How to setup TensorFlow on Ubuntu

How to setup TensorFlow on Ubuntu

How to setup TensorFlow on Ubuntu - This tutorial will help you set up TensorFlow 1.12 on Ubuntu 16.04 with a GPU using Docker and nvidia-docker.

TensorFlow is one of the most popular deep-learning libraries. It was created by Google and was released as an open-source project in 2015. TensorFlow is used for both research and production environments. Installing TensorFlow can be cumbersome. The difficulty varies based on your environment constraints, and more when you’re a data scientist that just wants to build your neural networks.

When using TensorFlow on GPU — setting up requires a few steps. In the following tutorial, we will go over the process required to set up TensorFlow.


You may also like:5 TensorFlow and ML Courses for Programmers


Requirements:

Step 1 — Prepare your environment with Docker and Nvidia-Docker

Docker is a tool designed to make it easier to create, deploy, and run applications by using containers. What exactly is a container? Containers allow data scientists and developers to wrap up an environment with all of the parts it needs — such as libraries and other dependencies — and ship it all out in one package.

To use docker with GPUs and to be able to use TensorFlow in your application, you’ll need to install Docker with Nvidia-Docker. If you already have those installed, move to the next step. Otherwise, you can follow our previous guide to installing nvidia docker.

Prerequisites: Step 2 — Dockerfile

Docker can build images (environments) automatically by reading the instructions from a Dockerfile. A Dockerfile is a text document that contains all the commands a user could call on the command line to assemble an image.

In our case, those commands will describe the installation of Python 3.6, CUDA 9 and CUDNN 7.2.1 — and of course the installation of TensorFlow 1.12 from source.

For this environment, we will use the following Dockerfile

FROM nvidia/cuda:9.0-base-ubuntu16.04

RUN apt-get update && apt-get install -y --no-install-recommends \
        build-essential \
        cuda-command-line-tools-9-0 \
        cuda-cublas-dev-9-0 \
        cuda-cudart-dev-9-0 \
        cuda-cufft-dev-9-0 \
        cuda-curand-dev-9-0 \
        cuda-cusolver-dev-9-0 \
        cuda-cusparse-dev-9-0 \
        curl \
        git \
        libcudnn7=7.2.1.38-1+cuda9.0 \
        libcudnn7-dev=7.2.1.38-1+cuda9.0 \
	libnccl2=2.4.2-1+cuda9.0 \
	libnccl-dev=2.4.2-1+cuda9.0 \
        libcurl3-dev \
        libfreetype6-dev \
        libhdf5-serial-dev \
        libpng12-dev \
        libzmq3-dev \
        pkg-config \
        rsync \
        software-properties-common \
        unzip \
        zip \
        zlib1g-dev \
        wget \
        && \
    rm -rf /var/lib/apt/lists/* && \
    find /usr/local/cuda-9.0/lib64/ -type f -name 'lib*_static.a' -not -name 'libcudart_static.a' -delete && \
    rm /usr/lib/x86_64-linux-gnu/libcudnn_static_v7.a

# install python 3.6 and pip

RUN apt-get update
RUN apt-get install -y software-properties-common vim
RUN add-apt-repository ppa:jonathonf/python-3.6
RUN apt-get update

RUN apt-get install -y build-essential python3.6 python3.6-dev python3-pip python3.6-venv
RUN apt-get install -y git

RUN apt-get update && \
        apt-get install nvinfer-runtime-trt-repo-ubuntu1604-4.0.1-ga-cuda9.0 && \
        apt-get update && \
        apt-get install libnvinfer4=4.1.2-1+cuda9.0 && \
        apt-get install libnvinfer-dev=4.1.2-1+cuda9.0

RUN python3.6 -m pip install pip --upgrade
RUN python3.6 -m pip install wheel 
RUN python3.6 -m pip install six numpy wheel mock
RUN python3.6 -m pip install keras_applications
RUN python3.6 -m pip install keras_preprocessing

RUN ln -s /usr/bin/python3.6 /usr/bin/python


# Set up Bazel.

# Running bazel inside a `docker build` command causes trouble, cf:
#   https://github.com/bazelbuild/bazel/issues/134
# The easiest solution is to set up a bazelrc file forcing --batch.
RUN echo "startup --batch" >>/etc/bazel.bazelrc
# Similarly, we need to workaround sandboxing issues:
#   https://github.com/bazelbuild/bazel/issues/418
RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \
    >>/etc/bazel.bazelrc
# Install the most recent bazel release.
ENV BAZEL_VERSION 0.15.0
WORKDIR /
RUN mkdir /bazel && \
    cd /bazel && \
    curl -H "User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36" -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \
    curl -H "User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36" -fSsL -o /bazel/LICENSE.txt https://raw.githubusercontent.com/bazelbuild/bazel/master/LICENSE && \
    chmod +x bazel-*.sh && \
    ./bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \
    cd / && \
    rm -f /bazel/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh




# Download and build TensorFlow.
WORKDIR /tensorflow
RUN git clone --branch=r1.12 --depth=1 https://github.com/tensorflow/tensorflow.git .

# Configure the build for our CUDA configuration.
ENV CI_BUILD_PYTHON python3.6
ENV LD_LIBRARY_PATH /usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
ENV TF_NEED_CUDA 1
ENV TF_NEED_TENSORRT 1
ENV TF_CUDA_COMPUTE_CAPABILITIES=3.5,5.2,6.0,6.1,7.0
ENV TF_CUDA_VERSION=9.0
ENV TF_CUDNN_VERSION=7

RUN ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1 && \
    LD_LIBRARY_PATH=/usr/local/cuda/lib64/stubs:${LD_LIBRARY_PATH} \
    tensorflow/tools/ci_build/builds/configured GPU \
    bazel build -c opt --copt=-mavx --config=cuda \
	--cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" \
        tensorflow/tools/pip_package:build_pip_package && \
    rm /usr/local/cuda/lib64/stubs/libcuda.so.1 && \
    bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/pip && \
    pip --no-cache-dir install --upgrade /tmp/pip/tensorflow-*.whl && \
    rm -rf /tmp/pip && \
    rm -rf /root/.cache
# Clean up pip wheel and Bazel cache when done.

WORKDIR /root

# TensorBoard
EXPOSE 6006

Step 3 — Running Dockerfile

To build the image from the Dockerfile, simply run the docker build command. Keep in mind that this build process might take a few hours to complete. We recommend using nohup utility so that if your terminal hangs — it will still run.

$ docker build -t deeplearning -f Dockerfile

This should output the setup process and should end with something similar to:

>> Successfully built deeplearning (= the image ID)

Your image is ready to use. To start the environment, simply type in the below command. But, don’t forget to replace your image id:

$ docker run --runtime=nvidia -it deeplearning /bin/bashStep 4 — Validating TensorFlow & start building!

Validate that TensorFlow is indeed running in your Dockerfile

$ python
import tensorflow as tf
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
2019-02-23 07:34:14.592926: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports 
instructions that this TensorFlow binary was not compiled to use: AVX2 FMA2019-02-23 07:34:17.452780: I 
tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA
node read from SysFS had negative value (-1), but there must be at leastone NUMA node, so returning NUMA node zero
2019-02-23 07:34:17.453267: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with 
properties: 
name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235pciBusID: 0000:00:1e.0
totalMemory: 11.17GiB freeMemory: 11.10GiB
2019-02-23 07:34:17.453306: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu
devices: 0
2019-02-23 07:34:17.772969: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect
StreamExecutor with strength 1 edge matrix:
2019-02-23 07:34:17.773032: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0
2019-02-23 07:34:17.773054: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N
2019-02-23 07:34:17.773403: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow
device (/job:localhost/replica:0/task:0/device:GPU:0 with 10757 MB memory)
-> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:1e.0,
compute capability: 3.7)
Device mapping:
/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla K80,
pci bus id: 0000:00:1e.0, compute capability: 3.7
2019-02-23 07:34:17.774289: I
tensorflow/core/common_runtime/direct_session.cc:307] Device mapping:
/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device
/job:localhost/replica:0/task:0/device:XLA_GPU:0 -> device: XLA_GPU device
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla K80, pci bus id: 0000:0

Congrats! Your new TensorFlow environment is set up and ready to start training, testing and deploying your deep learning models!

Conclusion

Tensorflow has truly disrupted the machine learning world by offering a solution to build production-ready models at scale. But Tensorflow is not always the most user-friendly. It can be difficult to smoothly incorporate into your machine learning pipeline. cnvrg.io data science platform leverages Tensorflow and other open-source tools so that data scientists can focus on the magic — the algorithms. You can find more tutorials on how to easily leverage open-source tools like TensorflowHow to set up Kubernetes for your machine learning workflows, and How to run Spark on Kubernetes. Finding simple ways to integrate these useful tools will get your models closer to production.

Further Reading

How To Set Up Django with Postgres, Nginx, and Gunicorn on Ubuntu 16.04

How To Install Python 3 and Set Up a Programming Environment on Ubuntu 18.04

How To Set Up Jupyter Notebook with Python 3 on Ubuntu 18.04

Originally published by*** *yochze **at towardsdatascience.com


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WordPress in Docker. Part 1: Dockerization

WordPress in Docker. Part 1: Dockerization

This entry-level guide will tell you why and how to Dockerize your WordPress projects.

This entry-level guide will tell you why and how to Dockerize your WordPress projects.

How to Set up a TensorFlow GPU Docker

How to Set up a TensorFlow GPU Docker

Why Docker is the best platform to use Tensorflow with a GPU. Docker is the best platform to easily install Tensorflow with a GPU. This tutorial aims demonstrate this and test it on a real-time object recognition application.

Docker Image for Tensorflow with GPU

Docker is a tool which allows us to pull predefined images. The image we will pull contains TensorFlow and nvidia tools as well as OpenCV. The idea is to package all the necessary tools for image processing. With that, we want to be able to run anyimage processing algorithm within minutes.

First of all, we need to install Docker.

> curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
> sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"
> sudo apt-get update
> apt-cache policy docker-ce
> sudo apt-get install -y docker-ce
> sudo systemctl status docker

After that, we will need to install nvidia-docker if we want to use GPU:

> wget https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb
> sudo dpkg -i nvidia-docker*.deb

At some point, this installation may fail if nvidia-modprobe is not installed, you can try to run (GPU only):

> sudo apt-get install nvidia-modprobe
> sudo nvidia-docker-plugin &

Eventually, you can run this command to test your installation. Hopefully, you will get the following output (GPU only):

> sudo nvidia-docker run --rm nvidia/cuda nvidia-smi

![](https://images.ctfassets.net/be04ylp8y0qc/5gFseyqmEDdawhGL9EbFr2/67eb9bd8f500d3956877ec06e68475fa/1_lZl-
-DrLap91AzyKvG9g1w.png?fm=webp "")

Fetch Image and Launch Jupyter

You probably are familiar with Jupyter Notebook. Jupyter Notebook documents are both human-readable documents containing the analysis description and the results(figures, tables, etc..) as well as executable documents which can be run to perform data analysis. Jupyter Notebook can also run distributed algorithms with GPU.

To run a jupyter notebook with TensorFlow powered by GPU and OpenCv, launch:

> sudo nvidia-docker run --rm --name tf1 -p 8888:8888 -p 6006:6006 redaboumahdi/image_processing:gpu jupyter notebook --allow-root

If you just want to run a jupyter notebook with TensorFlow powered by CPU and OpenCV, you can run the following command:

> sudo docker run --rm --name tf1 -p 8888:8888 -p 6006:6006 redaboumahdi/image_processing:cpu jupyter notebook --allow-root

You will get the following result out of your terminal. Then you can navigate to your localhost and use the port 8888, for me, the link looks like this: http://localhost:8888/

You will need to paste your token to identify and access your jupyter notebooks: 3299304f3cdd149fe0d68ce0a9cb204bfb80c7d4edc42687

And eventually, you will get the following result. You can therefore test your installation by running the jupyter notebooks.

The first link is a hello TensorFlow notebook to get more familiar with this tool. ]TensorFlow is an open-source software library for dataflow programming across a range of tasks. It is principally used to build deep neural networks. The third link gives an example of using TensorFlow to build a simple fully connected neural network. You can find here a TensorFlow implementation of a convolutionnal neural network. I highly recommand using GPU to train CNN / RNN / LSTM networks.

Real-Time Object Recognition

Now it is time to test our configuration and spend some time with ourmachine learning algorithms. The following code helps us track objects over frames with our webcam. It is a sample of code taken from the internet, you can find the github repository at the end of the article.

First of all, we need to open the access to the xserver to our docker image. There are different ways of doing so. The first one opens an access to your xserver to anyone. Other methods are described in the links at the end of the article.

> xhost +local:root

Then we will bash to our Docker image using this command:

> sudo docker run -p 8888:8888 --device /dev/video0 --env="DISPLAY" 
--volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" -it image_processing bash

We will need to clone the github repository, which is a real-time object detector:

> git clone https://github.com/datitran/object_detector_app.git && cd object_detector_app/

Finally, you can launch the python code:

> python object_detection_app.py

The code that we are using uses OpenCV. It is know as one of the most used libraries for image processing and available for C++ as well as Python.

You should see the following output, OpenCV will open your webcam and render a video. OpenCV will also find any object in the frame and print the label of the predicted object.

Conclusion

I showed how one can use Docker to get your computer ready for image processing. This image contains OpenCV and TensorFlow with either GPU or CPU. We tested our installation through a real-time object detector. I hope it convinced you that most of what you need to process images is contained in this Docker image. Thank you for following my tutorial. Please don’t hesitate to send me any feedback!

Useful Links

Recommended Reading

How to Use TensorFlow Using Java/JavaScript

Torch vs Theano vs TensorFlow vs Keras

TensorFlow vs NumPy vs Pure Python: Performance Comparison

List all containers in Docker(Docker command)

List all containers in Docker(Docker command)

We can get a list of all containers in docker using `docker container list` or `docker ps` commands.

We can get a list of all containers in docker using docker container list or docker ps commands.

List Docker Containers

To list down docker containers we can use below two commands

  • docker container list
  • docker ps

docker container ls command introduced in docker 1.13 version. In older versions we have to use docker ps command.

List all Containers in docker, using docker ls command

The below command returns a list of all containers in docker.

docker container list -all

or

docker container ls -all

List all containers in docker, using docker ps command

In older version of docker we can use docker ps command to list all containers in docker.

$ docker ps -all

or

$ docker ps -a

List all Running docker containers

The default docker container ls command shows all running docker containers.

$ docker container list

or

$ docker container ls

or

To get list of all running docker containers use the below command

$ docker ps

List all stopped docker containers command

To get list of all stopped containers in docker use the below commands

$ docker container list -f "status=exited"

or

$ docker container ls -f "status=exited"

or you can use docker ps command

$ docker ps -f "status=exited"

List all latest created docker containers

To list out all latest created containers in docker use the below command.

$ docker container list --latest

Show n last created docker containers

To display n last created containers in docker use the below command.

$ docker container list --last=n