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curl https://raw.githubusercontent.com/github/gitignore/master/Python.gitignore > .gitignore

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# basic setup for a single droplet with ssh and firewall config
# reference: https://www.digitalocean.com/community/tutorials/initial-server-setup-with-ubuntu-18-04

1. Login in as root 
$ root@xxx.xxx.xxx.xxx

2. Add a non-root user, give sudo privelages and transfer ssh public key
$ adduser sammy
$ usermod -aG sudo sammy
$ rsync --archive --chown=sammy:sammy ~/.ssh /home/sammy

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# for dev purposes or machines that aren't opened up to the public 
# ref: https://www.humankode.com/ssl/create-a-selfsigned-certificate-for-nginx-in-5-minutes

# 1. make a local directory...create a conf file for localhost config 
$ sudo nano localhost.conf

# paste this in there... 
-------------------------------
[req]
default_bits       = 2048

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# based on the digital ocean guides 
# This is for configuring nginx to proxy_pass to multiple backend servers 
# from a gateway server. 

# 1. install 
$ sudo apt update 
$ sudo apt install nginx 

# 2. adjust firewall 
$ sudo ufw allow 'Nginx Full'

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# based on digital ocean for 18.04

$ sudo apt update
$ sudo apt install postgresql postgresql-contrib

# make a new non-postgres user for the system without switching roles
$ sudo -u postgres createuser --interactive
$ sudo -u postgres createdb <your-user-name>

# setup a db password for user 

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from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker, scoped_session

from contextlib import contextmanager

import logging
l = logging.getLogger(__name__)


class DataAccessLayer(object):

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{
    "configurations": [
        {
            "name": "Linux",
            "includePath": [
                "${workspaceFolder}/**",
                "~/.conan/**"
            ],
            "defines": [],
            "compilerPath": "/usr/bin/clang",

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""" Defines a generic interface for composable widgets. Panels are the view layer, Datastores are the model. 

Downloaders should fill the datastores with data. Panels should register observers to implement parts of the API. This could be 
as simple as downloading and displaying data, or calling custom APIs that call things.
"""
import abc

import IPython
import ipywidgets as widgets
import inspect

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""" A high-level API that wraps the sklearn Pipeline interface. If designed well we should be able to use this base class to 
encapsulate elements of jupyter notebook script. A user should subclass and override the specified hook methods of these classes 
to build a workflow for deployment and benchmarking.  
"""
import abc  
#from sklearn.externals.joblib import parallel_backend  warning says to use parallel backend from joblibe directly... 
from joblib import parallel_backend
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV,\
     ParameterGrid, ParameterSampler

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from typing import Any, List, NamedTuple, Optional
import pydantic 
import datetime
import re

_ym_re = re.compile(r'[0-9]{4}-[0-9]{2}')

class YearMonth(NamedTuple):
    """A custom YearMonth type. Can validate against YYYY-MM, ('YYYY', 'MM') or (int, int). 
    Checks that year and month are valid ISO 8601 ranges.