Connect to Anaconda Platform and run your first flow

There are two main ways to develop and execute code on Anaconda Platform:

You can use cloud workstations through VS Code, Cursor, Windsurf, or SSH, backed by cloud instances hosted as part of the platform in your cloud account.
You can access the platform from any existing development environment, such as a laptop or a cloud-based environment.

Invite users to the platform

Admin step Before anyone can authenticate, invite them to the platform by adding their email in the user management view. After a user has been invited, they can log in to the platform with the configured SSO provider.

Tokens are personal.Users should not share their personal access tokens. If you need shareable access tokens that are not tied to a person, see Programmatic access via machine users.

Connect to the platform

You can connect to the platform from your existing development environment or with a cloud workstation. If you have an existing installation of open-source Metaflow in your environment and you want to use Anaconda Platform in the same environment, use the third tab to avoid conflicts.

Use my existing environment
Set up a workstation
I have an existing Metaflow setup

Click your profile in the lower-left corner to open your Local Setup page:

The Local Setup page showing the Configure Outerbounds section with a ready-to-copy configure command

Open a terminal and install the outerbounds package:

conda
uv
pip

conda install outerbounds

uv pip install --upgrade outerbounds

pip install -U outerbounds

You can install the package in an isolated environment, but it is not required. Next, copy the configuration command shown on your Local Setup page and run it in your terminal:

outerbounds configure <CONFIGURATION_STRING>

The command saves your access token in a system-wide configuration file.You are now ready to start using the platform.

Troubleshooting the connection.If you ever run into problems with the connection, execute outerbounds check -v to see details about your setup. You should see a row of OKs if everything works correctly. Otherwise, contact your administrator.

You can develop with cloud workstations by following these steps:

First, an administrator sets up a cloud workstation for each user.
After this, users can connect to their personal cloud workstation through Visual Studio Code.

Troubleshooting workstations.If you run into problems with the connection, execute outerbounds check -w to see details about your setup. You should see a row of OKs if everything works correctly. If you have any questions about workstations, contact your administrator.

Click your profile in the lower-left corner to open your Local Setup page:

Create a new environment for the outerbounds package to avoid conflicts between metaflow and outerbounds:

conda
venv

conda create --name ob python=3.11 outerbounds
conda activate ob

python -m venv ob
source ob/bin/activate
pip install outerbounds

When configuring the access token, use the --profile option to create a new configuration profile instead of overwriting your existing Metaflow config:
outerbounds configure --profile ob <CONFIGURATION_STRING>
To use the platform, set an environment variable:
export METAFLOW_PROFILE=ob

You are now ready to start using the platform.

Troubleshooting the connection.If you ever run into problems with the connection, execute METAFLOW_PROFILE=ob outerbounds check -v to see details about your setup. You should see a row of OKs if everything works correctly. Otherwise, contact your administrator.

Run your first flow

After you have configured access to the platform, run a simple Metaflow flow to confirm that everything works.

If you are new to Metaflow, take a quick look at the Metaflow documentation. All features of Metaflow work on Anaconda Platform.

Save the following code snippet in a file called hello.py:

from metaflow import FlowSpec, step

class HelloFlow(FlowSpec):

    @step
    def start(self):
        print("Hello world! 👋")
        self.next(self.end)

    @step
    def end(self):
        pass

if __name__ == "__main__":
    HelloFlow()

Then, execute it:

python hello.py run

If you see Hello World! in the output, the flow ran successfully. In the output, you should also see a link to the Runs view, which shows all executions, both prototyping and production, taking place on the platform. You can click the latest HelloFlow run to explore information about the run that you just executed.

Hello artifacts

Extend the above example with the following lines, storing data in self. variables that are persisted automatically as artifacts, a core concept of Metaflow:

from metaflow import FlowSpec, step, card

class HelloFlow(FlowSpec):

    @card
    @step
    def start(self):
        self.greeting = "Hello world!"
        self.x = 1
        self.next(self.end)

    @card
    @step
    def end(self):
        print(self.greeting)
        self.x += 10
        print("x is", self.x)


if __name__ == "__main__":
    HelloFlow()

Note how we refer to the variables self.x and self.greeting both in the start and end steps. By design, this does not seem that special, but notably the steps could execute on separate cloud instances, as you will see when executing the code in the cloud. The data is moved automatically between steps and stored for later inspection. Run the flow as before:

python hello.py run

Check the Runs view again for the latest run. Navigate to the start task and observe a new card section in the task view, as shown in this clip:

The card section is produced by the @card decorator, which allows you to attach custom visualizations in the UI. Note how the card shows how the value of x changes between the start and end steps. Metaflow versions data automatically, and you can use this feature to track metrics, models, dataframes, or any other data across prototypes and production runs. If you are feeling adventurous, explore the options for visualizing artifacts through cards. Feel free to hack HelloFlow. You cannot break anything.

Anaconda Platform

Getting Started

Platform Guides

Connect to Anaconda Platform and run your first flow

Invite users to the platform

Connect to the platform

Run your first flow

Hello artifacts

​Invite users to the platform

​Connect to the platform

​Run your first flow

​Hello artifacts

Invite users to the platform

Connect to the platform

Run your first flow

Hello artifacts