Outerbounds (Metaflow Distribution)

0.12.28 · active · verified Thu Apr 16

Outerbounds is an opinionated distribution of Metaflow, designed to streamline machine learning workflows by providing pre-configured cloud infrastructure and a curated set of dependencies. It aims to reduce administrative overhead, allowing data scientists to focus more on model development. The current version is 0.12.28 and it follows a continuous release cadence, often aligning with Metaflow updates.

Common errors

```
ModuleNotFoundError: No module named 'metaflow'
```
cause The `outerbounds` package, despite being a distribution, explicitly depends on `metaflow`. This error indicates that `metaflow` itself was not installed or is not accessible in the current Python environment.

fix Ensure `outerbounds` (and thus `metaflow`) is installed correctly: `pip install outerbounds`. If using virtual environments, activate the correct environment.
```
RuntimeError: Failed to connect to backend: Failed to connect to S3 (AWS) / Failed to connect to Kubernetes (K8s)
```
cause This typically means your cloud environment (AWS, Kubernetes) is not properly configured, or your credentials are missing/invalid for Metaflow to store/retrieve data or orchestrate tasks.

fix Verify your cloud credentials and configuration. For AWS, run `aws configure` or ensure `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_DEFAULT_REGION` are set. For Kubernetes, ensure your `kubectl` context is correct and `metaflow configure kubernetes` has been run.
```
Flow 'MyFlow' did not find any steps. Did you decorate your methods with '@step'?
```
cause A Metaflow flow requires at least one method decorated with `@step`. This error occurs if no methods in your `FlowSpec` subclass are marked as steps.

fix Ensure that all methods intended as steps in your Metaflow flow are decorated with `@step` (e.g., `@step def start(self):`).
```
AttributeError: 'SomeFlow' object has no attribute 'some_data'
```
cause Attempting to access a data attribute (e.g., `self.some_data`) in a Metaflow flow before it has been set in a preceding step, or if there's a typo.

fix Metaflow data attributes are passed between steps. Ensure `self.some_data = value` is set in an earlier step before it's accessed in a later one. Verify variable names for typos.

Warnings

gotcha Outerbounds is primarily a distribution and configuration layer for Metaflow. It does not introduce new Python APIs compared to `metaflow`. All core functionalities and imports come directly from the `metaflow` package.
Fix: Always refer to `metaflow` documentation for API usage. Think of `outerbounds` as the 'batteries included' version of `metaflow`.
gotcha The primary value of `outerbounds` comes from its pre-configured cloud integration (AWS, Kubernetes). While you can run flows locally, leveraging its full capabilities requires proper cloud setup and authentication.
Fix: Ensure your environment is correctly configured for your chosen cloud provider (e.g., AWS credentials, Kubernetes context) before attempting to run flows on the cloud. Consult the Outerbounds/Metaflow documentation for specific `metaflow configure` commands.
gotcha When running flows in a distributed cloud environment, ensure all necessary Python dependencies are specified. `outerbounds` bundles common ML dependencies, but custom or less common packages may need to be explicitly listed using `@conda`, `@pypi`, or `@pip` decorators in your Metaflow code.
Fix: For each step, identify external dependencies and add appropriate decorators (e.g., `@conda(libraries={'scikit-learn': '1.0'})`) to ensure they are available in the execution environment. Missing dependencies are a common source of runtime errors in cloud steps.
breaking While `outerbounds` aims for stability, underlying `metaflow` versions may introduce breaking changes. Upgrading `outerbounds` implicitly upgrades `metaflow` and its dependencies, which can occasionally lead to unexpected behavior if your code relies on deprecated `metaflow` features.
Fix: Before major upgrades of `outerbounds` (or when `metaflow` itself gets a major upgrade), review the `metaflow` release notes for breaking changes. Test your critical workflows in a staging environment.

Install

pip install outerbounds Install Outerbounds

Imports

FlowSpec
```
from metaflow import FlowSpec
```
step
```
from metaflow import step
```
current
```
from metaflow import current
```
project
wrong:
```
from outerbounds import project
```
correct:
```
from metaflow import project
```
Outerbounds itself does not introduce new APIs; all core Metaflow symbols are imported directly from 'metaflow'.

Quickstart

This quickstart demonstrates a basic Metaflow workflow. While `outerbounds` provides underlying configuration for cloud execution, the code itself remains standard Metaflow. To run this, save it as a Python file (e.g., `my_flow.py`) and execute `python my_flow.py run` from your terminal. For cloud execution, you'll need to configure your Outerbounds environment (e.g., `metaflow configure aws`).

from metaflow import FlowSpec, step

class MyFirstFlow(FlowSpec):
    @step
    def start(self):
        self.message = 'Hello, Outerbounds!'
        print(self.message)
        self.next(self.end)

    @step
    def end(self):
        print(f"Flow finished with message: {self.message}")

if __name__ == '__main__':
    MyFirstFlow()

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