ux.md

LPOT UX

LPOT UX is only available on Linux based hosts.

Start the UX

1. Start the LPOT UX server:

   lpot_ux

2. 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.

My Models list

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:

New Model Configuration from New Model Wizard

Basic parameters

1. 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.

2. Open the Wizard by clicking the button.

3. Enter information in all required fields (marked by a *) in the Wizard:

4. Either save this configuration (by clicking Save), or change some advanced parameters (by clicking Next).

Advanced parameters

From the advanced parameters page, you can configure more features such as tuning, quantization, and benchmarking.

New Model Configuration from Examples

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.

Custom dataset or metric

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.

Bert model configuration

• Follow instructions to:
  • install Intel Tensorflow 1.15 up2
  • prepare dataset and a frozen pb model
• In the Create low precision model:
  • select created frozen model
  • choose NLP as model domain
  • select input_file, batch_size in inputs (in that order)
  • choose custom in output and enter IteratorGetNext:3, unstack:0, unstack:1 in input field
  • in Calibration/label_file, select dev-v1.1.json file from created dataset
  • in Calibration/dataset location, select evel.tf_record file from created dataset
  • in Evaluation/Transforms/label_file, select dev-v1.1.json file from created dataset
  • in Evaluation/Transforms/vocab_file, select vocab.txt file from created dataset
  • click Finish or change Advanced parameters

Model Graph Display

For Tensorflow frozen pb models there will be a new button available .

Click it to display graph of selected model:

.

Tuning

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.