Install Edge Mac

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The Coral USB Accelerator is a USB device that provides an Edge TPU as a coprocessor for yourcomputer. It accelerates inferencing for your machine learning models when attached to eithera Linux, Mac, or Windows host computer. This page is your guide to get started.

All you need to do is download the Edge TPU runtime and the PyCoral library on yourcomputer. Then we'll show you how to run a TensorFlow Lite model using the accelerator.

If you want to learn more about the hardware, see theUSB Accelerator datasheet.

Install Edge Mac

Requirements

  • A computer with one of the following operating systems:
    • Linux Debian 10, or a derivative thereof (such as Ubuntu 18.04), and a system architecture of either x86-64, Armv7 (32-bit), or Armv8 (64-bit) (Raspberry Pi is supported, but we have only tested Raspberry Pi 3 Model B+ and Raspberry Pi 4)
    • macOS 10.15 (Catalina) or 11 (Big Sur), with either MacPorts or Homebrew installed
    • Windows 10
  • One available USB port (for the best performance, use a USB 3.0 port)
  • Python 3.6-3.9

1: Install the Edge TPU runtime

The Edge TPU runtime provides the core programming interface for the Edge TPU. You can install it onyour host computer as follows, on Linux, on Mac, oron Windows.

Edge

This wikiHow will show you how to install web apps on Microsoft Edge. In order to do so, you will need the new Chromium-based Microsoft Edge. Navigate to the web site with the web app. Currently, only a small selection of sites have web. Edge looks like a Mac browser, not like a Windows browser with a Big Sur facelift. It feels more Mac-like to me than Chrome. Not everything is lollipops and roses, though. I didn’t care much for.

1a: On Linux

  1. Add our Debian package repository to your system:

  2. Install the Edge TPU runtime:

  3. Now connect the USB Accelerator to your computer using the provided USB 3.0 cable. If you alreadyplugged it in, remove it and replug it so the newly-installed udev rule can take effect.

Then continue to install the PyCoral library.

Install with maximum operating frequency (optional)

The above command installs the standard Edge TPU runtime for Linux, which operates the device at areduced clock frequency. You can instead install a runtime version that operates at the maximumclock frequency. This increases the inferencing speed but also increases powerconsumption and causes the USB Accelerator to become very hot.

If you're not certain your application requires increased performance, you should use the reducedoperating frequency. Otherwise, you can install the maximum frequency runtime as follows:

You cannot have both versions of the runtime installed at the same time, but you can switch bysimply installing the alternate runtime as shown above.

Caution: When operating the device using the maximum clock frequency, the metal on the USB Accelerator can become very hot to the touch. This might cause burn injuries. To avoid injury, either keep the device out of reach when operating it at maximum frequency, or use the reduced clock frequency.

1b: On Mac

  1. Download and unpack the Edge TPU runtime:

  2. Install the Edge TPU runtime:

    The installation script will ask whether you want to enable the maximum operating frequency.Running at the maximum operating frequency increases the inferencing speed but also increasespower consumption and causes the USB Accelerator to become very hot. If you're not certain yourapplication requires increased performance, you should type 'N' to use the reduced operatingfrequency. You can change this later by re-running this script.

    You can read more about the performance setting in section 4.1 of the USBAccelerator datasheet.

  3. Now connect the USB Accelerator to your computer using the provided USB 3.0 cable.

Then continue to install the PyCoral library.

1c: On Windows

  1. First, make sure you have the latest version of the Microsoft Visual C++ 2019 redistributable.

  2. Then download edgetpu_runtime_20210726.zip.

  3. Extract the ZIP files and double-click the install.bat file inside.

    A console opens to run the install script and it asks whether you want to enablethe maximum operating frequency. Running at the maximum operating frequency increases theinferencing speed but also increases power consumption and causes the USB Accelerator to becomevery hot. If you're not certain your application requires increased performance, you should type'N' to use the reduced operating frequency. You can change this later by re-running this script.

    You can read more about the performance setting in section 4.1 of the USBAccelerator datasheet.

  4. Now connect the USB Accelerator to your computer using the provided USB 3.0 cable.

2: Install the PyCoral library

PyCoral is a Python library built on top of the TensorFlow Lite library to speed up your developmentand provide extra functionality for the Edge TPU.

We recommend you start with the PyCoral API, and we use this API in our example code below,because it simplifies the amount of code you must write to run an inference. But you can build yourown projects using TensorFlow Lite directly, in either Python or C++.

To install the PyCoral library (and its dependencies), use the following commands based on your system.

2a: On Linux

If you're using Debian-based Linux system (including a Raspberry Pi), install PyCoral as follows:

2b: On Mac and Windows

If you're using Mac or Windows, install PyCoral as follows:

Windows users: Instead of typing python3 as shown here (and elsewhere in our docs), you can use the pylauncher. Just be sure you use Python 3.5 or newer.

Alternatively, you can download a specific PyCoral wheel fileand pass it to pip install.

3: Run a model on the Edge TPU

Install Edge For Mac

Now you're ready to run an inference on the Edge TPU.

Windows users: The following code relies on a Bash script to install dependencies. If you're new to using Bash on Windows, we suggest you try either Windows Subsystem for Linux or Git Bash from Git for Windows.

Follow these steps to perform image classification with our example code and MobileNet v2:

  1. Download the example code from GitHub:

  2. Download the model, labels, and bird photo:

  3. Run the image classifier with the bird photo (shown in figure 1):

You should see results like this:

Congrats! You just performed an inference on the Edge TPU using TensorFlow Lite.

To demonstrate varying inference speeds, the example repeats the same inference five times.Your inference speeds might differ based on your host system and whether you're using USB 2.0or 3.0.

The top classification label is printed with the confidence score, from 0 to 1.0.

To learn more about how the code works, take a look at the classify_image.py source codeand read about how to run inference with TensorFlow Lite.

Note:The example above uses the PyCoral API, which calls into the TensorFlow Lite Python API, but you caninstead directly call the TensorFlow Lite Python API or use the TensorFlow Lite C++ API. For moreinformation about these options, read theEdge TPU inferencing overview.
Mac

Next steps

To run some other models, such as real-time object detection, pose estimation, keyphrase detection,on-device transfer learning, and others, check out our example projects. Inparticular, if you want to try running a model with camera input (including support for theRaspberry Pi camera), try one of the several cameraexamples.

If you want to train your own model, try these tutorials:

  • Retrain an image classification model using post-training quantization (runs in Google Colab)
  • Retrain an image classification model using quantization-aware training (runs in Docker)
  • Retrain an object detection model using quantization-aware training (runs in Docker)

Or to create your own model that's compatible with the Edge TPU, readTensorFlow Models on the Edge TPU.

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