🚀 Quickstart


Installing and running dstack is very easy:

pip install dstack==0.6.2
dstack server start

If you run it for the first time, it may take a while. Once it's done, you'll see the following output:

$ dstack server start
To access the application, open this URL in the browser: http://localhost:8080/auth/verify?user=dstack&code=xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx&next=/
The default profile in "~/.dstack/config.yaml" is already configured. You are welcome to push your data using Python or R packages.
What's next?
- Checkout our documentation: https://docs.dstack.ai
- Ask questions and share feedback: https://discord.gg/8xfhEYa
- Star us on GitHub: https://github.com/dstackai/dstack

To access dstack, click the URL provided in the output. If you try to access dstack without using this URL, it will require you to sign up using a username and a password.

If you open the URL, you'll see the following interface:

You're logged as the dstack user. The current page is Applications. It shows you all published applications which you have access to. The sidebar on the left lets you open other pages: ML Models, Settings, Documentation, and Chat.

Now let's build a simple application to see how dstack works in action.

Minimal Application

Here's an elementary example of using dstack. The application takes real-time stock exchange data from Yahoo Finance for the FAANG companies and renders it for a selected symbol.

Here's the Python code that you have to run to make such an application:

import dstack.controls as ctrl
import dstack as ds
import plotly.express as px
def get_data():
return px.data.stocks()
def output_handler(self, ticker):
self.data = px.line(get_data(), x='date', y=ticker.value())
app = ds.app(controls=[(ctrl.ComboBox(items=get_data().columns[1:].tolist()))],
result = ds.push("stocks", app)

If you run it and click the provided URL, you'll see the application:

The user is prompted to choose one of the companies to view its latest market data in form of a candlestick chart.

Let's take a closer look at this code and describe every step.

Application output

First, we define the function output_handler that takes the arguments self of the type ctrl.Output and symbols of the type ctrl.ComboBox. The first argument represents the output the function is supposed to update. The second argument represents a combo box control in which the user selects a stock symbol (e.g. "FB", "AMZN", etc). Based on the selected symbol (see symbols.value()), the function fetches the market data for the corresponding stock (from the Yahoo Financial Services – using the pandas_datareader package), makes a Candlestick chart (using the plotly package), and updates the attribute data of the output with the resulting figure.

def output_handler(self: ctrl.Output, symbols: ctrl.ComboBox):
start = datetime.today() - timedelta(days=30)
end = datetime.today()
df = web.DataReader(symbols.value(), 'yahoo', start, end)
fig = go.Figure(
data=[go.Candlestick(x=df.index, open=df['Open'], high=df['High'], low=df['Low'], close=df['Close'])])
self.data = fig

User controls and application

Once this function is defined, we call the function dstack.app() where we pass lists of controls and outputs. The attributecontrols include an instance of dstack.controls.ComboBox wehere we pass a list of tickers. The attribute outputs includes an instance of dstack.controls.Output where we pass our handler output_handler.

app = ds.app(controls=[ctrl.ComboBox(items=["FB", "AMZN", "AAPL", "NFLX", "GOOG"])],

Deploy application

Finally, we deploy our application to the dstack server by using the function dstack.push(). The arguments of the call are "minimal_app" – the name of the application, and app – the instance of our application. If successful, this call returns a push result that has an attribute url. This is the URL of the deployed application.

result = ds.push("minimal_app", app)

If we click the URL, we'll see the application.

To learn in more detail about what applications consist of and how to use all their features, check out the Concepts page.

To see other examples, please check out the Tutorials page.


Do you have any feedback either minor or critical? Please, file an issue in our GitHub repo or write to us on our Discord Channel.

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