In previous posts, we have built simple neural networks by hand. Fortunately, there are libraries to build network architectures and calculate gradients automatically. TensorFlow is one of the most famous one. I will explain how to install this Python library on Ubuntu 18.04.
Why using Docker?
Neural network calculations are primarily based on matrix operations, which are most efficiently performed on GPUs. In order to use your computer’s GPU with TensorFlow, it is necessary to install 2 libraries on your machine:
- CUDA (Compute Unified Device Architecture): a parallel computing platform developed by NVIDIA for general computing on GPUs
- cuDNN (CUDA Deep Neural Network): a GPU-accelerated library of primitives used to accelerate deep learning frameworks such as TensorFlow or Pytorch.
In order to be able to use these libraries, you must first ensure that your computer is equipped with a CUDA-enabled GPU. The list of these GPU can be found here. You must then install the latest NVIDIA driver corresponding to your GPU.
As you can see, there is a lot of prerequisites before being able to install TensorFlow. You can follow the official procedure to install CUDA from the NVIDIA website here. However, I learnt the hard way that it is easy to mess up your computer and your graphics card while installing all these libraries and drivers. That’s why, I would highly recommend installing TensorFlow inside a Docker container.
Docker is essentially a self-contained OS with all the dependencies necessary for a smooth installation.
First of all, check the instructions on the official TensorFlow page.
1.Install the latest NVIDIA drivers.
$ sudo ubuntu-drivers devices $ sudo ubuntu-drivers autoinstall $ sudo reboot
2. Install Docker
Please follow these instructions.
Check that you have installed Docker 19.03 or higher.
$ docker version
Add the current user to the docker group and reboot.
$ sudo usermod -a -G docker $USER $ sudo reboot
$ docker run hello-world
3. Install the NVIDIA Container toolkit
Please follow these instructions.
$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID) $ curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - $ curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list $ sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit $ sudo systemctl restart docker
Test that everything was installed correctly.
$ docker run --gpus all --rm nvidia/cuda nvidia-smi
You should see some information about your GPU and the CUDA version installed.
4. Download the TensorFlow Docker images with GPU support
$ docker pull tensorflow/tensorflow:latest-gpu-py3 $ docker pull tensorflow/tensorflow:latest-gpu-py3-jupyter
5. Test that the image is working properly
$ docker run --gpus all -it --rm tensorflow/tensorflow:latest-gpu-py3 python -c "import tensorflow as tf; print(tf.version); print(tf.test.is_gpu_available()); print(tf.test.is_built_with_cuda())"
This should return the TensorFlow version and whether GPU support is available.
Please have a look at my Docker cheat sheet for my information about Docker.
6. Run a TensorFlow container
Create a new container from the TensorFlow image
$ docker run -it --rm tensorflow/tensorflow:latest-gpu-py3
You should be logged-in in the new container. You can explore it using ls, cd, etc… You can exit using $ exit. Now let’s see a more practical example. First, let’s create a directory to exchange files between your machine and the container:
$ mkdir ~/docker_ws
$ docker run -u $(id -u):$(id -g) --gpus all -it --rm --name my_tf_container -v ~/docker_ws:/notebooks -p 8888:8888 -p 6006:6006 tensorflow/tensorflow:latest-gpu-py3-jupyter
Let’s explain the different options.
-u $(id -u):$(id -g) # assign a user and a group ID --gpus all # allow GPU support -it # run an interactive container inside a terminal -rm # automatically clean up the container and remove the file system after closing the container --name my_tf_container # give it a friendly name -v ~/docker_ws:/notebooks # share a directory between the host and the container -p 8888:8888 # define port 8888 to connect to the container -p 6006:6006 # forward port 6006 for Tensorboard
Once the container is running, your should see a URL to copy and paste in your browser that looks like “http://127.0.0.1:8888/?token=xxxxxxxxxx”. You should then see a list of TensorFlow tutorials, as shown below.
Finally, you can use $ docker exec to run a command inside a running docker container. In another terminal, run this command:
$ docker exec -it my_tf_container tensorboard --logdir tf_logs/
You should be able to access the TensorBoard page via this URL “http://localhost:6006/” (see also https://www.youtube.com/watch?v=W3bk2pojLoU)
Play around with the tutorials and enjoy!