Niels Warncke nielsrolf

## setup.sh
# Load .env
cd /workspace
sed -i '/^KVA_STORAGE=/d' .env
echo 'KVA_STORAGE=/workspace/kva_store' >> .env
export $(grep -v '^#' /workspace/.env | xargs)
echo "export $(grep -v '^#' /workspace/.env | xargs)" >> ~/.bashrc

# Github & Huggingface tokens
git config --global credential.helper store
echo "https://${GITHUB_USER}:${GITHUB_TOKEN}@github.com" > ~/.git-credentials

## music.py
import os
import shutil
from mutagen.mp3 import MP3
from mutagen.id3 import ID3, TIT2, TPE1, TALB
import click


def update(file, directory, target_directory):
    if file.endswith('.mp3'):  # Check if the file is an MP3
        path = os.path.join(directory, file)

## llm_reasoning_steps.py
import random


def generate_riddle(n_hints, n_words, loop_length):
    """
    Build a path towards the correct hint and fill the rest with random hints
    """
    loop_length = loop_length - 1
    assert n_words + loop_length < n_hints
    values = list(range(n_words))

## deduplicate.py
import torch
import clip
from PIL import Image
import requests
from io import BytesIO
import click
import os

device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-B/32", device=device)

## PolymarketAnalysis.ipynb

      
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                nielsrolf
                / PolymarketAnalysis.ipynb
            
            
              Created
              November 17, 2021 17:29
            
              
                An analysis of the expected RoI of strategies that trade bets on polymarket.com, purely based on community likelihood estimations
              
          
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## AlgorithmLearningByGradientDescent.ipynb

      
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                nielsrolf
                / AlgorithmLearningByGradientDescent.ipynb
            
            
              Created
              November 17, 2021 17:14
            
              
                Neural networks for logical tasks, but the weights are probabilities for the effective weight to be either 1 or 0 in the forward pass
              
          
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## Empty html page for codex output
<html>
    <head>
        <script>


window.onload = (() => {
  /* Codex output here */

});
        </script>

## siren-examples.ipynb

      
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                nielsrolf
                / siren-examples.ipynb
            
            
              Created
              July 5, 2021 01:25
            
              
                SIREN-Examples.ipynb
              
          
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## gradientgenerator.ipynb

      
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                nielsrolf
                / gradientgenerator.ipynb
            
            
              Created
              June 18, 2021 19:59
            
              
                GradientGenerator.ipynb
              
          
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## vscode.py
"""
"
Poor man's ML workstation:

Recipe:
1. Auto-setup codeserver on colab
2. Auto-mount google drive and store editor settings and extensions directly on drive
3. Treat a drive folder as home directory

Result:
	# Load .env
	cd /workspace
	sed -i '/^KVA_STORAGE=/d' .env
	echo 'KVA_STORAGE=/workspace/kva_store' >> .env
	export $(grep -v '^#' /workspace/.env \| xargs)
	echo "export $(grep -v '^#' /workspace/.env \| xargs)" >> ~/.bashrc

	# Github & Huggingface tokens
	git config --global credential.helper store
	echo "https://${GITHUB_USER}:${GITHUB_TOKEN}@github.com" > ~/.git-credentials
	import os
	import shutil
	from mutagen.mp3 import MP3
	from mutagen.id3 import ID3, TIT2, TPE1, TALB
	import click


	def update(file, directory, target_directory):
	if file.endswith('.mp3'): # Check if the file is an MP3
	path = os.path.join(directory, file)
	import random


	def generate_riddle(n_hints, n_words, loop_length):
	"""
	Build a path towards the correct hint and fill the rest with random hints
	"""
	loop_length = loop_length - 1
	assert n_words + loop_length < n_hints
	values = list(range(n_words))
	import torch
	import clip
	from PIL import Image
	import requests
	from io import BytesIO
	import click
	import os

	device = "cuda" if torch.cuda.is_available() else "cpu"
	model, preprocess = clip.load("ViT-B/32", device=device)
	<html>
	<head>
	<script>


	window.onload = (() => {
	/* Codex output here */

	});
	</script>
	"""
	"
	Poor man's ML workstation:

	Recipe:
	1. Auto-setup codeserver on colab
	2. Auto-mount google drive and store editor settings and extensions directly on drive
	3. Treat a drive folder as home directory

	Result: