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LPOT UX

LPOT UX is only available on Linux based hosts.

Start the UX

  1. Start the LPOT UX server:

    lpot_ux

    Note: TF 2.5.0 requires setting environment variable TF_ENABLE_MKL_NATIVE_FORMAT=0 for INT8 quantization:

    TF_ENABLE_MKL_NATIVE_FORMAT=0 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.

Home

This view shows introduction to LPOT and 2 buttons for creating new configurations in 2 different ways. First one links to Examples, the second one to Config wizard.

Home

On the left hand side there is a panel with list of configurations.

Menu

One can see system information by clicking System info button. The result is details dialog:

System info table

By clicking See models button you can navigate to My models list.

Configuration details

When clicking on configuration from the left hand side list, you can see its details view. You can see the results, rerun the tuning, check the configuration and console output. You can also see the model graph.

Details

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:

My models

New Model Configuration from New Model Wizard

Basic parameters

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

Wizard1 Wizard2

  1. Either save this configuration (by clicking Finish), or change some advanced parameters (by checking the checkbox Show advanced ).

Advanced parameters

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

Wizard advanced

New Model Configuration from Examples

Examples

Included are models you can use to test tuning. You have to point to the Dataset that you want to use click Finish too add it to your models. A new model will be downloaded and 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 in first step:
    • select created frozen model
    • 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 Bert1
  • in second step don't change anything Bert2
  • in third step :
    • choose NLP as model domain
    • 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 Bert3

Model Graph Display

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

Click it to display graph of selected model:

Bert model graph.

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 Start Tuning button 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.