danlester

import panel as pn
import param
import ast
import json

HSPACER = pn.Spacer(height=4, background="#261342")
pn.extension("tabulator")

class EmployeeOverviewAdminRoot(param.Parameterized):
    """Employee Overview App for Administrators"""

danlester

terraform {
  required_providers {
    kubernetes = {
      source  = "hashicorp/kubernetes"
      version = "2.7.1"
    }
  }
  required_version = ">= 1.0"
}

danlester

import os
from pathlib import Path
jupyter_user = os.environ['JUPYTERHUB_USER']

def make_files(depth, breadth, start_folder, key, max_depth=3):
    print(f"Make Files at depth {depth}")
    for i in range(0,breadth):
        with Path(start_folder, f"testfile_{key}_{depth}_{i}.txt").open("wt") as f:
            f.write("Here is the file.")
        if depth < max_depth:

danlester

danlester

import requests

api_url = 'https://myjupyterhub.net/hub/api'

token = '<token from token menu of an admin user>'

# List all groups

r = requests.get(api_url + '/groups',
    headers={

danlester

import dash
from dash.dependencies import Input, Output
import dash_html_components as html
from datetime import datetime

app = dash.Dash(__name__)

start_time = datetime.now()

app.layout = html.Div(children=[

danlester

import streamlit as st

@st.cache
def get_start_time():
    from datetime import datetime
    return datetime.now()

start_time = get_start_time()
st.write(start_time)

danlester

import streamlit as st

from datetime import datetime
start_time = datetime.now()
st.write(start_time)

count = 0

if st.button('Increment'):
    count += 1

danlester

c.JupyterHub.spawner_class = 'dockerspawner.DockerSpawner'
 
c.DockerSpawner.image = 'ideonate/jh-voila-oauth-singleuser:latest'
 
from jupyter_client.localinterfaces import public_ips
 
c.JupyterHub.hub_ip = public_ips()[0]

c.DockerSpawner.name_template = "{prefix}-{username}-{servername}"

danlester

CSV_COLUMNS = ['p{}'.format(i) for i in range(30)] + ['Image']
CSV_COLUMN_DEFAULTS = [[0.0]]*30 + [['']]

def parse_csv(rows_string_tensor):   
    columns = tf.decode_csv(rows_string_tensor, record_defaults=CSV_COLUMN_DEFAULTS)
    raw_features = dict(zip(CSV_COLUMNS, columns))
    
    image_str_array_sparse = tf.string_split([raw_features['Image']])
    image_str_array = tf.sparse_to_dense(image_str_array_sparse.indices, image_str_array_sparse.dense_shape, image_str_array_sparse.values, '')
    image_str_array = image_str_array[0]