# Dash app slow once deployed on a virtual machine

**URL:** <https://community.plotly.com/t/dash-app-slow-once-deployed-on-a-virtual-machine/31134>\
**Category:** 📊 Plotly Python\
**Created:** [November 10, 2019, 2:37pm UTC](https://community.plotly.com/t/dash-app-slow-once-deployed-on-a-virtual-machine/31134 "2019-11-10T14:37:53Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![Silvano](https://avatars.discourse-cdn.com/v4/letter/s/3e96dc/32.png) [@Silvano](https://community.plotly.com/u/Silvano)\
**Post date:** [November 10, 2019, 2:37pm UTC](https://community.plotly.com/t/dash-app-slow-once-deployed-on-a-virtual-machine/31134/1 "2019-11-10T14:37:53Z")

</div>

Hi All,  
I am new in this community and quite fresh with python and dash - I saw some similar posts but could not solve my specific problem and I thought to open a dedicated thread - hope this is fine.

* * *

I wanted to create an app to allow the visualization of some data coming from a CSV file - that the user can select.  
The CSV file is can be relative big (30-40 columns and 200K lines)

Here the file I use for my tests: [0\_Test.csv](http://pautasso.eu/Download/0_Test.csv)

The app I have created it works fine locally but once I have deployed on a virtual machine it gets incredibly slow.  
I did some tests and I do not think it is due to the computational time to preform the calculations on the VM but I have the feeling I am sending relative big amounts of data from client to VM and viceversa. Therefore, the limitation should be the internet connection (that’s only my feeling).

looking at different posts and forums I saw people suggesting to use this commands to compute directly on the client browser:

```auto
app.config['suppress_callback_exceptions'] = True
app.css.config.serve_locally = True
app.scripts.config.serve_locally = True

```

I have tried to use them, but I did not see difference on performance.  
(and since to me it is not clear what they do, I commented them in my code)

can anyone please help me to get the code here below more efficient?

```auto
import base64
import datetime
import io
import plotly
import dash
from dash.dependencies import Input, Output, State
import dash_core_components as dcc
import dash_html_components as html
import dash_table
import time
import plotly.graph_objects as go
import numpy as np

import pandas as pd

def update_figure(df,value):
	if value is not None:
		x_axes= df['DateTime']
		figure = {
						'data': [{'type': 'Scattergl', 'x': x_axes,'y': df[i], 'name': i }for i in value]
						 }
	else: figure = {}
	
	return figure

def filter_Spikes(df,value_spike):
	if value_spike is not None:
		for column in value_spike:
			print(column +'...processing')
			try:
				mask = df[column].rolling( window=3, center=True).mean().fillna(method='bfill').fillna(method='ffill')
				difference = np.abs(df[column] - mask)
				outlier_idx = difference > ((df[column].std()*5))
				print(outlier_idx.sum())
				for col in df.columns:
					df[col][outlier_idx]= np.nan
			except:
				continue
		
	
	return df

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash( __name__ , external_stylesheets=external_stylesheets)
#app.config['suppress_callback_exceptions'] = True
#app.css.config.serve_locally = True
#app.scripts.config.serve_locally = True

app.layout = html.Div([
	dcc.Upload(
        id='upload-data',
        children=html.Div([
            'Drag and Drop or ',
            html.A('Select Files')
        ]),
	style={
            'width': '95%',
            'height': '60px',
            'lineHeight': '60px',
            'borderWidth': '1px',
            'borderStyle': 'dashed',
            'borderRadius': '5px',
            'textAlign': 'center',
            'margin': '10px'
        },
        # Allow multiple files to be uploaded
	multiple=False
    ),
	dcc.Dropdown(
                id='data_drop_down',
                multi=True,
				style = {'width': '70%'}),
	
	dcc.Dropdown(
                id='data_drop_down_spikes',
                multi=True,
				style = {'width': '70%'}),
	
	html.Div(
			id='output-data-upload'),
	
	dcc.Graph(
				id='graph',
				style={"height" : "750px", "width" : "100%"}
				),   
		
])

@app.callback([Output('output-data-upload', 'children'),
			  Output('data_drop_down', 'options'),
			  Output('data_drop_down', 'disabled'),
			  Output('graph', 'figure'),
			  Output('data_drop_down_spikes', 'options')],
              [Input('upload-data', 'contents'),
			   Input('data_drop_down', 'value'),
			   Input('data_drop_down_spikes', 'value')]
              )
def parse_contents(contents, value, value_spike):
	if contents is not None:
		content_type, content_string = contents.split(',')
		if content_string[-2:] == "==":
			decoded = base64.b64decode(content_string)
		else:
			decoded = base64.b64decode(content_string+'==')
		df = pd.read_csv(io.StringIO(decoded.decode('"ISO-8859-1"')),sep=";", encoding = "ISO-8859-1", header=19)
		
		for i in df.columns:
			if 'Date'.casefold() in i.casefold():
				Date_col = i
			if 'Time'.casefold() in i.casefold():
				Time_col = i 
		df["DateTime"] = df[Date_col] + " " + df[Time_col]
		df.index = pd.to_datetime(df["DateTime"])
		
			
		
		options=[{'label': i, 'value': i} for i in df.columns]
		options_spikes=options
		disabled = False
		if value_spike is not None: 
			df=filter_Spikes(df, value_spike).copy()
		
		Start_figure = time.clock()
		figure=update_figure(df,value)
		End_figure = time.clock()
	
		

		children = html.Div(['time update_figure:', str(End_figure-Start_figure)])	
		return children, options, disabled, figure, options_spikes
	else:
		children = html.Div(['No data has been uploaded'])		
		
		options=[{'label': 'None', 'value': 'None'}]
		options_spikes=options

		disabled = True
		
		figure=update_figure(pd.DataFrame(), value)
		
		return children, options, disabled, figure, options_spikes
	
server = app.server
if __name__ == ' __main__':
    app.run_server(debug=False)

```

Thank you very much!  
This is really a great community! 🙂