Add a TensorFlow Lite Modular Service

Viam provides an example modular resource written in C++ that extends the ML model service to run any TensorFlow Lite model. The example includes an inference client program as well, which generates audio samples and uses the modular resource to classify the audio samples based on a pre-trained model.

This tutorial walks you through everything necessary to start using these example files with your machine, including building the C++ SDK, configuring your machine and installing viam-server, and generating results with the example inference client program.

The provided example code demonstrates the design, implementation, and usage of a custom module to help you write your own. This code is for instructional purposes only, and is not intended for production use.

You can find the example files in the Viam C++ SDK:

• example_mlmodelservice_tflite.cpp - an example module which provides the MLModelService modular resource capable of running any TensorFlow Lite model.
• example_audio_classification_client.cpp - an example inference client which generates audio samples and invokes the example_mlmodelservice_tflite module to classify those samples using the yamnet/classification TensorFlow Lite model.

Build the C++ SDK

To build the Viam C++ SDK, you will need a macOS or Linux computer. Follow the instructions below for your platform:

Download the yamnet/classification model file

This example uses the yamnet/classification TensorFlow Lite model for audio classification.

1. Download the yamnet/classification TensorFlow Lite model file and place it in your example_workspace directory:

   curl -Lo ~/example_workspace/lite-model_yamnet_classification_tflite_1.tflite https://tfhub.dev/google/lite-model/yamnet/classification/tflite/1?lite-format=tflite
   

   Alternatively, you may download the model file here: yamnet classification tflite model. If you download in this fashion, move the downloaded file to your ~/example_workspace directory.

2. Extract the labels file yamnet_label_list.txt from the downloaded model file:

   unzip ~/example_workspace/lite-model_yamnet_classification_tflite_1.tflite -d ~/example_workspace/
   

   The labels file provides pre-populated labels for the calculated scores, so that output scores can be associated and returned with their matching labels. You can omit this file if desired, which will cause the inference client to return the computed scores without labels.

Install viam-server

Next, install viam-server on your machine, if you have not done so already:

1. Navigate to the Viam app in your browser and add a new machine.

2. Navigate to the CONFIGURE tab and find your machine’s card. An alert will be present directing you to Set up your machine part. Click View setup instructions to open the setup instructions.

3. Select your system’s OS and architecture. Keep the RDK type as RDK. Follow the steps listed to install viam-server on your system.

   Important

   If you are installing viam-server within the bullseye Docker container provided with the C++ SDK, you will need to run the following command instead of the command listed in step 2 Download and install viam-server in the Viam app:

   curl https://storage.googleapis.com/packages.viam.com/apps/viam-server/viam-server-stable-x86_64.AppImage -o viam-server && chmod 755 viam-server && sudo ./viam-server --appimage-extract-and-run -config /etc/viam.json
   

4. Once complete, verify that step 3 of the setup instructions indicates that your machine has successfully connected.

5. Stop viam-server by pressing CTL-C on your keyboard from within the terminal window where you entered the commands from step 3 above.

Generate your machine configuration

When you built the C++ SDK, the build process also built the example_audio_classification_client binary, which includes a --generate function that determines and creates the necessary machine configuration to support this example.

To generate your machine’s configuration using example_audio_classification_client:

1. First, determine the full path to the yamnet/classification model you just downloaded. If you followed the instructions above, this path is: ~/example_workspace/lite-model_yamnet_classification_tflite_1.tflite.

2. Next, determine the full path to the example_mlmodelservice_tflite modular resource example provided with the Viam C++ SDK. If you followed the instructions above, this path is: ~/example_workspace/opt/bin/example_mlmodelservice_tflite.

3. Run the example_audio_classification_client binary, providing both paths to the --generate function in the following fashion:

   cd ~/example_workspace/opt/bin
   ./example_audio_classification_client --generate --model-path ~/example_workspace/lite-model_yamnet_classification_tflite_1.tflite --tflite-module-path ~/example_workspace/opt/bin/example_mlmodelservice_tflite > ~/example_workspace/viam-example-mlmodel-config.json
   

4. Verify that the resulting configuration file was created successfully:

   cat ~/example_workspace/viam-example-mlmodel-config.json
   

5. Copy the contents of this file. Then return to your machine’s page on the Viam app, select the CONFIGURE tab, select JSON mode, and add the configuration into the text area.

   Important

   If you already have other configured components, you will need to add each generated JSON object to the respective modules or services array. If you do not already have configured components, you can replace the contents in JSON with the generated contents.

6. Click the Save button in the top right corner of the page. Now, when you switch back to Builder mode, you can see the new configuration settings.

This generated configuration features the minimum required configuration to support this tutorial: services parameters for the ML model service and modules parameters for the example_mlmodelservice_tflite module.

Run the inference client

With everything configured and running, you can now run the inference client that connects to viam-server and uses the example_mlmodelservice_tflite module.

1. First, determine your machine address and API key and API key ID. To do so, navigate to your machine’s CONNECT tab on the Viam app, select the Code sample page, and toggle Include API Key. The API key resembles abcdef1g23hi45jklm6nopqrstu7vwx8, the API key ID resembles a1234b5c-678d-9012-3e45-67fabc8d9efa and the machine address resembles my-machine-main.abcdefg123.viam.cloud.

   Caution

   Do not share your API key or machine address publicly. Sharing this information could compromise your system security by allowing unauthorized access to your machine, or to the computer running your machine.

2. Next, start viam-server once more on your machine, this time as a background process:

   viam-server -config /etc/viam.json
   

   Important

   If you are working within the bullseye Docker container on Linux, run the following command instead of the above, from within the directory you installed viam-server to:

   ./viam-server --appimage-extract-and-run -config /etc/viam.json &
   

3. Then, run the following to start the inference client, providing the necessary access credentials and the path to the labels file extracted earlier:

   cd ~/example_workspace/opt/bin
   ./example_audio_classification_client --model-label-path ~/example_workspace/yamnet_label_list.txt --robot-host my-machine-main.abcdefg123.viam.cloud --robot-api-key abcdef1g23hi45jklm6nopqrstu7vwx8 --robot-api-key-id a1234b5c-678d-9012-3e45-67fabc8d9efa
   

   The command should return output similar to:

   0: Static                               0.5
   1: Noise                                0.332031
   2: White noise                          0.261719
   3: Cacophony                            0.109375
   4: Pink noise                           0.0585938
   
   Measuring inference latency ...
   Inference latency (seconds), Mean: 0.012795
   Inference latency (seconds), Var : 0.000164449
   

   The labels shown in the example output require that you have provided the yamnet_label_list.txt labels file to example_audio_classification_client using the --model-label-path flag. If you have omitted the labels file, the computed scores will be returned without labels.

Understanding the code

The example_mlmodelservice_tflite module, and the MLModelService modular resource it provides, extends the existing ML model service to run any TensorFlow Lite model. The example_audio_classification_client inference client provides sample audio data and the yamnet/classification TensorFlow Lite model to the MLModelService modular resource and interprets the results it returns.

All example code is provided in the Viam C++ SDK in the src/viam/examples/ directory.

The code in src/viam/examples/mlmodel/example_audio_classification_client.cpp is richly commented to explain each step it takes in generating and analyzing the data provided. What follows is a high-level overview of the steps it takes when executed:

1. The inference client generates two signals that meet the input requirements of the yamnet/classification model: the first signal is silence, while the second is noise. As written, the example analyzes only the noise signal, but you can change which signal is classified by changing which is assigned to the samples variable in the code.

2. The client then populates an input tensor named sample as a tensor_view over the provided sample data. The tensor must be named according to the configured value under tensor_name_remappings in your machine configuration. If you followed the instructions above to generate your machine configuration, the value sample was pre-populated for you in your generated machine configuration.

3. The client invokes the infer method provided by the example_mlmodelservice_tflite module, providing it with the sample input tensor data it generated earlier.

4. The example_mlmodelservice_tflite module returns a map of response tensors as a result.

5. The client validates the result, including its expected type: a vector of float values. The expected output must be defined under tensor_name_remappings in your machine configuration for validation to succeed. If you followed the instructions above to generate your machine configuration, the value categories was pre-populated for you in your generated machine configuration.

6. If a labels file was provided, labels are read in as a vector of string values and the top 5 scores are associated with their labels.

7. Finally, the client runs 100 rounds of inference using the determined label and score pairs, and returns the results of the rounds, including mean and variance values.

Similarly, the example_mlmodelservice_tflite module can be found at src/viam/examples/modules/example_mlmodelservice_tflite.cpp and also offers rich comments explaining its features and considerations.

Next steps

This tutorial explores audio classification using the yamnet/classification TensorFlow Lite model, but the MLModelService modular resource provided by the example_mlmodelservice_tflite module can accept any TensorFlow Lite model so long as it fulfils the TFLite model constraints.

Once you have run the example and examined the module and client code, you might explore the following next steps:

• Write a client similar to example_audio_classification_client that generates a different kind of data and provides a suitable TensorFlow Lite model for that data to the MLModelService modular resource. For example, you might find a new pre-trained TensorFlow Lite model that analyzes speech waveforms and write a client to provide these waveform samples to the MLModelService modular resource and interpret the results returned.
• Write a client similar to example_audio_classification_client that trains its own model on existing or incoming data, as long as that model fulfils the TFLite model constraints. For example, you might add a movement sensor component to your machine that captures sensor readings to the built-in data management service. Then you could write a client that trains a new model based on the collected data, provides the model and new sensor data readings to the MLModelService modular resource, and interprets the results returned.
• Write a module similar to example_mlmodelservice_tflite that accepts models for other inference engines besides TensorFlow Lite, then write a client that provides a valid model and source data for that inference engine.

Troubleshooting and additional documentation

• If you experience issues building the C++ SDK, see C++ SDK: Limitations, Known Issues, and Troubleshooting.
• To customize your C++ build process or make adjustments to fit your platform or deployment requirements, see C++ SDK: Options to Configure or Customize the Build

You can find additional reference material in the C++ SDK documentation.