# Deploying Models > Deploy trained machine learning models to the target hardware. With a completed model, it's time to test it out on real hardware. 1. Select & Download the model 2. Test it in simulation (Optional) 3. Deploy it to the target hardware ## Download the Model Once training has successfully completed, there may be multiple models available. These will typically involve a tradeoff between accuracy vs flash footprint & processing time. Once selected, download the model locally. It comes bundled as the model source files & weights and a standalone executable that can be used to test the model on your local machine. ## Test the Model in Simulation (Optional) Downloading the model contains two key components: 1. The ML algorithms C code, to be deployed to the embedded device. 2. An standalone executable (for each OS) that can be run on your local machine. Using the standalone executable downloaded with the model, you can test the model in a simulated environment on your local machine. The model accepts a CSV input and requires similar configuration as the model training (like specifying the label and session columns when applicable). The standalone executable is very useful to validate the model on the entire dataset, non-training data or specific scenarios before deploying it to the target hardware. When deploying the model to the target hardware, sometimes the model can be incorrectly input or configured. It's also useful to sanity check the model's behaviour in a controlled environment, allowing you to test standardised inputs and validate the outputs are identical in the both the simulation and the target hardware environments. These tests help identify any discrepancies in the model before deploying to firmware early on and isolating firmware issues. Take note that the input columns won't necessarily match the training csv data. The resultant order is not clear, but column headers seem to be sorted alphabetically. e.g. Using training data formatted like
acc_x acc_y acc_z acc_mag
Needs to be rearranged to this for model input:
acc_mag acc_x acc_y acc_z
When testing using the standalone executable, ensure the CSV data used is re-ordered to match. ## Deploy the Model to the Target Hardware Once you are satisfied with the model's performance in simulation, you can deploy it to the target hardware. Follow the hardware-specific instructions to transfer the model files and executable to the device. Refer to the sample in: `samples/algorithms/imu_algo`. Insert the algorithm into `nrf_edgeai_generated` folder into the `imu_algo/gesture_algorithm` subfolder. The sample contains two `main.c` files: 1. The first being the general application setting up hardware, sampling accelerometer, 2. The second containing the gesture algorithm and used as a standalone extension added to the first dynamically. However, there are a few points that need to be updated to accomodate the model: ### Model input count Check `nrf_edgeai_user_model.c` for the parameter `USER_UNIQ_INPUTS_NUM`. This indicates the number of input values the model expects. In `imu_algo/gesture_algorithm/main.c`, update the `USER_UNIQ_INPUTS_NUM` value to the corresponding value above. ### Model Input type Similarly, check the model configuration for the input type: `INPUT_FEATURE_DATA_TYPE`. In the `main.c`, search for the `flt_32 input_sample` and update the type depending on the `INPUT_FEATURE_DATA_TYPE`, using `flt32_t`, `int16_t` or `int8_t`. ### Input model order Similarly, the model is fed by copying values to `input_sample`, before being provided to the model e.g. ```c input_sample[0] = mag(acc_sample->x, acc_sample->y, acc_sample->z); input_sample[1] = (float)acc_sample->x; input_sample[2] = (float)acc_sample->y; input_sample[3] = (float)acc_sample->z; nrf_edgeai_feed_inputs(user_model, input_sample, USER_UNIQ_INPUTS_NUM); ``` These will need to be updated depending on the input order of the model. For accelerometer values, refer to the `acc_sample->``x`/`y`/`z`. Here, it's simpler to calculate magnitudes manually. Additionally, gyro data can be integrated easily using e.g. ```c /* Feed data into the model.*/ const struct imu_sample *acc_sample; const struct imu_sample *gyro_sample; ... imu_data = zbus_chan_const_msg(chan); acc_start = imu_data->accelerometer.offset; gyro_start = imu_data->gyroscope.offset; inference_window_start = imu_data->accelerometer.timestamp_ticks; for (int i = 0; i < imu_data->accelerometer.num; i++) { /* This model expects Floating Point inputs, and a magnitude */ acc_sample = &imu_data->samples[acc_start + i]; gyro_sample = &gyro_data->samples[gyro_start + i]; input_sample[0] = sqrtf((float)acc_sample->x * (float)acc_sample->x + (float)acc_sample->y * (float)acc_sample->y + (float)acc_sample->z * (float)acc_sample->z); input_sample[1] = (float)acc_sample->x; input_sample[2] = (float)acc_sample->y; input_sample[3] = (float)acc_sample->z; input_sample[4] = sqrtf((float)gyro_sample->x * (float)gyro_sample->x + (float)gyro_sample->y * (float)gyro_sample->y + (float)gyro_sample->z * (float)gyro_sample->z); input_sample[5] = (float)gyro_sample->x; input_sample[6] = (float)gyro_sample->y; input_sample[7] = (float)gyro_sample->z; /* Feed this sample pair into the model's windowing buffer */ res = nrf_edgeai_feed_inputs(user_model, input_sample, USER_UNIQ_INPUTS_NUM); if (res == NRF_EDGEAI_ERR_INPROGRESS) { continue; } ... ``` ### Output Class Names Depending on which classes the model will output, update the class labels at the top of the file. ```c const char *class_strings[] = { "Double Shake", "Double Tap", "Triple Tap", "CW rotate", "CCW rotate", "Idle", "Unknown", }; ``` ### Output class probabilities. The sample model outputs the class probabilities as floating point values. However, depending on the model, it can output integer probabilities (uint8 or uint16). If so update the probability extraction to accomodate this: ```c printk("class: %s (%d) %.3f\n", class_strings[predicted_class], predicted_class, (double)user_model->decoded_output.classif.probabilities .p_f32[predicted_class]); ``` Changing `user_model->decoded_output.classif.probabilities.p_f32` to the appropriate type. Test the model on the hardware to ensure it performs as expected.