# Tensorleap Integration

This page outlines the different elements of the tensorleap integration and reviews the integration flow and development cycle within the platform.

### Prerequisites

* [Access to Tensorleap](/getting-started/tensorleap-setup/installation.md#accessing-the-tensorleap-platform) 
* Leap CLI is [installed](/getting-started/tensorleap-setup/installation.md#tensorleap-cli-installation) and [authenticated](/getting-started/tensorleap-setup/cli-authentication.md)
* .onnx or .h5 model
* [A valid code integration](/tensorleap-integration/writing-integration-code.md) that instructs Tensorleap on how to load the dataset and parse and visualize different elements. 
* Tensoleap [configuration yamls](/tensorleap-integration/configurations-files.md):
  * A [leap.yaml](/tensorleap-integration/leap.yaml.md) file that contains the setup of your integration.
  * (optionally) requirements.txt file if non-default requirements are needed
* An [integration test](/tensorleap-integration/integration-test.md) script that instructs Tensorleap on what code needs to be run during model analysis (loss, metrics, visualizers & metadata)

### Expected Folder Structure

The expected file structure for a Tensorleap code integration is the following:

```
my_leap_project/
├── ...
├── leap_binder.py
├── leap.yaml
├── integration_test.py
```

Here, leap\_binder.py contains the [integration script](/tensorleap-integration/writing-integration-code.md).

### The Tensorleap integration flow 

1. Start from an existing repo from our [Leap Hub](/getting-started/quickstart/quickstart-using-leap-hub.md) or use the CLI to add template Tensorleap integration files into your existing repository.
2. Make sure your data is accessible by the [Tensorleap Server](/getting-started/tensorleap-setup/installation.md#tensorleap-server).
3. Create a basic [Integration script](/tensorleap-integration/writing-integration-code.md). This can include an [input\_encoder](/tensorleap-integration/writing-integration-code/input-encoder.md), [GT encoder](/tensorleap-integration/writing-integration-code/ground-truth-encoder.md), [loss](/tensorleap-integration/writing-integration-code/custom-loss-function.md), and a basic [visualizer](/tensorleap-integration/writing-integration-code/visualizer-function.md) for the input, prediction and GT.
4. Verify your integration validity locally using an [Integration test](/tensorleap-integration/integration-test.md).
5. Use the [CLI](/tensorleap-integration/uploading-with-cli/cli-assets-upload.md) to push the code and models to the platform 
6. Use the Platform to [Evaluate](/user-interface/project/menu-bar/evaluate-a-model.md) and process the data using your model.

{% hint style="info" %}
After the integration step the model and dataset can be analyzed using the platform. Usually, during the analysis phase the insights and analysis being done points to a missing metadata, better visualization method, or a new metric. These elements should then be added to the integration script and then steps 4-7 are repeated.
{% endhint %}


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