Installing Pycharm, Python Tensorflow, Cuda and cudnn in Ubu

LINKS

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3. How to use the bluez and hcitool

Remove PyCharm

To uninstall PyCharm installed by the above described method, use the command below to uninstall the community edition of PyCharm:

sudo apt-get remove pycharm-community

To remove the professional version, use the command below:

sudo apt-get remove pycharm

Afterward, use the command below to remove the PPA from the sources list:

sudo add-apt-repository --remove ppa:mystic-mirage/pycharm

That’s all you need to do. I hope this tutorial helped you toinstall PyCharm in Ubuntu Linux. Any questions or suggestions are always welcomed.


Install Python2.7

ref: https://askubuntu.com/questions/101591/how-do-i-install-the-latest-python-2-7-x-or-3-x-on-ubuntu

sudo add-apt-repository ppa:fkrull/deadsnakes

sudo apt-get update

sudo apt-get install python2.7


3.3 bluetooth service discovery

Now we have the bluetooth MAC address of the target device, use the sdptool command to know which services (like DUN, Handsfree audio) are available on that target device.

sdptool browse 28:ED:6A:A0:26:B7

You can also use the interactive bluetoothctl tool for this purpose.

If the target device is present, you can ping it with l2ping command, requires root privilege:

➜  bluez-5.50  sudo l2ping 94:87:E0:B3:AC:6F
Ping: 94:87:E0:B3:AC:6F from B8:27:EB:8E:CC:51 (data size 44) ...
44 bytes from 94:87:E0:B3:AC:6F id 0 time 53.94ms
44 bytes from 94:87:E0:B3:AC:6F id 1 time 77.12ms
44 bytes from 94:87:E0:B3:AC:6F id 2 time 38.63ms
44 bytes from 94:87:E0:B3:AC:6F id 3 time 46.13ms
44 bytes from 94:87:E0:B3:AC:6F id 4 time 59.96ms
5 sent, 5 received, 0% loss

So, bluetooth service discovery is useful to determine the type of the device, like if it's a bluetooth mp3 player or it's a keyboard.

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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

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

Solutions:

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

ref:http://xcat-docs.readthedocs.io/en/stable/advanced/gpu/nvidia/verify_cuda_install.html

-> 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:

./bin/ppc64le/linux/release/deviceQuery

./bin/ppc64le/linux/release/bandwidthTest

if both passes, then cuda should work.

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

ref:https://www.tensorflow.org/tutorials/using_gpu

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.

print(sess.run(c))

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:

ref:https://stackoverflow.com/questions/42326748/tensorflow-on-gpu-no-known-devices-despite-cudas-devicequery-returning-a-pas

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

or

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.

ref:https://stackoverflow.com/questions/43691706/pycharm-tensorflow-importerror-but-works-fine-with-terminal

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.

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:

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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

The version compatibility across the OS and these packages is a nightmare for every new person who tries to use Tensorflow.  In here, I record the successful procedure to install everything listed in the title of this note.

1. Introduction

Bluez is the default Bluetooth protocol stack on Linux. It should be present and installed on your Linux distribution. If not, building and installing from source is not too difficult:

  • Download the latest stable source release of Bluez from here. Unzip the compressed file you downloaded.
  • Install the headers and libraries required for Bluez compilation:

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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/

GCC

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.

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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.

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Download CUDA package from NVIDIA website

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

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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.

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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.

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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.

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

nvidia-smi

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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.


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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 

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