> For the complete documentation index, see [llms.txt](https://docs.bdb.ai/data-science-lab/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/project/tabs-for-a-data-science-lab-project/tabs-for-tensorflow-and-pytorch-environment/model/import-model.md).

# Import Model

{% hint style="info" %}
*<mark style="color:green;">Please Note:</mark>*&#x20;

* *The External models can be registered to the Data Pipeline module and they can be inferred using the Data Science Lab script runner.*
* Only the **Native prediction functionality** will work for the External models.
  {% endhint %}

### Importing the Model

* Navigate to the ***Model*** tab.
* Click the ***Import Model*** option.

<figure><img src="/files/kTd53NLnPHkEBughWjUG" alt=""><figcaption><p>Using the Import Model option from the Model tab</p></figcaption></figure>

* The user gets redirected to upload the model file.

&#x20;     ![](/files/zEteQzcLd9yiSDM7pEAL)

* A notification message appears.
* The imported model gets added to the model list.&#x20;

<figure><img src="/files/C9fwdYprfPkmxfULRk2n" alt=""><figcaption></figcaption></figure>

{% hint style="info" %}
*<mark style="color:green;">Please Note:</mark> The Imported models are referred as **External models** in model list and they are marked with* <img src="/files/G1D9xE16CWy777Ev7UJj" alt="" data-size="line"> *as pre-fix to their names (as displayed in the given image)**.***&#x20;

![](/files/6QpPnxlUeF2uZSa6bKDR)
{% endhint %}

### Exporting the Model to Data Pipeline

* The user can start a new Notebook with wrapper function that includes Data, Imported Model, Predict function, and output Dataset with predictions.

<figure><img src="/files/3wd9k8fVfCaGtOh8n2b8" alt=""><figcaption></figcaption></figure>

* Register the Imported Model from the model tab given on the Notebook page.

&#x20;     ![](/files/oKzjgHEEbrEMZfQl0D7Z)

* The Register Model confirmation dialog box appears.
* Click the ***Yes*** option.

&#x20;     ![](/files/DnjkrzEmHWiv4pvNUKOq)

* A notification message appears, and the model gets registered.   &#x20;

<figure><img src="/files/UhkOXgJsN1hGtunPwuaT" alt=""><figcaption></figcaption></figure>

* Export the script using the ***Export*** functionality provided for the Notebooks on the ***Notebook List*** page.   &#x20;

<figure><img src="/files/2vx5NOPJgJoo413qdRYf" alt=""><figcaption></figcaption></figure>

* The ***Export to Pipeline*** window appears.
* Select a specific script from the Notebook.
* Select the ***Next*** option. &#x20;

<figure><img src="/files/MjlBfTDYGIvAT6caGLoM" alt=""><figcaption></figcaption></figure>

* Click the ***Export*** option from the screen that appears.      &#x20;

<figure><img src="/files/vt4RPPyFkk5J7nyGEyQL" alt=""><figcaption></figcaption></figure>

* A notification message appears.

<figure><img src="/files/H0uzRuosd8GZvE9qVapY" alt=""><figcaption></figcaption></figure>

{% hint style="info" %}
*<mark style="color:green;">Please Note</mark>: The imported model gets registered to the Data Pipeline module.*
{% endhint %}

### Accessing the Exported Model within the Pipeline User interface

* Navigate to the ***Data Pipeline Workflow editor***.
* Drag the ***DS Lab Script Runner*** component and configure it.
* Select the ***script name*** from the drop-down option.
* The exported model along with the script can be accessed inside the ***Script Runner*** component.
* The user can connect the DS Lab Script Runner component to an Input Event.
* Run the Pipeline.    &#x20;

<figure><img src="/files/bxEQNAGyTEEyRhwD6EU8" alt=""><figcaption></figcaption></figure>

* The model predictions can be generated in the **Preview tab** of the connected Input Event.

<figure><img src="/files/ZjV4XWM6BtefLGcTQAQH" alt=""><figcaption></figcaption></figure>

{% hint style="info" %}
*<mark style="color:green;">Please Note:</mark> Only the **Exported Models** are accessed through the **DS Lab Script Runner** component, the other models can be accessed through the **Model Runner** component inside the Data Pipeline.*
{% endhint %}

<details>

<summary>Try out the Import Model Functionality yourself</summary>

Some of the Sample models and related scripts are provided below for the user to try his hands on this functionality. Please download them by a click, and use them in your Notebook by following the above mentioned steps.

</details>

### Sample files for Sklearn

{% file src="/files/z4lz5oUQ2ajd2cRh7oxc" %}
Sample Sklearn model for import.
{% endfile %}

{% file src="/files/2pTNuCwfLp2bSwF5cqBy" %}
Sample python script based on the imported Sklearn model.
{% endfile %}

### Sample files for Keras

{% file src="/files/rgsI4afzv9q3KUoRwzup" %}
Sample Keras model for import.
{% endfile %}

{% file src="/files/N83zk98T1su5rAcShxfi" %}
Sample python script based on the imported Keras model.
{% endfile %}

### Sample files for PyTorch

{% file src="/files/jrBPSL4WEpIf87TPmpdN" %}
Sample PyTorch model for import.
{% endfile %}

{% file src="/files/4ovDb8XfupI7ON007qzm" %}
Sample python script based on the imported PyTorch model.
{% endfile %}

{% hint style="info" %}
*<mark style="color:green;">Please Note:</mark>*&#x20;

* *The supported  extensions for External models - .pkl, .h5, .pth & .pt*
* *Refer the **Data Science Lab Quick Start Flow** page to get an overview of the **Data Science Lab** module in nutshell.* [***Click here***](https://docs.bdb.ai/data-science-lab/data-science-lab-quick-start-flow) *to get redirected to the quick start flow page.*
  {% endhint %}


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