8000 Updated documentation for Dashboard. by raresgaia123 · Pull Request #469 · cisco-open/flame · GitHub
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Updated documentation for Dashboard. #469

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77 changes: 77 additions & 0 deletions docs/system/fiab-amzn2-gpu.md
Original file line number Diff line number Diff line change
Expand Up @@ -272,6 +272,83 @@ ping -c 1 mlflow.flame.test
These ping commands should run successfully without any error. As another alternative, open a browser and go to `mlflow.flame.test`.
That should return a mlflow's web page.

## Using Dashboard
Dashboard can be accessed by clicking [here](http://dashboard.flame.test/design).

### Design Creation
From the `Designs` page, a design can be created by clicking on `Create New` button.
After a design is created, the TAG can be created.

<p align="center"><img src="../images/tag_canvas.png" alt="System workflow" width="600px"/></p>

There are two ways to create a TAG:
1. Selecting a pre-defined template from the drop-down
2. From scratch by clicking on `Add Role` button.

After the TAG is created, a code file must be associated with each role.
The code file can be added by clicking on each Role and using the file selector from the bottom of the right-hand side drawer.

<p align="center"><img src="../images/code_file.png" alt="System workflow" width="600px"/></p>

There's also a validation indicator for the TAG on the top-right side of the canvas.

<p align="center"><img src="../images/tag_validation.png" alt="System workflow" width="600px"/></p>

The TAG can be expanded with some simulated workers, by clicking on the `Expand` button.

<p align="center"><img src="../images/expanded_tag.png" alt="System workflow" width="600px"/></p>

Once the TAG is valid, the `Save Schema` button is enabled and can be clicked.

### Datasets Creation
Navigate to the `Datasets` page from the hamburger menu.

<p align="center"><img src="../images/datasets_page.png" alt="System workflow" width="600px"/></p>

In order to add a new `Dataset`, the `Create New` button must be pressed and input the metadata of the `Dataset`.
Also, for that `Dataset` to be visible, it has to be set as public by checking the `Is Public` checkbox.

<p align="center"><img src="../images/dataset_is_public.png" alt="System workflow" width="600px"/></p>

### Job Creation
Next, a `Job` can be created.
Using the hamburger menu, go to `Jobs` page.

<p align="center"><img src="../images/jobs_page.png" alt="System workflow" width="600px"/></p>

Clicking on the `Create New` button, a stepper will be displayed.

On the first step, fill out the details for `Name`, select a `Design`, select the `Backend` option and add the `Timeout` in seconds.

On the second step, select at least one `Dataset`.

On the last step, if there is a pre-trained model, that can be used by typing it's name and version number. Also, pick the `Optimizer` and `Selector` options and set new `Hyperparameters` or update the default ones.

The `Job` can be saved by clicking on the `Save` button.

### Job execution
From the `Jobs` page, click on the menu icon and select the `Start Job` option.

<p align="center"><img src="../images/start_job.png" alt="System workflow" width="600px"/></p>

### Job result
After the `Job` is completed, the results of this `Job` can be viewed by clicking on the `Job Name`.
In this page, there is the visualisation of the expanded topology with actual workers.

More details can be displayed by clicking on the `Graph Icon`.

<p align="center"><img src="../images/graph_icon.png" alt="System workflow" width="600px"/></p>

The first section represents a timeline of each worker runtime metrics.

By clicking on each worker, more details can be displayed like: individual metrics, hyper parameters or model artifact.

<p align="center"><img src="../images/workers.png" alt="System workflow" width="600px"/></p>

Also, the resulted model can be downloaded by clicking on the download icon.

<p align="center"><img src="../images/model.png&qu 9E81 ot; alt="System workflow" width="600px"/></p>

## Stopping flame
Once using flame is done, one can stop flame by running the following command:
```bash
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77 changes: 77 additions & 0 deletions docs/system/fiab-ubuntu.md
Original file line number Diff line number Diff line change
Expand Up @@ -198,6 +198,83 @@ ping -c 1 mlflow.flame.test
These ping commands should run successfully without any errors. As another alternative, open a browser and go to `mlflow.flame.test`.
That should return a mlflow's web page.

## Using Dashboard
Dashboard can be accessed by clicking [here](http://dashboard.flame.test/design).

### Design Creation
From the `Designs` page, a design can be created by clicking on `Create New` button.
After a design is created, the TAG can be created.

<p align="center"><img src="../images/tag_canvas.png" alt="System workflow" width="600px"/></p>

There are two ways to create a TAG:
1. Selecting a pre-defined template from the drop-down
2. From scratch by clicking on `Add Role` button.

After the TAG is created, a code file must be associated with each role.
The code file can be added by clicking on each Role and using the file selector from the bottom of the right-hand side drawer.

<p align="center"><img src="../images/code_file.png" alt="System workflow" width="600px"/></p>

There's also a validation indicator for the TAG on the top-right side of the canvas.

<p align="center"><img src="../images/tag_validation.png" alt="System workflow" width="600px"/></p>

The TAG can be expanded with some simulated workers, by clicking on the `Expand` button.

<p align="center"><img src="../images/expanded_tag.png" alt="System workflow" width="600px"/></p>

Once the TAG is valid, the `Save Schema` button is enabled and can be clicked.

### Datasets Creation
Navigate to the `Datasets` page from the hamburger menu.

<p align="center"><img src="../images/datasets_page.png" alt="System workflow" width="600px"/></p>

In order to add a new `Dataset`, the `Create New` button must be pressed and input the metadata of the `Dataset`.
Also, for that `Dataset` to be visible, it has to be set as public by checking the `Is Public` checkbox.

<p align="center"><img src="../images/dataset_is_public.png" alt="System workflow" width="600px"/></p>

### Job Creation
Next, a `Job` can be created.
Using the hamburger menu, go to `Jobs` page.

<p align="center"><img src="../images/jobs_page.png" alt="System workflow" width="600px"/></p>

Clicking on the `Create New` button, a stepper will be displayed.

On the first step, fill out the details for `Name`, select a `Design`, select the `Backend` option and add the `Timeout` in seconds.

On the second step, select at least one `Dataset`.

On the last step, if there is a pre-trained model, that can be used by typing it's name and version number. Also, pick the `Optimizer` and `Selector` options and set new `Hyperparameters` or update the default ones.

The `Job` can be saved by clicking on the `Save` button.

### Job execution
From the `Jobs` page, click on the menu icon and select the `Start Job` option.

<p align="center"><img src="../images/start_job.png" alt="System workflow" width="600px"/></p>

### Job result
After the `Job` is completed, the results of this `Job` can be viewed by clicking on the `Job Name`.
In this page, there is the visualisation of the expanded topology with actual workers.

More details can be displayed by clicking on the `Graph Icon`.

<p align="center"><img src="../images/graph_icon.png" alt="System workflow" width="600px"/></p>

The first section represents a timeline of each worker runtime metrics.

By clicking on each worker, more details can be displayed like: individual metrics, hyper parameters or model artifact.

<p align="center"><img src="../images/workers.png" alt="System workflow" width="600px"/></p>

Also, the resulted model can be downloaded by clicking on the download icon.

<p align="center"><img src="../images/model.png" alt="System workflow" width="600px"/></p>

## Stopping flame
Once using flame is done, one can stop flame by running the following command:
```bash
Expand Down
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