[BlueZ] 1、Download install and use the BlueZ and hcitool on

作者: 软件开发  发布:2019-11-03

3.2 hcitool scan for bluetooth devices

Before start scanning make sure that your bluetooth device is turned on and not blocked, you can check that with the rfkill command:

sudo rfkill list

If the bluetooth device is blocked (soft or hard blocked), unblock it with the rfkill command again:

sudo rfkill unblock bluetooth

Bring up the bluetooth device with hciconfig command and start scanning, make sure the target device's bluetooth is on and It's discoverable:

sudo hciconfig hci0 up
hcitool scan

Wait few moment to complete the hcitool scan or hcitool lescan, the results will be something like bellow:

图片 1

图片 2

Here 00:1A:7D:DA:71:0A is the bluetooth MAC address and SHEN-PC is the name of the bluetooth device, i.e. an PC.

note: use hcitool lescan will forever scan ble devices, if use ctrl c stop it, it will show error(ref to LINKS 4 to solve):

hcitool lescan
Set scan parameters failed: Input/output error

First of all, make sure that you have the exact same version of software in every steps, or it may not work.  To install Tensorflow at Ubuntu, you will need to install Ubuntu 16.04.  Run 

星期日, 02. 九月 2018 11:58下午 - beautifulzzzz

Install cudnn v5.1 (you may require for cudnn v6.0 too, see the update below)


Download cudnn @https://developer.nvidia.com/rdp/cudnn-download

My os is Ubuntu 16.04.  amd64.  The debian archives for Ubuntu 16.04 Power8 doesn't apply to my os.  So I have to download the tar file for cuDNN v5.1 Library for Linux.  (This is under the testing.  If it doesn't work.  I have to change os to Ubuntu 14.04 cuz there are debian archives for amd64 version)

Next you need to uncompress and copy cuDNN to the toolkit directory. The toolkit default install location is /usr/local/cuda

tar xvzf cudnn-8.0-linux-x64-v5.1-ga.tgz

sudo cp -P cuda/include/cudnn.h /usr/local/cuda/include

sudo cp -P cuda/lib64/libcudnn /usr/local/cuda/lib64*

*sudo chmod a r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn**

Now you need to update your bash file

nano ~/.bashrc

With the text editor open, scroll to the bottom and put in these lines:

export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"

export CUDA_HOME=/usr/local/cuda

Save and close it.

3.1 setup bluetooth service

Start the bluetooth service and enable automatic startup, assuming you're using systemd as the init daemon:

sudo systemctl start bluetooth.service
sudo systemctl enable bluetooth.service

Installing CUDA Toolkit 8.0 on Ubuntu 16.04

ref: http://www.pradeepadiga.me/blog/2017/03/22/installing-cuda-toolkit-8-0-on-ubuntu-16-04/


One of them is to ensure where GCC is installed or not. We can confirm it by executing the following command.

gcc --version

Since I am using Ubuntu, GCC comes pre-installed and here is the output that I got.

图片 3

build essentials

It is important have thebuild-essential package installed. This is usually pre-installed on Ubuntu, however if it is not you can install it by executing the following command.

sudo apt-get install build-essential

On my laptop it was already installed hence I got the following output.

图片 4

Download CUDA package from NVIDIA website

Navigate to https://developer.nvidia.com/cuda-downloads and download the appropriate package.

图片 5

Once the page is successfully downloaded, we need to install the package. First navigate to the folder where the package is located. In my case it is under ~/Downloads/CUDA$ folder. Then issue the following command which installs the package.

sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb

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Then update the package list from the repositories using the below command.

sudo apt-get update

Then install CUDA by executing the following command.

sudo apt-get install cuda

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After a couple of minutes the installation would succeed and you should a screen similar to the following.

图片 8

One of the important post installation steps is to update the PATH variable to include the CUDA binaries folder. To update it, we need to edit the file /etc/environment. I use the nano text editor in this post, so the command would be

sudo nano /etc/environment

Once nano is open edit the PATH variable to include /usr/local/cuda-8.0/bin folder. After editing the file screen would look like this.

图片 9

After editing this line press Ctrl X to exit the editor and press Y when prompted whether you want to save it.

This method of editing the PATH variable usually requires a reboot to take effect. However executing the below command would update the PATH variable immediately.

source /etc/environment

Now we are ready to validate the CUDA installation. Just execute the following command in the terminal.

nvcc --version

If the installation was successful, we should see the CUDA compiler version as seen in this screenshot.

图片 10

Alternatively you can also execute the following command in the terminal. This gives more detailed information about the drivers.


图片 11

We are now ready to enjoy the goodness of CUDA and can continue with the installation of TensorFlow. Stay tuned for the installation instructions of TensorFlow.

2. Download And Install

I follow the blog (Installing Bluez 5.44 onto Raspbian?) to install bluez-5.50.

Download the most recent version from the official page:.

For example, at the time of writing it was 5.50, so I used(on my pi):

wget http://www.kernel.org/pub/linux/bluetooth/bluez-5.50.tar.xz

Then I extracted it and built it:

tar -xf bluez-5.50.tar.xz
cd bluez-5.50

Read the README! It lists the dependencies and the configure switches:

Install the dependencies first: (glib, dbus, libdbus, udev, etc.)

sudo apt install libdbus-1-dev libudev-dev libical-dev libreadline-dev

note: If you do not install the libdbus-1-dev, you will later get this strange error:

configure: error: D-Bus >= 1.6 is required

once you've installed dependencies, you can configure switches:

./configure --prefix=/usr --mandir=/usr/share/man --sysconfdir=/etc --localstatedir=/var  --enable-experimental

then do:

sudo make install

It takes maybe 10 minutes to compile. After installing, you should find bluetoothd in /usr/libexec/bluetooth. You should also see bluetoothd in /usr/lib/bluetooth.

Go to each of these directories and type

./bluetoothd --version

You'll note that the one in libexec is new and the one in lib is old.

In order to make sure that d-bus is talking to you new BlueZ 5.50 and not your old BlueZ 5.43, you need to tell systemd to use the new bluetooth daemon:

sudo vim /lib/systemd/system/bluetooth.service

Make sure the exec.start line points to your new daemon in /usr/libexec/bluetooth.

For me, that wasn't enough. No matter what, upon restart I always got bluetoothd 5.43... So I just created a symlink from the old one to the new.

First rename the old file:

sudo mv /usr/lib/bluetooth/bluetoothd /usr/lib/bluetooth/bluetoothd-543.orig

Create the symlink:

sudo ln -s /usr/libexec/bluetooth/bluetoothd /usr/lib/bluetooth/bluetoothd
sudo systemctl daemon-reload

That should do it.

图片 12


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3. How to use the bluez and hcitool


TO "1", install cuda and cudnn in the correct version as above says. Use nvcc -V and nvidia-smi command to examine the driver and installation.

Run sample code to test whether cuda works correctly


-> Go to the dir that installed cuda (mine is/usr/local/cuda/samples)

-> runmake, it might take few minutes to make the files.

-> rundeviceQueryandbandwidthTest. They are at:



if both passes, then cuda should work.

TO "2", run the testing code provided from the official site of Tensorflow:


write down the code below and run through terminal:

import tensorflow as tf

# Creates a graph.

a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')

b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')

c = tf.matmul(a, b)

# Creates a session with log_device_placement set to True.

sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

# Runs the op.


Save as a .py file and run python ./${file_full_path}

You should see the following output:

Device mapping:

/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla K40c, pci bus

id: 0000:05:00.0

b: /job:localhost/replica:0/task:0/device:GPU:0

a: /job:localhost/replica:0/task:0/device:GPU:0

MatMul: /job:localhost/replica:0/task:0/device:GPU:0

[[ 22.  28.]

[ 49.  64.]]

If it shows device mapping: no known devices, then unintall your tensorflow and install tensorflow-gpu.  See this:


Package tensorflow is likely a CPU version.  To use gpu to run CUDA, you need to install tensorflow-gpu.  However, since installing tensorflow-gpu will reinstall tensorflow automatically, it'd be wise to manually uninstall all the tensorflow-related package, then install tensorflow-gpu.

In my case:

pip uninstall tensorflow

is not enough. Because when reinstall with:

pip install tensorflow-gpu

It's still reinstall tensorflow with cpu not gpu. So, before install tensorfow-pgu, I tried to remove all related tensor folders in site-packages uninstall protobuf, and it works!

For conclusion:

pip uninstall tensorflow

Remove all tensor folders in ~/Python35/Lib/site-packages

pip uninstall protobuf

pip install tensorflow-gpu

If the commandpip uninstall tensorflow results in some kind of error and failed in uninstall, then we can remove the package from pycharm.  Go File-> settings-> project interpreter->minus sign to uninstall.  Also check the fold from terminal whether they are still there. /usr/local/lib/python2.7/dist-packages/ and /usr/local/lib/python2.7/site-packages/.  List of folders need to remove:

*tensorflow,tensorboard,anything starts from **tensor, and protobuf*.

Open NVIDIA X Server Settings.  At GPU-0 (your gpu name) tab, monitor your GPU Utilization.  If you see it rapidly goes up in a very short period of time, it means your tensorflow runs on gpu correctly.

If you see error message occurs while you import tensorflow like:

ImportError: libcudnn.so.5: cannot open shared object file: No such file or directory


ImportError: libcudnn.so.6: cannot open shared object file: No such file or directory

it means it couldn't find the corresponding version of cudnn library.  It might trying to load cudnn v6.  If so,Download cuDNN v6.0 (April 27, 2017), for CUDA 8.0, untar and place the files to the corresponding right places as above (since cudnn v5.1 and v6.0 can be exist at the same time, you don't have to delete the old copied files from v5.1) and run the tensorflow code again.  It should be fixed.

TO "3",  it is possible that everything works when you run through terminal, but it suddenly breaks when you run through pycharm although you have already added LD_LIBRARY_PATH and CUDA_HOME to ~/bashrc file.  It is because pycharm has its own environment variable Running in pycharm, we need to set CUDA_HOME and LD_LIBRARY_PATH again probably for each individual project.

To do that, go to the Run menu->edit configurations then choose your project. Then you click on the Environment Variables and add an entry for CUDA_HOME and LD_LIBRARY_PATH.


Up to now, your configuration of cuda and tensorflow should be done.  Make sure you have enabled to use gpu in your code, then everything should work.


  • 1.Bluetooth on Modern Linux by Szymon Janc
  • 2.dbus-python tutorial
  • 3.Linux bluetooth setup with bluez and hcitool
  • 4.hcitool lescan shows I/O error

图片 14


Install pycharm

ref: https://itsfoss.com/install-pycharm-ubuntu/

Open a terminal and use the following commands:

sudo add-apt-repository ppa:mystic-mirage/pycharmsudo apt-get update

To install the community edition of PyCharm, use the command below. It will download data of around 120 MB.

sudo apt-get install pycharm-community

To install the professional edition of PyCharm, use the command below. It will download data of around 160 MB.

sudo apt-get install pycharm

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