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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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How to Install PHP on Ubuntu 18.04

PHP is one of the most popular programming language currently in the world. PHP has different versions 5.6, 7.0, 7.1 and 7.2. Ubuntu 18.04 ships with default PHP 7.2 support, We can easily install PHP 7.2 on Ubuntu. Ubuntu 18.04 is the current latest version of Ubuntu. This tutorial outlines how to install PHP 7.2 on Ubuntu 18.04.

Install PHP on Ubuntu 18.04

PHP is one of the most popular programming language currently in the world. PHP has different versions 5.6, 7.0, 7.1 and 7.2. Ubuntu 18.04 ships with default PHP 7.2 support, We can easily install PHP 7.2 on Ubuntu. Ubuntu 18.04 is the current latest version of Ubuntu. This tutorial outlines how to install PHP 7.2 on Ubuntu 18.04.


Prerequisites

Before you start installing PHP on Ubuntu 18.04. You must have a non-root user account on your server with sudo privileges.


How to Install PHP 7.0 on Ubuntu 18.04

Update the apt package manager index typing following command:

sudo apt update

Run the following command to install PHP on your server. The following command will install php7.2.

sudo apt install php7.0

Check PHP version and confirm the installation by running following command

php -v

Following are some basic PHP extensions needs to be installed on your server

sudo apt install php7.0-curl php7.0-mysql php7.0-common php7.0-cli php7.0-gd php7.0-opcache

How to Install PHP 7.2 on Ubuntu 18.04

Update the apt package manager index typing following command:

sudo apt update

Run the following command to install PHP on your server. The following command will install php7.2.

sudo apt install php7.2

Check PHP version and confirm the installation by running following command


php -v

Following are some basic PHP extensions needs to be installed on your server

sudo apt install php7.2-curl php7.2-mysql php7.2-common php7.2-cli php7.2-gd php7.2-opcache

Conclusion

In this tutorial, you have learned how to install PHP on Ubuntu 18.04 successfully with some of its basic extensions useful for frameworks and tested successfully. If you have any of the queries regarding this then you can comment below.

How to setting an ssh connection on a Virtualbox Ubuntu

How to setting an ssh connection on a Virtualbox Ubuntu

How To setting SSH connection to Ubuntu on Virtualbox. How to setting an ssh connection on a Virtualbox Ubuntu.

Create an Ubuntu Virtualbox

Create a new Virtual Machine and check Create a new virtual disk

Name your virtualmachine, select Linux as OS type and Ubuntu (64 bit) as linux version.

Set the virtual RAM at half of the your real RAM, in my case I have 8gb on my laptop, so i give to virtual machine 4gb

Create a new virtual disk:

Select the source for install your Ubuntu OS, select VDI (VirtualBox Disk Image)

Select Dynamic allocation:

Set the size of your virtual Hard Disk and click on create:

Start your Machine:

Select the iso image of ubuntu (You must download it from ubuntu official web site).

Install Ubuntu following the installation wizard:

After have installed Ubuntu, login your virtualmachine ubuntu with username and password inserted during the installation:

Install openssh-server

sudo apt-get update
sudo apt-get install openssh-server

stop your virtualmachine.

Set the net of your virtualmachine:

click on settings & net:

Select NAT as connection and click on advanced, and set as in the picture below.

Click on port forwarding:

and set the SSH parameter:

Name: SSH

Protocol: TCP

Host IP: 127.0.0.1

Host Port: 2222

IP Guest: Empty

Port Guest: 22

Start your Virtual Machine:

When your VM is started, open your terminal and try to connect:

ssh [email protected] -p 2222

now you shoud be inside your virtualmachine.

Problems and Possible Solutions:

If you can’t connect try to disable or change settings to ubuntu firewall:

sudo ufw disable

or try to connect to the ip of VM:

click on global tools:

Click on create:

Back to Settings > Network and select adapter 1

Select Adapter only Host.

go inside the VM and get the ip number with ifconfig and get ip address:

ifconfig

In my case the ip is 192.168.56.101

now from terminal I try to connect in ssh to the ip 192.168.56.101

ssh [email protected]

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