Edge Tpu | Google Iot | Googlecloud Tutorial

Introduction to Edge TPU

The Edge TPU is a small, specialized ASIC designed by Google that provides high performance for machine learning inference at the edge. It is part of Google's Cloud IoT solutions and is aimed at enabling efficient AI processing on devices with limited resources. The Edge TPU allows for low-latency inference with low power consumption, making it ideal for IoT devices and other edge applications.

Getting Started with Edge TPU

To get started with Edge TPU, you need the following:

An Edge TPU device, such as the Coral USB Accelerator or the Coral Dev Board.
A compatible host machine (e.g., a computer, Raspberry Pi, or other single-board computer).
Basic knowledge of Python and machine learning frameworks.

Setting Up the Environment

First, we need to set up the environment for running machine learning models on the Edge TPU. Follow these steps:

1. Install the Edge TPU Runtime

Install the Edge TPU runtime on your host machine. For Debian-based systems, use the following commands:

$ sudo apt-get update

$ echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list

$ curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -

$ sudo apt-get update

$ sudo apt-get install libedgetpu1-std

Running a Pre-trained Model on Edge TPU

Next, let's run a pre-trained model on the Edge TPU. Google provides several pre-trained models that are optimized for the Edge TPU. Follow these steps:

1. Download the Model

Download a MobileNet model optimized for Edge TPU:

$ wget https://dl.google.com/coral/canned_models/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite

$ wget https://dl.google.com/coral/canned_models/inat_bird_labels.txt

2. Install the Required Python Packages

Install the required Python packages:

$ pip install tflite_runtime

$ pip install numpy

3. Run the Inference

Create a Python script to run the inference:

import numpy as np

from tflite_runtime.interpreter import Interpreter

from tflite_runtime.interpreter import load_delegate

import cv2

import time

# Load the model

interpreter = Interpreter(model_path="mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite",

experimental_delegates=[load_delegate('libedgetpu.so.1')])

interpreter.allocate_tensors()

# Get input and output tensors

input_details = interpreter.get_input_details()

output_details = interpreter.get_output_details()

# Prepare the input image

input_shape = input_details[0]['shape']

img = cv2.imread('bird.jpg')

img = cv2.resize(img, (input_shape[1], input_shape[2]))

img = np.expand_dims(img, axis=0)

# Set the tensor to point to the input data to be inferred

interpreter.set_tensor(input_details[0]['index'], img)

# Run the inference

start_time = time.time()

interpreter.invoke()

end_time = time.time()

# Get the results

output_data = interpreter.get_tensor(output_details[0]['index'])

print("Inference time: ", end_time - start_time)

print("Output:", output_data)

Building Custom Models for Edge TPU

To build custom models for Edge TPU, you need to follow these steps:

1. Train a Model

Train a model using TensorFlow or another machine learning framework. Ensure that the model is compatible with TensorFlow Lite.

2. Convert the Model to TensorFlow Lite Format

Convert the trained model to TensorFlow Lite format using the TFLite Converter:

import tensorflow as tf

converter = tf.lite.TFLiteConverter.from_saved_model('path/to/saved_model')

tflite_model = converter.convert()

with open('model.tflite', 'wb') as f:

f.write(tflite_model)

3. Compile the Model for Edge TPU

Use the Edge TPU compiler to compile the TensorFlow Lite model for the Edge TPU:

$ edgetpu_compiler model.tflite

Conclusion

The Edge TPU is a powerful tool for running machine learning models on edge devices with limited resources. By following this comprehensive tutorial, you should now have a good understanding of how to set up the Edge TPU, run pre-trained models, and build custom models for the Edge TPU. Happy learning!

Edge TPU Comprehensive Tutorial