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Start the LPOT UX server:
lpot_ux
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The server prints information on how to access the Web UI.
An example message looks like this:
LPOT UX Server started. Setup port forwarding from your local port 5000 to 5000 on this machine. Then open address http://localhost:5000/?token=338174d13706855fc6924cec7b3a8ae8
Make certain that requested port forwarding is set up (depending on your OS) and then open the address in your web browser.
This view lists all Model Configurations defined on a given server.
You can create a new model using pre-defined models by using a New Model Wizard or Examples:
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If all related files are located in one directory, point your Workspace there.
Click the button (on the top-left part of UX) and navigate to the desired directory. Click Choose to confirm your selection.
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Enter information in all required fields (marked by a *) in the Wizard:
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Either save this configuration (by clicking Save), or change some advanced parameters (by clicking Next).
From the advanced parameters page, you can configure more features such as tuning, quantization, and benchmarking.
Included are models you can use to test tuning. Visit Examples to:
- Download a model to a selected Workspace.
- Download a predefined configuration file for models.
When both models and configurations are downloaded, you can point to the Dataset to be used and then click Add to my models. A new model will be added to the My models list, ready for tuning.
If you choose custom in the Dataset or Metric section, the appropriate code templates will be generated for you to fill in with your code. The path to the template will be available by clicking the Copy code template path button located in the right-most column in the My models list.
Follow the comments in the generated code template to fill in required methods with your own code.
Now that you have created a Model Configuration, you can do the following:
- See the generated config (by clicking the Show config link).
- Start the tuning process:
- Click the blue arrow to start the tuning.
- Click Show output to see logs that are generated during tuning.
- Your model will be tuned according to configuration.
- When the tuning is finished, you will see accuracy results in the My Models list:
- The Accuracy section displays comparisons in accuracy metrics between the original and tuned models.
- Model size compares the sizes of both models.
- When automatic benchmarking is finished, Throughput shows the performance gain from tuning.
You can provide your own certificate and key to the server in order to use TLS-encrypted communication between the UI and the server.
Add two parameters to the server start command:
lpot_ux --certfile path_to_cert.crt --keyfile path_to_private_key.key