Category: GPU

Deeplab v3 Test

  1. Create Python Env
    $ mkdir ~/tf_ros && cd ~/tf_ros
    $ virtualenv --system-site-packages -p python2 tensor_ros_deeplab
  2. Activate Environment
    source tensor_ros_deeplab/bin/activate
  3. Install tensorflow GPU
    pip install --upgrade tensorflow_gpu-1.12.0-cp27-none-linux_x86_64.whl
  4. Move to the Deeplab dir
    $ git clone https://github.com/tensorflow/models
  5. Clone Tensorflow's Models repo containing Deeplab
    ~/tf_ros/models/research/deeplab
  6. Download a pretrained model:
    $ wget https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md
  7. Use sample script for a single image:
    https://github.com/tensorflow/models/blob/master/research/deeplab/deeplab_demo.ipynb
  8. Change the MODEL var to the path of the previously downloaded model:
    MODEL = DeepLabModel("~/tf_ros/models/research/deeplab/deeplabv3_mnv2_dm05_pascal_trainaug_2018_10_01.tar.gz")
  9. To use a local image instead of a URL change the run_visualization function to:
def run_visualization(image_path):
  """Inferences DeepLab model and visualizes result."""
  try:
    original_im = Image.open(image_path)
  except IOError:
    print('Cannot read image. Please check path: ' + image_path)
    return

  print('running deeplab on image %s...' % image_path)
  resized_im, seg_map = MODEL.run(original_im)

  vis_segmentation(resized_im, seg_map)
  1. Finally save and run the script:
    ptyhon script_name.py

TensorFlow and ROS Kinetic with Python 2.7

TensorFlow and ROS Kinetic with Python 2.7

  1. Create a working directory
    $ mkdir ~/tf_ros && cd ~/tf_ros

  2. Download and Install an NVIDIA Driver >= 384.x (https://www.nvidia.co.jp/Download/index.aspx) We recommend RUN files, please check our other post on this topic.

  3. Download and Install CUDA 9.0 (https://developer.nvidia.com/cuda-90-download-archive)

  4. Download and Install CUDNN for CUDA 9.0 (https://developer.nvidia.com/rdp/cudnn-download; https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux)

  5. Download TensorFlow for Python 2.7
    $ wget https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.10.1-cp27-none-linux_x86_64.whl

  6. Install Python VirtualEnv
    $ sudo apt-get install python-virtualenv

  7. Create Python 2.7 virtual environment.
    $ virtualenv --system-site-packages -p python2 p2tf_venv
    (p2tf_venv can be any name)

  8. Activate environment
    $ source p2tf_venv/bin/activate

  9. Install TensorFlow
    (p2_venv) $ pip install --upgrade tensorflow_gpu-1.10.1-cp27-none-linux_x86_64.whl

  10. Make sure you have in LD_LIBARY_PATH, the cuda 9 libraries and binaries.

$ export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH && export PATH=/usr/local/cuda/bin:$PATH

  1. Check the ROS libraries are in the LD_LIBRARY_PATH as well.
    $ echo $LD_LIBRAY_PATH.
    It should contain /opt/ros/kinetic/lib:/opt/ros/kinetic/lib/x86_64-linux-gnu

  2. Check that Python ROS libraries are also included in the $PYTHONPATH.
    $ echo $PYTHONPATH.
    It should contain /opt/ros/kinetic/lib/python2.7/dist-packages.

  3. Test your installation is correct.
    (p2_venv) $ python -c "import tensorflow as tf; print(tf.__version__)"
    It should print the TensorFlow version install, such as 1.10.1.

  4. Ready to go!

How to setup Nvidia Drivers and CUDA in Ubuntu

Disabling Nouveau, if required (login loop or low res mode), otherwise skip to next section

  1. Confirm nouveau is loaded

lsmod | grep -i nouveau

You'll see the text nouveau in the 4th column, if loaded

video XXXX Y nouveau

  1. If nouveau is loaded then blacklist it. Create the file blacklist-nouveau.conf in /etc/modprobe.d/

sudo nano /etc/modprobe.d/blacklist-nouveau.conf

And add the following text

blacklist nouveau
options nouveau modeset=0

  1. Execute sudo update-initramfs -u

  2. Restart

NVIDIA Driver Setup

  1. Download the RUN file from NVidia's website
    https://www.geforce.com/drivers
    You'll have a file named similarly to NVIDIA-Linux-x86_64-XXX.YY.run

  2. Assign execution permissions chmod +x NVIDIA-Linux-x86_64-XXX.YY.run

  3. Move to a virtual console pressing Ctrl+Alt+F1 and login

  4. Terminate the X Server executing sudo service lightdm stop

  5. Run the installer sudo ./NVIDIA-Linux-x86_64-XXX.YY.run
    (If you are running on a laptop run instead sudo ./NVIDIA-Linux-x86_64-XXX.YY.run --no-opengl-files)

  6. Follow the instruction from the wizard. At the end, do not allow the wizard to modify the X configuration.

  7. Once back in the console, execute sudo service lightdm start. The GUI should be displayed. Login.

  8. To confirm everything is set run in a terminal nvidia-smi

CUDA Setup

  1. Download the CUDA Installation RUN File from https://developer.nvidia.com/cuda-downloads
    You'll have a file named similarly to cuda_X.0.YY.Z_linux.run

  2. Assign execution permissions chmod +x cuda_X.0.YY.Z_linux.run

  3. Run the installer sudo ./cuda_X.0.YY.Z_linux.run

  4. Follow the instructions on screen. DO NOT install the NVIDIA Driver included. Install the CUDA Samples on your home directory.

  5. Once finished, to confirm everything is ok. Go to your home directory and execute cd NVIDIA_CUDA-X.Y_Samples/1_Utilities/deviceQuery Match X, Y to your CUDA version. i.e. CUDA 9.0 cd NVIDIA_CUDA-9.0_Samples/1_Utilities/deviceQuery

  6. Compile the sample running make

  7. Run the sample ./deviceQuery you should see the details about your GPU(s) and CUDA Setup.