# Manage custom model dependencies

> Manage custom model dependencies - Describes how to manage these dependencies from the Workshop and
> update the base drop-in environments to support your model code.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-04-24T16:03:56.556994+00:00` (UTC).

## Primary page

- [Manage custom model dependencies](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-dependencies.html): Full documentation for this topic (HTML).

## Related documentation

- [Classic UI documentation](https://docs.datarobot.com/en/docs/classic-ui/index.html): Linked from this page.
- [MLOps](https://docs.datarobot.com/en/docs/classic-ui/mlops/index.html): Linked from this page.
- [Deployment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/index.html): Linked from this page.
- [Prepare custom models for deployment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/index.html): Linked from this page.
- [Custom Model Workshop](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/index.html): Linked from this page.
- [drop-in environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/drop-in-environments.html): Linked from this page.
- [DRUM](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-drum.html): Linked from this page.

## Documentation content

# Manage custom model dependencies

Custom models can contain various machine learning libraries in the model code, but not every [drop-in environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/drop-in-environments.html) provided by DataRobot natively supports all libraries. However, you can manage these dependencies from the Workshop and update the base drop-in environments to support your model code. To manage model dependencies, you must include a `requirements.txt` file uploaded as part of your custom model. The text file must indicate the machine learning libraries used in the model code.

For example, consider a custom R model that uses Caret and XGBoost libraries. If this model is added to the Workshop and the R drop-in environment is selected, the base environment will only support Caret, not XGBoost. To address this, edit `requirements.txt` to include the Caret and XGBoost dependencies. After editing and re-uploading the requirements file, the base environment includes XGBoost, making the model available within the environment.

> [!NOTE] Important
> Custom model dependencies aren't applied when testing a model locally with [DRUM](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-drum.html).

List the following, depending on the model language, in `requirements.txt`:

- For R models, list the machine learning library dependencies.
- For Python models, list the dependenciesandany version constraints for the libraries. Supported constraint types include<,<=,==,>=,>, and multiple constraints can be issued in a single entry (for example,pandas >= 0.24, < 1.0).

Once the requirements file is updated to include dependencies and constraints, navigate to your custom model's Assemble tab. Upload the file under the Model > Content header. The Model Dependencies field updates to display the dependencies and constraints listed in the file.

From the Assemble tab, select a base drop-in environment under the Model Environment header. DataRobot warns you that a new environment must be built to account for the model dependencies. Select Build environment, and DataRobot installs the required libraries and constraints to the base environment.

Once the base environment is updated, your custom model will be usable with the environment, allowing you to test, deploy, or register it.