Versions
Choosing a model to analyze and examine in Tensorleap and managing uploaded models
The Versions view is where you track all the saved versions and models of your project. From here, you can select which model to examine in the Network Tab, add a model run (i.e. a specific evaluation) for analysis in the dashboard, delete, export, and upload new models via the Platform.
Versions Layout Overview
Click on the top left to open the Versions view. Once open, you can choose to fix the view by clicking
.

The Versions view lists all the models you have in your project. In the Top of the version control you have the Project Name, and below it, the name of the currently selected model that is viewed within the Network Tab. The selected model is also highlighted in a different background color than the other models.
Many evaluation of the same model could be made with different code integration script & versions. Each of these would result in a model run, that could be viewed within the version control by expanding a Model version. hovering over a Run would provide extra details: when did it started, and what script version did it use for the evaluation.

Switching & modifying Model versions
To review and modify different models that exists in the platform - simply hover the mouse over the required model, and click "Open Commit"

Deleting and renaming Models and Model Runs
To delete a model or a model run: hover over it, and click the trash can icon.
To rename a model or a model run: click over the name of the requested version and enter a new name.
Adding a model run to dashboard analysis
Once an evaluate is completed, multiple Model Runs could be added to the dashboard panel for analysis purposes. To do so, click the "Add To Dashboard" Icon that is to right of each Model Run.
Saving a Model Version
In order to keep changes to the model mapping's or to the selected code integration, the model version needs to be saved. Saving a model can be done either in-place (overwriting current model configurations) or to a new model.

To save a current model we need to click on the top disk icon. This would ensure any future evaluation of this model would use the current configuration.
To save to a new model we can click the "disk with pencil" icon to the bottom. This would require the user to provide a new name for the experiment and then "Save it as a new version".
Export & Upload a model from Tensorleap
Exporting a model

To export a trained model out of Tensorleap:
With the Versions view open, search for the model
Hover your mouse on the model, then click
on the right to open the Export Model window.
On the Export Model window, select the format in which the model will be saved.
Click
to start the export process.
The job appears on the list to the right with status set to Pending. A notification message also appears briefly on your screen.
6. Once Tensorleap completes compiling the file, status is set to Finished.
7. Click to save the file to your computer.

The available export formats are Json (Tensorflow 2), H5 (Tensorflow 2), ONNX , SavedModel (Tensorflow)
Importing a model via the Platform

To import a model from the platform, click the top most cloud icon in the version panel. This would allow you to provide a name for the Model, select its type, select it from a location on your disk and upload it to the Tensorleap system.

It is highly recommended to use the CLI to upload models and codebases into the platform instead of the UI interfaces.
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