One Paragraph of project description goes here
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
# Info on how to get your api key (kaggle.json) here: https://github.com/Kaggle/kaggle-api#api-credentials | |
!pip install kaggle | |
api_token = {"username":"USERNAME","key":"API_KEY"} | |
import json | |
import zipfile | |
import os | |
with open('/content/.kaggle/kaggle.json', 'w') as file: | |
json.dump(api_token, file) | |
!chmod 600 /content/.kaggle/kaggle.json | |
!kaggle config path -p /content |
import torch | |
from torchvision import datasets | |
class ImageFolderWithPaths(datasets.ImageFolder): | |
"""Custom dataset that includes image file paths. Extends | |
torchvision.datasets.ImageFolder | |
""" | |
# override the __getitem__ method. this is the method that dataloader calls | |
def __getitem__(self, index): |
Experimental but very promising pip
replacement that handles package managing as well as virtual environments and Python version management.
uv
comes included with uvx
, an alias for uv tool run
. uvx
allos you to install and execute command-line tools on an ephemeral environment.
Note that you don't have to actively install a Python version! uv
will automatically fetch the required Python version for your project.
# ----------------------------------------------------------------------------- | |
# AI-powered Git Commit Function | |
# Copy paste this gist into your ~/.bashrc or ~/.zshrc to gain the `gcm` command. It: | |
# 1) gets the current staged changed diff | |
# 2) sends them to an LLM to write the git commit message | |
# 3) allows you to easily accept, edit, regenerate, cancel | |
# But - just read and edit the code however you like | |
# the `llm` CLI util is awesome, can get it here: https://llm.datasette.io/en/stable/ | |
gcm() { |