')

Copy the file to the output folder, then you can download it in output

!cp -r  ~/.cache/pip/wheels/*/*/*/*/*.whl /kaggle/working

After the download is complete, put these whl files into a folder, and then download the following two whl files (click to download)

• pyg_lib
• torch_sparse

Compress all 6 whl files downloaded above into a compressed package, create a dataset in kaggle, and upload the compressed package

Just add the compressed package to the notebook that needs to use pyg

2.5 Install the whl file in the compressed package

!pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv torch_geometric --no-index --find-links=file:///kaggle/input/pyg-packages-torch1121-cu113/pyg-packages

You can also use my dataset directly, the name ispyg-packages-torch1.12.1+cu113