---
title: "Run custom Python scripts with the Custom action"
description: "This article is about actions that use the Data Platform's Python data processing engine. To use Apache Spark clusters, see Custom PySpark action"
url: https://docs.ovhcloud.com/de/guides/public-cloud/data-platform/dpe-actions-custom
lang: de
lastUpdated: 2026-09-14
---
> For AI agents: the complete documentation index is available at https://docs.ovhcloud.com/de/llms.txt, the full documentation bundle is available at https://docs.ovhcloud.com/de/llms-full.txt.

# Run custom Python scripts with the Custom action

## Objective

:::info
This article is about actions that use the Data Platform's Python data processing engine. To use Apache Spark clusters, see [Custom PySpark action](https://docs.ovhcloud.com/de/guides/public-cloud/data-platform/dpe-actions-custom-pyspark.md).
:::

A _Custom action_ allows you to execute custom Python scripts in a scalable cloud cluster environment.

Using our [Software Development Kit (SDK)](https://docs.ovhcloud.com/de/guides/public-cloud/data-platform/landing-page-developers-python-sdk.md) to easily interact with the different components of the platform, _Custom actions_ can be used to implement a variety of use-cases such as:

- Execute a manipulation algorithm or ETL job on your data warehouse
- Execute a simple data analysis or machine learning algorithm
- Extract data from data sources not available on the Data Platform marketplace without having to create connectors for it
- Extract real time data (like MQTT, Kafka, etc..)

:::info
Custom actions benefit from the whole Data Processing Engine's feature-set, typically the power of the [segmentation to parallelize the execution](https://docs.ovhcloud.com/de/guides/public-cloud/data-platform/dpe-jobs-segmentation.md) of your algorithm or the orchestration within [workflows](https://docs.ovhcloud.com/de/guides/public-cloud/data-platform/dpe-workflows.md) triggered immediately or on a scheduled basis.
:::

## Configure a Custom action

In the Data Processing Engine of your Project, go in the Actions tab and click on the **New Action** button. Choose the action type _Custom_.

![Creation screen of a custom action](/images/public-cloud/data-platform/product/dpe/actions/custom/picts/custom-action.png)
Drag and drop your _.py_ script onto the "Drag and drop" section.\
Alternatively, select the **Start with a boilerplate** option to get started directly on the Platform's Python interface with example code snippets.

![Creation screen of a custom action](/images/public-cloud/data-platform/product/dpe/actions/custom/picts/action-creation.png)
The name of the function to be executed **must be entered manually** at the top of the screen in the Information panel (here and by default: "customfunc").

You will be able to edit your source file directly in the editing interface or drop a new file if needed. When you are developing your own custom actions you can use any functions provided in the [Software Development Kit (SDK)](https://docs.ovhcloud.com/de/guides/public-cloud/data-platform/landing-page-developers-python-sdk.md). to easily interact with other components of the platform.\
To read more about all the available SDK functions check out the article below:

[Discover all SDK methods](https://docs.ovhcloud.com/de/guides/public-cloud/data-platform/landing-page-developers-python-sdk.md)

:::info
Everything your script writes to standard output or standard error, including plain `print()` calls, appears in the job logs alongside the messages emitted through the `logging` module.
:::

## Use the helper panel

![DPE Custom action helper](/images/public-cloud/data-platform/product/dpe/actions/custom/picts/custom-action-helper.png)
The Custom action editor includes a helper panel, so you can find guidance without leaving your script:

- **Scenarios**: ready-to-use action scripts organized by category, to copy and adapt to your use case.
- **SDK guides**: documentation for the [SDK](https://docs.ovhcloud.com/de/guides/public-cloud/data-platform/landing-page-developers-python-sdk.md) methods you can call from your script.
- **Data**: browse your project's data directly from the editor, to check names and structures while you write your code.
- **FAQ**: answers to common Custom action questions.

:::info
The helper content is the same live catalog that powers the [Data Platform Extension](https://docs.ovhcloud.com/de/guides/public-cloud/data-platform/dpe-notebooks-data-platform-extension.md) in notebooks, so it is always up to date.
:::

## Manage dependencies

### Setting language version

You can choose the Python version of your custom action among the following:

- Python 3.11
- Python 3.9 _(default option)_

:::info
[Workflows](https://docs.ovhcloud.com/de/guides/public-cloud/data-platform/dpe-workflows.md) cannot be executed with multiple versions at once.
:::

:::info
We are regularly updating the available versions to provide you with a best-practice development framework. Your existing work is not migrated to a new version as long as its language version is still supported.
:::

### Installing Python packages

You might need to install specific packages not included by default. You can add them in the "Python Requirements" field respecting the format used in a basic requirements file for "pip" (Python package manager) then press "ENTER" on your keyboard.

This is what it should looks like once you pressed "ENTER":

![Creation screen of a custom action](/images/public-cloud/data-platform/product/dpe/actions/custom/picts/action-requirements.png)
:::info
When working with a Custom Action in an [Always-up](https://docs.ovhcloud.com/de/guides/public-cloud/data-platform/dpe-jobs-preferences.md#execution-modes) execution environment, updating dependencies triggers a redeployment of the environment to put the changes into effect.
:::

### Installing packages from a Git repository

You can install Python packages directly from a GitHub or GitLab repository using the `git+` prefix in your requirements:

```
git+https://github.com/{OWNER}/{REPO}.git
```

To pin a specific version, add a tag or commit hash:

```
git+https://github.com/{OWNER}/{REPO}.git@<tag>
```

#### Auto-install the latest release

To always install the most recent published release without manually tracking version tags, use `@latest`:

```
git+https://github.com/{OWNER}/{REPO}.git@latest
```

The platform detects the `git+` prefix and `@latest` suffix, then automatically resolves and substitutes the latest release tag before installing.

:::info
After adding or modifying a Git dependency, click **Force Build** to reinstall. You no longer need to manually update the tag or commit hash each time a new version of your module is published, but note that the latest release is not picked up automatically at runtime, a manual **Force Build** is always required.
:::

### Default list of dependencies

:::warning
Data Platform blocks the minors of the versions allowing bug fixes to be installed. If you need a more recent version of a library you can override it manually by adding the same package with the new version in the "Requirements" field.
:::

Here is the list of all the packages and their version (as you could find them in a requirements file for pip) shipped with the Data Processing Engine workers:

[Discover all default Python packages](https://docs.ovhcloud.com/de/guides/public-cloud/data-platform/dpe-actions-custom-default-packages.md)

## Examples of sample scripts

### Example of the extraction of a file followed by loading it into the default dataset

```python
import logging
import sys
from forepaas.dwh import connect
from forepaas.dwh import bulk_insert

def customfunc(event):
    logger = logging.getLogger(__name__)
    
    try:
        logger.info("Begin function")
        
        # Connect to the source connector
        connector = connect("dwh/dropbox_test/consommations.csv")
        
        # Upload raw file from the source connector
        connection_str = get_raw(connector)
        
        # Connect to the source
        source = connect(connection_str)
        
        # Connect to the destination connector
        destination = connect("dwh/default_dataset/consommations")
        
        # Extract dataframe from source and bulk insert into the destination
        for df in extract(source):
            stats, error = bulk_insert(destination, "consommations", df)
            logger.info(stats)
            logger.info(error)
        
        del connector, source, destination
        logger.info("END function")
        
    except Exception as e:
        raise Exception("err:{} L:{}".format(e, sys.exc_info()[2].tb_lineno))

```

### Example of a data transfer between two datasets

```python
import logging
import sys
from forepaas.dwh import connect
from forepaas.dwh import bulk_insert

def customfunc(event):
    logger = logging.getLogger(__name__)
    
    try:
        logger.info("Begin function")
        
        # Connection to a source datastore
        connector = connect("dwh/default_dataset/consommations")
        
        # Data extraction from the source by a SELECT
        lines = connector.select("consommations", {"filter_attribute": "2018-01-01"})
        
        del connector
        
        # Treatment of each line of the data
        for line in lines:
            line["new_insight"] = (line["factor1"] + line["factor2"] * 2) / 100
        
        # Connection to the destination datastore
        connector = connect("dwh/analytics_dataset/agr_consommations")
        
        # Bulk insert into the destination
        stats, err = bulk_insert(connector, "agr_consommations", lines)
        logger.info(stats)
        logger.info(err)
        
        del connector
        logger.info("END function")
        
    except Exception as e:
        raise Exception("err:{} L:{}".format(e, sys.exc_info()[2].tb_lineno))

```

## Go further

If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/de/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project.

Ask questions, give your feedback and interact directly with the team building the Data Platform on the dedicated [Discord channel](https://discord.gg/ovhcloud).

If you need support with your OVHcloud services, create a request in our [Help Centre](https://help.ovhcloud.com/csm?id=csm_get_help).

Join our [community of users](https://community.ovhcloud.com/).