aksh-at

# Quick snippet to connect to a Jupyter notebook server running inside a Modal container,
# especially useful for exploring the contents of Modal shared volumes.
# This uses https://github.com/ekzhang/bore to expose the server to the public internet.
#
# Steps
# -----
# 1. (Recommended) Change `JUPYTER_TOKEN` to a different value; default is 1234.
# 2. `modal run jupyter-bore.py`
# 3. Find the `bore.pub` URL printed in the logs, and navigate to it using your browser.

takuma104

import torch
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np

plt.rcParams["figure.figsize"] = (10,5)
plt.rcParams['figure.facecolor'] = 'white'

def generate_tuxedo_cat_picture(fn_prefix, seed=0):

jdbcode

# Source: https://stackoverflow.com/a/33597599/5391200

from pylab import *

cmap = cm.get_cmap('seismic', 5)  # matplotlib color palette name, n colors
for i in range(cmap.N):
    rgb = cmap(i)[:3]  # will return rgba, we take only first 3 so we get rgb
    print(matplotlib.colors.rgb2hex(rgb))

cmap = cm.get_cmap('plasma', 101)

BIGBALLON

from PIL import Image, ImageOps


def padding(img, expected_size):
    desired_size = expected_size
    delta_width = desired_size - img.size[0]
    delta_height = desired_size - img.size[1]
    pad_width = delta_width // 2
    pad_height = delta_height // 2
    padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)

artificialsoph

Log into your server with ssh, something like

ssh -i "my_secret.pem" ubuntu@12.123.12.123

If it's a new server, you'll need to install a few things.

Install conda with

mda590

"""
Produces load on all available CPU cores.
Requires system environment var STRESS_MINS to be set.
"""

from multiprocessing import Pool
from multiprocessing import cpu_count
import time
import os

benzap

#!/bin/env python
# Requires: youtube_dl module
# Requires: ffmpeg
# Usage:
#
# python youtube2mp3.py <URL>, ...
# 
# Example:
# 
# python youtube2mp3.py https://www.youtube.com/watch?v=dQw4w9WgXcQ

dsal1951

def calc_lift(x,y,clf,bins=10):
	"""
	Takes input arrays and trained SkLearn Classifier and returns a Pandas
	DataFrame with the average lift generated by the model in each bin
	
	Parameters
	-------------------
	x:    Numpy array or Pandas Dataframe with shape = [n_samples, n_features]

	y:    A 1-d Numpy array or Pandas Series with shape = [n_samples]

tgarc

ipython nbconvert --to markdown <notebook>.ipynb --config jekyll.py

magicznyleszek

CSS Selectors Cheatsheet

Hi! If you see an error or something is missing (like :focus-within for few years :P) please let me know ❤️

Element selectors

Element -- selects all h2 elements on the page

h2 {