Introduction
Bella is a spectral render engine which models the complexities of optical physics, using analogies grounded in the real world, to produce a system that behaves in a predictable and intuitive way. Running your Bella engine on Qarnot is as easy as uploading your case and launching a script. Here’s a walkthrough of the steps.
Versions
The test use case uses Bella v.23.6.0.
| Release year | Version |
|---|---|
| jan 2024 | 23.6.0 |
If you are interested in another version, please send us an email at qlab@qarnot.com.
Prerequisites
Before launching the case, please ensure that the following prerequisites have been met.
- Create an account (create an account)
- Retrieve the authentication token (authentification token)
- Install one of Qarnot’s SDK (Python) (Node.js) (C#) (Commande Line)
Test cases
This use case is based on the 3D Bella render tutorial simulation. The documentation is available here : bella_scenes_documentation. Some use cases are available on the bella render documentation. We suggest here to compute a simple orange-juice scene.
You can download the input scene here : orange-juice.bsz and drag it into a folder named ‘input’ to launch the calculation.
Launching a batch case
Before starting a calculation with the Python SDK, a few steps are required :
- Please ensure that you have created a Qarnot account create qarnot account.
- Retrieve the authentication token get token.
- Install Qarnot’s Python SDK qarnot-install.
Note: in addition to the Python SDK, Qarnot provides C# and Node.js SDKs and a Command Line.
Copy the following code in a Python script and save it next to the input folder you unzipped before. Be sure you have copied your authentication token in the script (instead of <<<MY_SECRET_TOKEN>>>) to be able to launch the task on Qarnot.
Results
At any given time, you can monitor the status of your task on Tasq :
Once the task is deployed, it should take around 15 minutes to run. You should also now have a result folder in the output bucket and on your computer containing all the numerical results. You can open the out_orange_0_0000.png (~40Mb) and out_orange_0_0001.png (~40Mb) files to view the results.
The expected results are as follow :
You could test in the same way any other scenes available in the tutorial.
Available parameters :
* RESSOURCE_PATH : Default /job
* BENCHMARK : Activate the benchmark options (True of False)
* RESOLUTION : Override the resolution in the bsx file. (eg : 60x60)
* RENDER_TURN_TABLE : Tells the CLI to render a turntable animation of the input file. (True or False)
* TURN_TABLE_ANGLE : Sets the angle between frames for a turntable animation (eg : 160)
* TURN_TABLE_STEP : Sets the total angle covered by a turntable animation. (eg : 40)
* PARSE_FRAGMENTS : Provide a BSA fragment to be parsed by the scene (eg : camera.resolution=vec2(100 100); beautyPass.targetNoise=10u; )
* FINAL_BSI_DIR : The relative path where bsi objects will be saved (saved as according to beautyPass:saveBsi). It will be available in the output bucket.
* FINAL_BSI_NAME : The name of bsi objects will be saved
* OUTPUT_PATH : The output relative path where outputs will be saved. It will be available in the output bucket with name <output_path>_0_xxxx with xxxx the image increment number (eg : out_orange_0_0001.png).
* LICENSE_PATH : The path to the license in the input bucket.
License
The exemple below is a simple use case working without license.
Of course, you can bring your own license by uploading the license file in the input bucket and setting its path in the LICENSE_PATH parameter.
Wrapping up
That’s it! If you have any questions, please contact qlab@qarnot.com and we will help you with pleasure!