Running a Bokeh Server


The architecture of Bokeh is such that high-level “model objects” (representing things like plots, ranges, axes, glyphs, etc.) are created in Python, and then converted to a JSON format that is consumed by the client library, BokehJS. (See Defining Key Concepts for a more detailed disussion.) By itself, this flexible and decoupled design offers advantages, for instance it is easy to have other languages (R, Scala, Lua, ...) drive the exact same Bokeh plots and visualizations in the browser.

However, if it were possible to keep the “model objects” in python and in the browser in sync with one another, then more additional and powerful possibilities immediately open up:

  • respond to UI and tool events generated in a browser with computations or queries using the full power of python
  • automatically push updates the UI (i.e. widgets or plots), in a browser
  • use periodic, timeout, and asychronous callbacks drive streaming updates

This capability to synchronize between python and the browser is the main purpose of the Bokeh Server.

Use Case Scenarios

Now that we know what the Bokeh server is for, and what it is capable of doing, it’s worth considering a few different scenarios when you might want to use a Bokeh Server.

Local or Individual Use

One way that you might want to use the Bokeh server is during exploratory data analysis, possibly in a Jupyter notebook. Alternatively, you might want to create a small app that you can run locally, or that you can send to colleagues to run locally. The Bokeh server is very useful and easy to use in this scenario. All of the methods here below can be used effectively:

Specifying output_server

Connecting with bokeh.client

Building Bokeh Applications

For the most flexible approach, that could transition most directly to a deployable application, it is suggested to follow the techniques in Building Bokeh Applications.

Creating Deployable Applications

Another way that you might want to use the Bokeh server is to publish interactive data visualizations and applications that can be viewed and used by a wider audience (perhaps on the internet, or perhaps on an internal company network). The Bokeh Server is also well-suited to this usage, and you will want to first consult the section

Building Bokeh Applications

to understand how to create Bokeh Applications, and then refer to the section

Deployment Scenarios

for information on how to deploy the Bokeh server with your application.

Shared Publishing

Both of the scenarios above involve a single creator making applications on the server, either for their own local use, or for consumption by a larger audience. Another scenario is the case where a group of several creators all want publish different applications to the same server. This is not a good use-case for single Bokeh server. Because it is possible to create applications that execute arbitrary python code, process isolation and security concerns make this kind of shared tenancy prohibitive.

In order to support this kind of multi-creator, multi-application environment, one approach is to build up infrastructure that can run as many Bokeh servers as-needed, either on a per-app, or at least a per-user basis. It is possible that we may create a public service to enable just this kind of usage in the future, and it would also certainly be possible for third parties to build their own private infrastructure to do so as well, but that is beyond the scope of this User’s Guide.

Another possibility is to have a single centrally created app (perhaps by an organization), that can access data or other artifacts published by many different people (possibly with access controls). This sort of scenario is possible with the Bokeh server, but often involves integrating a Bokeh server with other web application frameworks. See

Integration with Web App Frameworks

for general information, and a complete example at

Specifying output_server

With the previous Flask-based Bokeh server, there was a function that could be used to load Bokeh documents in to a running Bokeh server programmatically. The function still exists and works, however its utility is somewhat limited.

First, we must have a Bokeh Server running. To do that, execute the command:

bokeh serve

When the server starts you should see output similar to the following on your console:

DEBUG:bokeh.server.tornado:Allowed Host headers: ['localhost:5006']
DEBUG:bokeh.server.tornado:These host origins can connect to the websocket: ['localhost:5006']
DEBUG:bokeh.server.tornado:Patterns are: [<<< several endpoints >>>]
INFO:bokeh.command.subcommands.serve:Starting Bokeh server on port 5006 with applications at paths ['/']

This starts the Bokeh Server in a mode where it can easily accept connections and data from any script that uses output_server() to connect to it.

A simple script that illustrates this is here:

from bokeh.plotting import figure, show, output_server

p = figure(title="Server Plot")[1, 2, 3], [4, 5, 6])



Because the script calls show, a browser tab is automatically opened up to the correct URL to view the document, which in this case is:


Connecting with bokeh.client

With the new Tornado and websocket-based server introduced in Bokeh 0.11, there is also a proper client API for interacting directly with a Bokeh Server. This client API can be used to trigger updates to the plots and widgets in the browser, either in response to UI events from the browser or as a results of periodic or asynchronous callbacks. As before, the first step is to start a Bokeh Server:

bokeh serve

Next, let’s look at a complete example, and then examine a few key lines individually:

import numpy as np
from numpy import pi

from bokeh.client import push_session
from bokeh.driving import cosine
from bokeh.plotting import figure, curdoc

x = np.linspace(0, 4*pi, 80)
y = np.sin(x)

p = figure()
r1 = p.line([0, 4*pi], [-1, 1], color="firebrick")
r2 = p.line(x, y, color="navy", line_width=4)

# open a session to keep our local document in sync with server
session = push_session(curdoc())

def update(step):["y"] = y * step
    r2.glyph.line_alpha = 1 - 0.8 * abs(step)

curdoc().add_periodic_callback(update, 50) # open the document in a browser

session.loop_until_closed() # run forever

If you run this script, you will see a plot with an animated line appear in a new browser tab. The first half of the script is like most any script that uses the bokeh.plotting interface. The first interesting line is:

session = push_session(curdoc())

This line opens a new session with the Bokeh Server, initializing it with our current Document. This local Document will be automatically kept in sync with the server. The next few lines define and add a periodic callback to be run every 50 milliseconds:

def update(step):["y"] = y * step
    r2.glyph.line_alpha = 1 - 0.8 * abs(step)

curdoc().add_periodic_callback(update, 50)

Next, analogous to, there is this a show() on session objects that will automatically open a browser tab to display the synced Document.

Finally, we need to tell the session to loop forever, so that the periodic callbacks happen:

session.loop_until_closed() # run forever

This mode of interaction can be very useful, especially for individual exploratory data analysis (e.g, in a Juypter notebook). However, it does have some drawbacks when compared to the Application technique described below. In particular, in addition to network traffic between the browser and the server, there is network traffic between the python client and the server as well. Depending on the particular usage, this could be a significant consideration.

Building Bokeh Applications

By far the most flexible way to create interactive data visualizations using the Bokeh server is to create Bokeh Applications, and serve them with the bokeh serve command.

Single module format

Let’s look again at a complete example and then examine some specific parts in more detail:


import numpy as np

from bokeh.models import Button
from bokeh.palettes import RdYlBu3
from bokeh.plotting import figure, curdoc, vplot

# create a plot and style its properties
p = figure(x_range=(0, 100), y_range=(0, 100), toolbar_location=None)
p.border_fill_color = 'black'
p.background_fill_color = 'black'
p.outline_line_color = None
p.grid.grid_line_color = None

# add a text renderer to out plot (no data yet)
r = p.text(x=[], y=[], text=[], text_color=[], text_font_size="20pt",
           text_baseline="middle", text_align="center")

i = 0

ds = r.data_source

# create a callback that will add a number in a random location
def callback():
    global i['x'].append(np.random.random()*70 + 15)['y'].append(np.random.random()*70 + 15)['text_color'].append(RdYlBu3[i%3])['text'].append(str(i))
    i = i + 1

# add a button widget and configure with the call back
button = Button(label="Press Me")

# put the button and plot in a layout and add to the document
curdoc().add_root(vplot(button, p))

Notice that we have not specified an output or connection method anywhere in this code. It is a simple script that creates and updates objects. The flexibility of the bokeh command line tool means that we can defer output options until the end. We could, e.g., run bokeh json to get a JSON serialized version of the the application. But in this case, we would like to run the app on a Bokeh server, so we execute:

bokeh serve --show

The --show option will cause a browser to open up a new tab automatically to the address of the running application, which in this case is:


In addition to creating Bokeh applications from single python files, it is also possible to create applications from directories.

Directory format

Bokeh applications may also be created by creating and populating a filesystem directory with the appropriate files. To start a directory application in a directory myapp, execute bokeh serve with the name of the directory, for instance:

bokeh serve --show myapp

At a minimum, the directory must contain a that constructs a Document for the Bokeh Server to serve:


The full set of files that Bokeh server knows about is:


See below for information about Application Theming and Lifecycle Hooks for details on the other files.

When executing your Bokeh server ensures that the standard __file__ module attribute works as you would expect. So it is possible to include data files or custom user defined models in your directory however you like. An example might be:

   |      +---things.csv
   |      +---custom.js

In this case you might have code similar to:

from os.path import dirname, join
import pandas

pandas.read_csv(join(dirname(__file__), 'data', 'things.csv')

And similar code to load the JavaScript implementation for a custom model from custom.js


Currently only absolute imports are supported in We hope to lift this limitation in future releases.

Callbacks and Events

Before jumping in to callbacks and events specifically in the context of the Bokeh Server, it’s worth discussing different use-cases for callbacks in general.

JavaScript Callbacks in the Browser

Regardless of whether there is a Bokeh Server involved, it is possible to create callbacks that execute in the browser, using CustomJS and other methods. See JavaScript Callbacks for more detailed information and examples.

It is critical to note that no python code is ever executed when a CustomJS callback is used. This is true even when the call back is supplied as python code to be translated to JavaScript. A CustomJS callback is only executed inside a browser JavaScript interpreter, and can only directly interact JavaScript data and functions (e.g., BokehJS Backbone models).

Python Callbacks with Jupyter Interactors

If you are working in the Jupyter Notebook, it is possible to use Jupyter interactors to quickly create simple GUI forms automatically. Updates to the widgets in the GUI can trigger python callback functions that execute in the Jupyter Python kernel. It is often useful to have these callbacks call push_notebook() to push updates to displayed plots. For more detailed information, see Jupyter Interactors.


It is currently possible to push udpates from python, to BokehJS (i.e., to update plots, etc.) using push_notebook(). It is not currently possible to get events or updates from the other direction (e.g. to have a range or selection update trigger a python callback) without using a Bokeh Server as described in the next section. Adding the capability for two-way Python<–>JS synchronization through Jupyter comms is a planned future addition.

Python Callbacks in Bokeh Applications

Python Callbacks with bokeh.client

Application Theming

Lifecycle Hooks

Sometimes it is desirable to have code execute at specific times in a server or session lifetime. For instance, if you are using a Bokeh Server along side a Django server, you would need to call django.setup() once, as each Bokeh server started, to initialize the Django properly for use by Bokeh application code.

Bokeh provides this capability through a set of Lifecycle Hooks. To use these hooks, you must create your application in Directory format, and include a designated file called in the directory. In this file you can include any or all of the following conventionally named functions:

def on_server_loaded(server_context):
    ''' If present, this function is called when the server first starts. '''

def on_server_unloaded(server_context):
    ''' If present, this function is called when the server shuts down. '''

def on_session_created(session_context):
    ''' If present, this function is called when a session is created. '''

def on_session_destroyed(session_context):
    ''' If present, this function is called when a session is closed. '''

Examples and Video Tutorials

Integration with Web App Frameworks

Deployment Scenarios

With an application like the one above, we can do different things. We can run it just as above locally any time we want to interact with it. Or we can share it with other people, and they can run it locally themselves in the same manner. But we might also want to deploy the application in a way that other people can access it. This section describes some of the considerations that arise in that case.

Standalone Bokeh Server

First, it is possible to simply run the Bokeh server on a network for users to interact with directly. Depending on the computational burden of your application code, the number of users, the power of the machine used to run on, etc., this could be a simple and immediate option for deployment an internal network.

However, it is often the case that there are needs around authentication, scaling, and uptime. In these cases more sophisticated deployment configurations are needed. In the following sections we discuss some of these considerations.


We intend to expand this section with more guidance for other tools and configurations. If have experience with other web deployment scenarios and wish to contribute your knowledge here, please contact us on the mailing list.

Reverse Proxying with Nginx

If the goal is to serve an web application to the general Internet, it is often desirable to host the application on an internal network, and proxy connections to it through some dedicated HTTP server. One very common HTTP and reverse-proxying server is Nginx.

server {
    listen 80 default_server;
    server_name _;

    access_log  /tmp/bokeh.access.log;
    error_log   /tmp/bokeh.error.log debug;

    location / {
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection "upgrade";
        proxy_http_version 1.1;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header Host $host:$server_port;
        proxy_buffering off;


The above server block sets up Nginx to to proxy incoming connections to on port 80 to internally. To work in this configuration, we will need to use some of the command line options to configure the Bokeh Server. In particular we need to use --port to specify that the Bokeh Server should listen itself on port 5100. We also need to set the --host option to whitelist as an acceptable Host on the incoming request header:

serve --port 5100 --host


The --host option is to guard against spoofed Host values. In a more realistic scenario where you have Nginx and the Bokeh server server running on, you would set --host Then any attempted connections that do not report this Host in the request header (as all connections from Nginx do) will be rejected.

Note that in the basic server block above we have not configured any special handling for static resources, e.g., the Bokeh JS and CSS files. This means that these files are served directly by the Bokeh server itself. While this works, it places an unnecessary additional load on the Bokeh server, since Nginx has a fast static asset handler. To utilize Nginx to server Bokeh’s static assets, you can add a new stanza inside the server block above, similar to this:

location /static {
    alias /path/to/bokeh/server/static;

Be careful that the file permissions of the Bokeh resources are accessible to whatever user Nginx is running as. Alternatively, you can copy the resources to a global static directory during your deployment process. See A Full Example with Automation for a demonstration of this.

Load Balancing with Nginx

The architecture of the Bokeh server is specifically designed to be scalable—by and large, if you need more capacity, you simply run additional servers. Often in this situation it is desired to run all the Bokeh server instances behind a load balancer, so that new connections are distributed amongst the individual servers.

Nginx offers a load balancing capability. We will describe some of the basics of one possible configuration, but please also refer to the Nginx load balancer documentation. For instance, there are various different strategies available for choosing what server to connect to next.

First we need to add an upstream stanze to our NGinx configuration, typically above the server stanza. This section looks something like:

upstream myapp {
    least_conn;                 # Use Least Connections strategy
    server;      # Bokeh Server 0
    server;      # Bokeh Server 1
    server;      # Bokeh Server 2
    server;      # Bokeh Server 3
    server;      # Bokeh Server 4
    server;      # Bokeh Server 5

We have labeled this upstream stanza as myapp. We will use this name below. Additionally, we have listed the internal connection information for six different Bokeh server instances (each running on a different port) inside the stanza. You can run and list as many Bokeh servers as you need.

You would run the Bokeh servers with commands similar to:

serve --port 5100 --host
serve --port 5101 --host

Next, in the location stanza for our Bokeh server, change the proxy_pass value to refer to the upstream stanza we created above. In this case we use proxy_pass http://myapp; as shown here:

server {

    location / {
        proxy_pass http://myapp;

        # all other settings unchanged
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection "upgrade";
        proxy_http_version 1.1;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header Host $host:$server_port;
        proxy_buffering off;


Process Control with Supervisord

It is often desired to use process control and monitoring tools when deploying web applications. One popular such tool is Supervisor, which can automatically start and stop process, as well as re-start processes if they terminate unexpectedly. Supervisor is configured using INI style config files. A sample file that might be used to start a single Bokeh Server app is below:

; supervisor config file

file=/tmp/supervisor.sock   ; (the path to the socket file)
chmod=0700                  ; sockef file mode (default 0700)

logfile=/var/log/supervisord.log ; (main log file; default $CWD/supervisord.log)
pidfile=/var/run/ ; (supervisord pidfile; default $CWD/
childlogdir=/var/log/supervisor  ; ('AUTO' child log dir, default $TEMP)

; The section below must be in the present for the RPC (supervisorctl/web)
; interface in to function.
supervisor.rpcinterface_factory = supervisor.rpcinterface:make_main_rpcinterface

serverurl=unix:///tmp/supervisor.sock ; use a unix:// URL for a unix socket

command=/path/to/bokeh serve --host

The standard location for the supervisor configj file varies from system to system. Consult the Supervisor configuration documentation for more details. It is also possible to specify a config file explicity. To do this, execute:

supervisord -c /path/to/supervisord.conf

to start the Supervisor process. Then to control processes execute supervisorctl commands. For instance to start all processes, run:

supervisctl -c /path/to/supervisord.conf start all

To stop all processes run:

supervisctl -c /path/to/supervisord.conf start all

And to update the process control after editing the config file, run:

supervisctl -c /path/to/supervisord.conf update

A Full Example with Automation

To deploy the demo site at we combine all of the above techniques. Additionally, we used SaltStack to automate many aspects of the deployment.


Other devops automation tools include Puppet, Ansible, and Chef. We would like to provide specific guidance where ever we can, so if you have experience with these tools and would be interested in contributing your knowledge, please contact us on the mailing list.

You can see all the code for deploying the site at the public GitHub repository here:

You can modify or deploy your own version of this site on an Amazon Linux instance by simply running the script at the top level. With minor modifications, this machinery should work on many linux variants.