Deep learning setup¶
process_facsdnnfacs
.We’re going to add the deep learning library requirements TensorFlow & Keras to our facsvatar anaconda environment.
Currently tested with Ubuntu 16.04 (not yet on Windows, but instructions provided)
If you didn’t setup your Python environment yet, look here: Default set-up
Make sure your terminal has facsvatar active:
source/conda activate facsvatar # Ubuntu: `source`, Windows `conda`
Dependencies¶
Python 3.4+ (tested 3.6)
TensorFlow 1.7.0
- CUDA Toolkit v9.0 # GPU training
- cuDNN v7.1.3 # GPU training
Keras (TensorFlow backend)
Anaconda install all - untested¶
Make sure your terminal has facsvatar active.
source/conda activate facsvatar # Ubuntu: `source`, Windows `conda`
# Keras
conda install -c anaconda keras-gpu
TensorFlow - Manual¶
Instructions are based on the Anaconda instructions found here: https://www.tensorflow.org/install/ , so look there for the most recent instructions.
You can skip the GPU sections if you want to run it on a CPU ((much) slower). Untested for now on Windows.
GPU¶
CUDA Toolkit v9.0¶
- Official instructions (Ubuntu): https://docs.nvidia.com/cuda/cuda-installation-guide-linux/#axzz4VZnqTJ2A
- Official instructions (Windows): https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/
Short instructions:
- Go to: https://developer.nvidia.com/cuda-90-download-archive
- Select .deb / .exe for your system
- Download file (and Ubuntu: open terminal at download location)
- Follow instructions
- Ubuntu: Do step 1 for all patches before step 2 (sudo dpkg -i cuda-xxx-update-xxx.deb)
- Ubuntu: If this fails, install ‘GDebi Package Installer’ from Ubuntu Software and open ‘.deb’ with that.
cuDNN v7.1.3¶
Go to: https://developer.nvidia.com/cudnn –> DOWNLOAD cuDNN –> Join / Login
Download cuDNN v7.1.3 (April 17, 2018) (or newer?), for CUDA 9.0
- Ubuntu: cuDNN v7.1.3 Runtime Library for Ubuntu16.04 (Deb)
- Ubuntu: cuDNN v7.1.3 Developer Library for Ubuntu16.04 (Deb)
- Windows: cuDNN v7.1.3 Library for Windows 7/10
Install by running in terminal: - Ubuntu: sudo dpkg -i libcudnn7_7.1.3.16-1+cuda9.0_amd64.deb - Ubuntu: sudo dpkg -i libcudnn7-dev_7.1.3.16-1+cuda9.0_amd64.deb - Windows:
Setup your environment variable to link to cuDNN
Ubuntu (in terminal): echo ‘export PATH=/usr/local/cuda-9.0/bin${PATH:+:${PATH}}’ >> ~/.bashrc
- Manually: Add above line to .bashrc (in your home directory, ctrl+h to show hidden files)
Windows: add the directory where you installed the cuDNN DLL to your %PATH% environment variable.
NVIDIA CUDA Profile Tools Interface (Ubuntu only) - untested¶
https://github.com/tensorflow/tensorflow/issues/16214
- Locate cuda-command-line-tools: sudo apt-cache search cuda-command-line-tools-9-0
- Install: sudo apt install cuda-command-line-tools-9-0
- Path to environment variable: echo ‘export LD_LIBRARY_PATH=${LD_LIBRARY_PATH:+${LD_LIBRARY_PATH}:}/usr/local/cuda/extras/CUPTI/lib64’ >> ~/.bashrc
Install TensorFlow with Anaconda (GPU/CPU)¶
If you didn’t setup your Python environment yet, look here: Default set-up
Make sure your terminal has facsvatar active:
source/conda activate facsvatar # Ubuntu: `source`, Windows `conda`
# GPU - Python 3.6
pip install --ignore-installed --upgrade \
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.7.0-cp36-cp36m-linux_x86_64.whl
# CPU - Python 3.6
pip install --ignore-installed --upgrade \
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.7.0-cp36-cp36m-linux_x86_64.whl
# test installation
python
>>> import tensorflow as tf # no error
>>> tf.__version__ # 1.7.0
>>> ctrl+z / ctrl+Break # leave Python; z: Ubuntu, Break: Windows
Keras - Manual¶
Official instructions: https://keras.io/
Make sure your terminal has facsvatar active.
source/conda activate facsvatar # Ubuntu: `source`, Windows `conda`
# Keras
pip install keras
# Only do the following commands if Keras doesn't use GPU
pip uninstall keras # Remove only Keras, but keep dependencies
pip install --upgrade --no-deps keras # and install it again without dependencies
Test Keras GPU¶
cd jupyter_notebooks # FACSvatar folder containing Jupyter notebooks
jupyter notebook # starts jupyter notebook and opens browser page
- Click Keras_GPU_test.ipynb
- Check right-top shows “py3 facsvatar” (our python env)
- Kernel –> Restart & Run All
- If you can find a device_type: “GPU”, Keras should be using GPU
- Congratulations, Deep Learning setup complete!