title: A Rainforest in Machine Learning author: Arjoonn Sharma @ CLAP Research patat: incrementalLists: true ...
View submit.py
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import requests | |
name = 'tfidf_benchmark' | |
contest = 'marco-search' | |
fpath = 'output' | |
files = {"upload": open(fpath, 'rb')} | |
url = 'https://models.clapresearch.com/api/submit' | |
requests.post(url, data={"user": name, "contest": contest}, |
View sc.bash
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sudo apt install libncurses5-dev libgnome2-dev libgnomeui-dev \ | |
libgtk2.0-dev libatk1.0-dev libbonoboui2-dev \ | |
libcairo2-dev libx11-dev libxpm-dev libxt-dev python-dev \ | |
python3-dev ruby-dev lua5.1 liblua5.1-dev libperl-dev git | |
sudo apt remove vim vim-runtime gvim | |
git clone https://github.com/vim/vim.git | |
cd vim/src |
View mailer.py
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# encoding: utf-8 | |
# Install: pipenv install requests bs4 --python 3 | |
# Usage: python mailer.py https://arxiv.org/abs/1805.12076 | |
# Always supply an arxiv ABS page!!! | |
import sys | |
import os | |
import smtplib | |
import requests |
View goldrush.py
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import aiohttp | |
import asyncio | |
import async_timeout | |
from tqdm import tqdm | |
TIMEOUT_LIMIT = 50 | |
async def _get(url, json, pbar): | |
async with aiohttp.ClientSession() as session: | |
for _ in range(100): |
View trainer.py
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'''Trains a simple convnet on the MNIST dataset. | |
Gets to 99.25% test accuracy after 12 epochs | |
(there is still a lot of margin for parameter tuning). | |
16 seconds per epoch on a GRID K520 GPU. | |
''' | |
import os | |
from __future__ import print_function | |
import keras |
View bulker.py
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import asyncio | |
import aiohttp | |
import async_timeout | |
headers = {} | |
links = [] # TODO: Populate this somehow with urls | |
done = asyncio.Queue() |
View dashboard.yml
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dashboard "Food": | |
- h1 text: Food | |
- h2 text: By caloric content | |
- 2 columns: | |
- attr:query: '[to_entries | .[] | {"component": "rows", "data": [ | |
{"component": "text", "args": {"tagName": "h3"}, "data": .key}, | |
{"component": "chart", "args": {"type": "line"}, "data": {"columns": .value}} | |
]}]' | |
- data: https://gist.githubusercontent.com/theSage21/9ca3a5b55ec324599d1baea229c19e27/raw/e81c3109a2c72ad5d718f18e846d6044022e91d7/data.json |
View dash.yml
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dashboard "Food": | |
- h1 text: Food | |
- h2 text: By caloric content | |
- 3 columns: | |
- attr:query: '[to_entries | .[] | {"component": "rows", "data": [ | |
{"component": "text", "args": {"tagName": "h3"}, "data": .key}, | |
{"component": "chart", "args": {"type": "pie"}, "data": {"columns": .value}} | |
]}]' | |
- data: https://gist.githubusercontent.com/theSage21/9ca3a5b55ec324599d1baea229c19e27/raw/bd8fb11a93f7607265c38daf273184dcd0b8a83e/data.json |
View data.json
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{ | |
"Bananas": [1,2,3, 4, 5], | |
"Tofu" : [1, 1.5, 3], | |
} |