> For the complete documentation index, see [llms.txt](https://docs.bdb.ai/data-science-lab-5/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bdb.ai/data-science-lab-5/tabs-for-a-dsl-project/model/import-model.md).
# Import Model
External models can be imported into the Data Science Lab and experimented inside the Notebooks using the Import Model functionality.
{% hint style="info" %}
*Please Note:*
* *The External models can be registered to the **Data Pipeline** module and inferred using the Data Science Lab script runner.*
* Only the **Native prediction functionality** will work for the External models.
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### Importing a Model
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*Check out the illustration on importing a model.*
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{% embed url="" %}
***Importing a Model***
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* Navigate to the ***Model*** tab for a Data Science Project.
* Click the ***Import Model*** option.
* The user gets redirected to upload the model file. Select and upload the file.
* A notification message appears.
* The imported model gets added to the model list.
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*Please Note: The Imported models are referred to as **External models** in the model list and are marked with a pre-fix to their names (as displayed in the above-given image)**.***
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### Exporting the Model to the Data Pipeline
The user needs to start a new .ipynb file with a wrapper function that includes Data, Imported Model, Predict function, and output Dataset with predictions.
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*Check out the walk-through on Export to Pipeline Functionality for a model.*
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* Navigate to a Data Science Notebook (.ipynb file) from an activated project. In this case, a pipeline has been imported with the wrapper function.
* Access the Imported Model inside this .ipynb file.
* Load the imported model to the Notebook cell.
* Mention the Loaded imported model in the inference script.
* Run the code cell with the inference script.
* The Data preview is displayed below.
* Click the Register option for the imported model from the ellipsis context menu.
* The ***Register Model*** dialog box appears to confirm the model registration.
* Click the ***Yes*** option.
* A notification message appears, and the model gets registered.
* Export the script using the ***Export*** functionality provided for the Data Science Notebook (.ipynb file).
* Another notification appears to ensure that the Notebook is saved.
* The ***Export to Pipeline*** window appears.
* Select a specific script from the Notebook. or Choose the ***Select All*** option to select the full script.
* Select the ***Next*** option.
* Click the ***Validate*** icon to validate the script.
* A notification message appears to ensure the validity of the script.
* Click the ***Export to Pipeline*** option.
* A notification message appears to ensure that the selected Notebook has been exported.
{% hint style="info" %}
*Please Note: The imported model gets registered to the Data Pipeline module as a script.*
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### Accessing the Exported Model within the Pipeline User interface
* Navigate to the ***Data Pipeline Workflow editor***.
* Drag the ***DS Lab Runner*** component and configure the Basic Information.
* Open the Meta Information tab of the DS Lab Runner component.
* Configure the following information for the ***Meta Information*** tab.
* Select ***Script Runner*** as the ***Execution Type.***
* Select function input type.
* Select the project name.
* Select the ***Script Name*** from the drop-down option. The same name given to the imported model appears as the script name.
* Provide details for the External Library (if applicable).
* Select the Start Function from the drop-down menu.
* The exported model can be accessed inside the ***Script** section*.
* The user can connect the DS Lab Script Runner component to an Input Event.
* Run the Pipeline.
* The model predictions can be generated in the **Preview tab** of the connected Input Event.
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*Please Note:*
* *The **Imported Models** can be accessed through the **Script Runner** component inside the Data Pipeline module.*
* *The execution type should be Model Runner inside the Data Pipeline while accessing the other exported Data Science models.*
* *The supported extensions for External models - .pkl, .h5, .pth & .pt*
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Try out the Import Model Functionality yourself
Some of the Sample models and related scripts are provided below for the users to try this functionality. Please download them with a click, and use them inside your Data Science Notebook by following the above-mentioned steps.
### Sample files for Sklearn
{% file src="/files/0zWLNiIM0knm1qNeBb3o" %}
Sample Sklearn model for import.
{% endfile %}
{% file src="/files/7Ebha1DYu6jigqia6mJL" %}
Sample python script based on the imported Sklearn model.
{% endfile %}
### Sample files for Keras
{% file src="/files/wNVs6i2rgzgOk3JMDGXc" %}
Sample Keras model for import.
{% endfile %}
{% file src="/files/y5OpsUy8n362KAsROtFv" %}
Sample python script based on the imported Keras model.
{% endfile %}
### Sample files for PyTorch
{% file src="/files/7j7r5cBD4mTp3C8rQEuj" %}
Sample Pytorch model for import
{% endfile %}
{% file src="/files/NcEagGQoee6yw7QAcJrm" %}
Sample python script based on the imported Pytorch model.
{% endfile %}
