---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 865713f84f23e255bd04305200e61bf8f59d3f4d9bf7946e7f7d16a627ea931a
  pageDirectory: concepts
  sources:
    - automl-python-api-reference-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - importnotebook
  citations:
    - file: automl-python-api-reference-databricks-on-aws.md
title: ImportNotebook
description: The databricks.automl.import_notebook method imports a notebook saved as an MLflow artifact.
tags:
  - auto-ml
  - notebook
  - mlflow
timestamp: "2026-06-19T17:39:38.838Z"
---

# ImportNotebook

**ImportNotebook** is a method in the Databricks AutoML Python API that imports a notebook previously saved as an MLflow artifact. It is exposed through the `databricks.automl.import_notebook` function, which returns an [ImportNotebookResult](/concepts/importnotebookresult.md) object containing details about the imported notebook. ^[automl-python-api-reference-databricks-on-aws.md]

## API Signature

The method accepts three parameters: the URI of the MLflow artifact, the destination path for the imported notebook, and an optional overwrite flag. ^[automl-python-api-reference-databricks-on-aws.md]

```python
databricks.automl.import_notebook(
    artifact_uri: str,
    path: str,
    overwrite: bool = False
) -> ImportNotebookResult
```

### Parameters

- **artifact_uri** (`str`): The URI of the artifact saved with an [MLflow Run](/concepts/mlflow-run.md). Typically obtained from a trial's `artifact_uri` attribute in an [AutoMLSummary](/concepts/automlsummary.md). ^[automl-python-api-reference-databricks-on-aws.md]
- **path** (`str`): The destination path for the imported notebook in the Databricks workspace, including the full directory path. ^[automl-python-api-reference-databricks-on-aws.md]
- **overwrite** (`bool`, optional): Whether to overwrite an existing notebook at the destination path. Defaults to `False`. ^[automl-python-api-reference-databricks-on-aws.md]

## Usage Example

ImportNotebook is commonly used in conjunction with an AutoML run. After calling `classify`, `regress`, or `forecast`, you can import a notebook generated by a specific trial to inspect or reuse it. ^[automl-python-api-reference-databricks-on-aws.md]

```python
summary = databricks.automl.classify(...)
result = databricks.automl.import_notebook(
    summary.trials[5].artifact_uri,
    "/Users/you@yourcompany.com/path/to/directory"
)
print(result.path)
print(result.url)
```

After a successful import, the `result` object contains the path and URL of the imported notebook, which can be opened and executed in the Databricks workspace. ^[automl-python-api-reference-databricks-on-aws.md]

## Return Type

The method returns an [ImportNotebookResult](/concepts/importnotebookresult.md) object with the following properties:

- **path**: The workspace path where the notebook was imported.
- **url**: The URL to open the imported notebook in the Databricks workspace.

## Related Concepts

- AutoML — The automated machine learning framework on Databricks
- [AutoMLSummary](/concepts/automlsummary.md) — Summary object returned by AutoML run methods
- [ImportNotebookResult](/concepts/importnotebookresult.md) — The result object returned by `import_notebook`
- [TrialInfo](/concepts/trialinfo.md) — Summary object for individual AutoML trials
- MLflow Artifacts — The storage mechanism for AutoML-generated notebooks

## Sources

- automl-python-api-reference-databricks-on-aws.md

# Citations

1. [automl-python-api-reference-databricks-on-aws.md](/references/automl-python-api-reference-databricks-on-aws-bc754c3a.md)
