33eyes

Commit jupyter notebooks code to git and keep output locally

1. Add a filter to git config by running the following command in bash inside the repo:

git config filter.strip-notebook-output.clean 'jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to=notebook --stdin --stdout --log-level=ERROR'  

2. Create a .gitattributes file inside the directory with the notebooks

3. Add the following to that file:

koaning

import uuid
import json
import random

import keras
import numpy as np
import tensorflow as tf
import click

shakna-israel

Obfuscating Python

Obfuscation isn't difficult in most programming languages. It's why we have "good practices" because it is so easy to hide what you mean in badly written code.

Obfuscation tends to be even easier in dynamic languages because of how forgiving they tend to be - and because they tend to give you direct access to the environment so that you can manipulate it.

Today, for fun, I'm going to obfuscate this code:

def _(n):

if n <= 0:

tvst

"""Hack to add per-session state to Streamlit.

Usage
-----

>>> import SessionState
>>>
>>> session_state = SessionState.get(user_name='', favorite_color='black')
>>> session_state.user_name
''

amqdn

pydevops

• 0.1. References
• 0.2. Other cheatsheets
• 0.3. Manage multiple gcloud config configurations
  • 0.3.1. Switch gcloud context with gcloud config
• 0.4. Credentials
• 0.5. info
• 0.6. projects
• 0.7. zones & regions
• 0.8. Organization
• 0.9. billing

chssch

# This small script shows how to use AllenNLP Semantic Role Labeling (http://allennlp.org/) with SpaCy 2.0 (http://spacy.io) components and extensions
# Script installs allennlp default model
# Important: Install allennlp form source and replace the spacy requirement with spacy-nightly in the requirements.txt
# Developed for SpaCy 2.0.0a18

from allennlp.commands import DEFAULT_MODELS
from allennlp.common.file_utils import cached_path
from allennlp.service.predictors import SemanticRoleLabelerPredictor
from allennlp.models.archival import load_archive

cbaziotis

def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:
        x (): input
        kernel (): weights
    Returns:
    """
    if K.backend() == 'tensorflow':

roycoding

rgl

bash -c 'while [[ "$(curl -s -o /dev/null -w ''%{http_code}'' localhost:9000)" != "200" ]]; do sleep 5; done'

# also check https://gist.github.com/rgl/c2ba64b7e2a5a04d1eb65983995dce76