The first thing you have to do is to download and install VirtualBox. Because you are going to run. Install Raspberry Pi Desktop on a Mac without a DVD drive using a thumb drive We had some trouble installing the Raspberry Pi Desktop on an old Macbook. We think it might be because the Macbook drive was very old.
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- Openpyxl is a Python library to read/write Excel 2010 xlsx/xlsm/xltx/xltm files. It was born from lack of existing library to read/write natively from Python the Office Open XML format.
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- A computer running Steam, Windows 7 or newer, Mac OS X 10.10 (Yosemite) or newer, SteamOS, or Linux Ubuntu 12.04 or newer. A wired network (5 GHz wireless may work, but is not recommended on the Raspberry Pi) A Raspberry Pi 3B or 3B+ running Raspbian Stretch; Supported Input/Controllers. The Steam Controller (wired or using the included.
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 TensorFlow Lite library on thecomputer where you'll connect the USB Accelerator. Then we'll show you how to perform imageclassification with an example app.
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If you want to learn more about the hardware, see theUSB Accelerator datasheet.
Requirements
![Mac Mac](https://www.teknotut.com/wp-content/uploads/2019/04/cara-install-raspberry-pi-headless.png)
- 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, with either MacPorts or Homebrew installed
- Windows 10
- One available USB port (for the best performance, use a USB 3.0 port)
- Python 3.5, 3.6, or 3.7
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.
1a: On Linux
- Add our Debian package repository to your system:
- Install the Edge TPU runtime:
- 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 TensorFlow Lite 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:
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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
- Download and unpack the Edge TPU runtime:
- Install the Edge TPU runtime:Seagate expansion 1tb mac. 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 read more about the performance setting in the USBAccelerator datasheet.
- Now connect the USB Accelerator to your computer using the provided USB 3.0 cable.
Then continue to install the TensorFlow Lite library.
1c: On Windows
- First, make sure you have the latest version of the Microsoft Visual C++ 2019 redistributable.
- Then download edgetpu_runtime_20200728.zip.
- 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. Sharepoint plugin safari mac. 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 read more about the performance setting in the USBAccelerator datasheet. - Now connect the USB Accelerator to your computer using the provided USB 3.0 cable.
2: Install the TensorFlow Lite library
There are several ways you can install TensorFlow Lite APIs, but to get started with Python,the easiest option is to install the
tflite_runtime
library. This library provides the bareminimum code required to run an inference with Python (primarily, the Interpreter
API), thus saving you a lot ofdisk space.To install it, follow the TensorFlow Lite Python quickstart, and then return to this page after you run the
pip3 install
command.3: Run a model using the TensorFlow Lite API
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 model:
- Download the example code from GitHub:
- Download the bird classifier model, labels file, and a bird photo:
- Run the image classifier with the bird photo (shown in figure 1):
You should see results like this:
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Congrats! You just performed an inference on the Edge TPU using TensorFlow Lite.
Cisdem pdf converterocr 5 2 0 download free. To demonstrate varying inference speeds, the example repeats the same inference five times. Itprints the time to perform each inference and then the top classification result (the label ID/nameand the confidence score, from 0 to 1.0). Your inference speeds might differ based on your hostsystem and whether you're using a USB 3.0 connection.
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 TensorFlow Lite Python API, but you can also run aninference using the Edge TPU Python API or the TensorFlow Lite C++ API. For information abouteach option, read the Edge TPU inferencing overview.
Next steps
To run some other types of neural networks, check out our example projects,including examples that perform real-time object detection, pose estimation, keyphrasedetection, on-device transfer learning, and more.
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If you want to create 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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