Saturday, April 21, 2018

Visual Studio 2015 - Nuget Package - Not responsive

Un-install Nuget Package that came with Visual Studio 2015 RTM ( Tools->Extensions and Updates).
Re-install latest (3.1.60 or >) Nuget Package Manager Visual Studio Extension ( from Online)
Then you can open without errors or silently failing

Download Here
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Friday, April 20, 2018

Step 2 - Install Tensor

 

 

$ sudo pip3 install --upgrade \
 https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.7.0-cp34-cp34m-linux_x86_64.whl 
 
 
CPU only:
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.7.0-cp36-cp36m-linux_x86_64.whl

 

 

 

 

 

Installing TensorFlow on Ubuntu

This guide explains how to install TensorFlow on Ubuntu. Although these instructions might also work on other Linux variants, we have only tested (and we only support) these instructions on machines meeting the following requirements:
  • 64-bit desktops or laptops
  • Ubuntu 16.04 or higher

Determine which TensorFlow to install

You must choose one of the following types of TensorFlow to install:
  • TensorFlow with CPU support only. If your system does not have a NVIDIA® GPU, you must install this version. Note that this version of TensorFlow is typically much easier to install (typically, in 5 or 10 minutes), so even if you have an NVIDIA GPU, we recommend installing this version first.
  • TensorFlow with GPU support. TensorFlow programs typically run significantly faster on a GPU than on a CPU. Therefore, if your system has a NVIDIA® GPU meeting the prerequisites shown below and you need to run performance-critical applications, you should ultimately install this version.

NVIDIA requirements to run TensorFlow with GPU support

If you are installing TensorFlow with GPU support using one of the mechanisms described in this guide, then the following NVIDIA software must be installed on your system:
  • CUDA® Toolkit 9.0. For details, see NVIDIA's documentation. Ensure that you append the relevant CUDA pathnames to the LD_LIBRARY_PATH environment variable as described in the NVIDIA documentation.
  • The NVIDIA drivers associated with CUDA Toolkit 9.0.
  • cuDNN v7.0. For details, see NVIDIA's documentation. Ensure that you create the CUDA_HOME environment variable as described in the NVIDIA documentation.
  • GPU card with CUDA Compute Capability 3.0 or higher for building from source and 3.5 or higher for our binaries. See NVIDIA documentation for a list of supported GPU cards.
  • The libcupti-dev library, which is the NVIDIA CUDA Profile Tools Interface. This library provides advanced profiling support. To install this library, issue the following command for CUDA Toolkit >= 8.0:
    $ sudo apt-get install cuda-command-line-tools
    and add its path to your LD_LIBRARY_PATH environment variable:
    $ export LD_LIBRARY_PATH=${LD_LIBRARY_PATH:+${LD_LIBRARY_PATH}:}/usr/local/cuda/extras/CUPTI/lib64
    For CUDA Toolkit <= 7.5 do:
    $ sudo apt-get install libcupti-dev
    * [OPTIONAL] For optimized inferencing performance, you can also install NVIDIA TensorRT 3.0. For details, see NVIDIA's TensorRT documentation. Only steps 1-4 in the TensorRT Tar File installation instructions are required for compatibility with TensorFlow; the Python package installation in steps 5 and 6 can be omitted. Detailed installation instructions can be found at package documentataion IMPORTANT: For compatibility with the pre-built tensorflow-gpu package, please use the Ubuntu 14.04 tar file package of TensorRT even when installing onto an Ubuntu 16.04 system.

If you have an earlier version of the preceding packages, please upgrade to the specified versions. If upgrading is not possible, then you may still run TensorFlow with GPU support, but only if you do the following:
  • Install TensorFlow from sources as documented in Installing TensorFlow from Sources.
  • Install or upgrade to at least the following NVIDIA versions:
    • CUDA toolkit 7.0 or greater
    • cuDNN v3 or greater
    • GPU card with CUDA Compute Capability 3.0 or higher.

Determine how to install TensorFlow

You must pick the mechanism by which you install TensorFlow. The supported choices are as follows:
We recommend the Virtualenv installation. Virtualenv is a virtual Python environment isolated from other Python development, incapable of interfering with or being affected by other Python programs on the same machine. During the Virtualenv installation process, you will install not only TensorFlow but also all the packages that TensorFlow requires. (This is actually pretty easy.) To start working with TensorFlow, you simply need to "activate" the virtual environment. All in all, Virtualenv provides a safe and reliable mechanism for installing and running TensorFlow.
Native pip installs TensorFlow directly on your system without going through any container system. We recommend the native pip install for system administrators aiming to make TensorFlow available to everyone on a multi-user system. Since a native pip installation is not walled-off in a separate container, the pip installation might interfere with other Python-based installations on your system. However, if you understand pip and your Python environment, a native pip installation often entails only a single command.
Docker completely isolates the TensorFlow installation from pre-existing packages on your machine. The Docker container contains TensorFlow and all its dependencies. Note that the Docker image can be quite large (hundreds of MBs). You might choose the Docker installation if you are incorporating TensorFlow into a larger application architecture that already uses Docker.
In Anaconda, you may use conda to create a virtual environment. However, within Anaconda, we recommend installing TensorFlow with the pip install command, not with the conda install command.
NOTE: The conda package is community supported, not officially supported. That is, the TensorFlow team neither tests nor maintains the conda package. Use that package at your own risk.

Installing with Virtualenv

Take the following steps to install TensorFlow with Virtualenv:
  1. Install pip and Virtualenv by issuing one of the following commands:
    $ sudo apt-get install python-pip python-dev python-virtualenv # for Python 2.7
$ sudo apt-get install python3-pip python3-dev python-virtualenv # for Python 3.n
  2. Create a Virtualenv environment by issuing one of the following commands:
    $ virtualenv --system-site-packages targetDirectory # for Python 2.7
$ virtualenv --system-site-packages -p python3 targetDirectory # for Python 3.n
    where targetDirectory specifies the top of the Virtualenv tree. Our instructions assume that targetDirectory is ~/tensorflow, but you may choose any directory.
  3. Activate the Virtualenv environment by issuing one of the following commands:
    $ source ~/tensorflow/bin/activate # bash, sh, ksh, or zsh
$ source ~/tensorflow/bin/activate.csh  # csh or tcsh
    The preceding source command should change your prompt to the following:
    (tensorflow)$
  4. Ensure pip ≥8.1 is installed:
    (tensorflow)$ easy_install -U pip
  5. Issue one of the following commands to install TensorFlow in the active Virtualenv environment:
    (tensorflow)$ pip install --upgrade tensorflow      # for Python 2.7
(tensorflow)$ pip3 install --upgrade tensorflow     # for Python 3.n
(tensorflow)$ pip install --upgrade tensorflow-gpu  # for Python 2.7 and GPU
(tensorflow)$ pip3 install --upgrade tensorflow-gpu # for Python 3.n and GPU
    If the above command succeeds, skip Step 6. If the preceding command fails, perform Step 6.
  6. (Optional) If Step 5 failed (typically because you invoked a pip version lower than 8.1), install TensorFlow in the active Virtualenv environment by issuing a command of the following format:
    (tensorflow)$ pip install --upgrade tfBinaryURL   # Python 2.7
(tensorflow)$ pip3 install --upgrade tfBinaryURL  # Python 3.n
    where tfBinaryURL identifies the URL of the TensorFlow Python package. The appropriate value of tfBinaryURLdepends on the operating system, Python version, and GPU support. Find the appropriate value for tfBinaryURL for your system here. For example, if you are installing TensorFlow for Linux, Python 3.4, and CPU-only support, issue the following command to install TensorFlow in the active Virtualenv environment:
    (tensorflow)$ pip3 install --upgrade \
 https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.7.0-cp34-cp34m-linux_x86_64.whl
If you encounter installation problems, see Common Installation Problems.

Next Steps

After installing TensorFlow, validate the installation.
Note that you must activate the Virtualenv environment each time you use TensorFlow. If the Virtualenv environment is not currently active, invoke one of the following commands:
 $ source ~/tensorflow/bin/activate      # bash, sh, ksh, or zsh
$ source ~/tensorflow/bin/activate.csh  # csh or tcsh
When the Virtualenv environment is active, you may run TensorFlow programs from this shell. Your prompt will become the following to indicate that your tensorflow environment is active:
(tensorflow)$
When you are done using TensorFlow, you may deactivate the environment by invoking the deactivate function as follows:
(tensorflow)$ deactivate
The prompt will revert back to your default prompt (as defined by the PS1 environment variable).

Uninstalling TensorFlow

To uninstall TensorFlow, simply remove the tree you created. For example:
$ rm -r targetDirectory

Installing with native pip

You may install TensorFlow through pip, choosing between a simple installation procedure or a more complex one.
Note: The REQUIRED_PACKAGES section of setup.py lists the TensorFlow packages that pip will install or upgrade.

Prerequisite: Python and Pip

Python is automatically installed on Ubuntu. Take a moment to confirm (by issuing a python -V command) that one of the following Python versions is already installed on your system:
  • Python 2.7
  • Python 3.4+
The pip or pip3 package manager is usually installed on Ubuntu. Take a moment to confirm (by issuing a pip -V or pip3 -V command) that pip or pip3 is installed. We strongly recommend version 8.1 or higher of pip or pip3. If Version 8.1 or later is not installed, issue the following command, which will either install or upgrade to the latest pip version:
$ sudo apt-get install python-pip python-dev   # for Python 2.7
$ sudo apt-get install python3-pip python3-dev # for Python 3.n

Install TensorFlow

Assuming the prerequisite software is installed on your Linux host, take the following steps:
  1. Install TensorFlow by invoking one of the following commands:
    $ pip install tensorflow      # Python 2.7; CPU support (no GPU support)
$ pip3 install tensorflow     # Python 3.n; CPU support (no GPU support)
$ pip install tensorflow-gpu  # Python 2.7;  GPU support
$ pip3 install tensorflow-gpu # Python 3.n; GPU support
    If the preceding command runs to completion, you should now validate your installation.
  2. (Optional.) If Step 1 failed, install the latest version of TensorFlow by issuing a command of the following format:
    $ sudo pip  install --upgrade tfBinaryURL   # Python 2.7
$ sudo pip3 install --upgrade tfBinaryURL   # Python 3.n
    where tfBinaryURL identifies the URL of the TensorFlow Python package. The appropriate value of tfBinaryURL depends on the operating system, Python version, and GPU support. Find the appropriate value for tfBinaryURL here. For example, to install TensorFlow for Linux, Python 3.4, and CPU-only support, issue the following command:
     $ sudo pip3 install --upgrade \
 https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.7.0-cp34-cp34m-linux_x86_64.whl
    If this step fails, see Common Installation Problems.

Next Steps

After installing TensorFlow, validate your installation.

Uninstalling TensorFlow

To uninstall TensorFlow, issue one of following commands:
$ sudo pip uninstall tensorflow  # for Python 2.7
$ sudo pip3 uninstall tensorflow # for Python 3.n

Installing with Docker

Take the following steps to install TensorFlow through Docker:
  1. Install Docker on your machine as described in the Docker documentation.
  2. Optionally, create a Linux group called docker to allow launching containers without sudo as described in the Docker documentation. (If you don't do this step, you'll have to use sudo each time you invoke Docker.)
  3. To install a version of TensorFlow that supports GPUs, you must first install nvidia-docker, which is stored in github.
  4. Launch a Docker container that contains one of the TensorFlow binary images.
The remainder of this section explains how to launch a Docker container.

CPU-only

To launch a Docker container with CPU-only support (that is, without GPU support), enter a command of the following format:
$ docker run -it -p hostPort:containerPort TensorFlowCPUImage
where:
  • -p hostPort:containerPort is optional. If you plan to run TensorFlow programs from the shell, omit this option. If you plan to run TensorFlow programs as Jupyter notebooks, set both hostPort and containerPort to 8888. If you'd like to run TensorBoard inside the container, add a second -p flag, setting both hostPort and containerPort to 6006.
  • TensorFlowCPUImage is required. It identifies the Docker container. Specify one of the following values:
    • tensorflow/tensorflow, which is the TensorFlow CPU binary image.
    • tensorflow/tensorflow:latest-devel, which is the latest TensorFlow CPU Binary image plus source code.
    • tensorflow/tensorflow:version, which is the specified version (for example, 1.1.0rc1) of TensorFlow CPU binary image.
    • tensorflow/tensorflow:version-devel, which is the specified version (for example, 1.1.0rc1) of the TensorFlow GPU binary image plus source code.
    TensorFlow images are available at dockerhub.
For example, the following command launches the latest TensorFlow CPU binary image in a Docker container from which you can run TensorFlow programs in a shell:
$ docker run -it tensorflow/tensorflow bash
The following command also launches the latest TensorFlow CPU binary image in a Docker container. However, in this Docker container, you can run TensorFlow programs in a Jupyter notebook:
$ docker run -it -p 8888:8888 tensorflow/tensorflow
Docker will download the TensorFlow binary image the first time you launch it.

GPU support

Prior to installing TensorFlow with GPU support, ensure that your system meets all NVIDIA software requirements. To launch a Docker container with NVidia GPU support, enter a command of the following format:
$ nvidia-docker run -it -p hostPort:containerPort TensorFlowGPUImage
where:
  • -p hostPort:containerPort is optional. If you plan to run TensorFlow programs from the shell, omit this option. If you plan to run TensorFlow programs as Jupyter notebooks, set both hostPort and containerPort to 8888.
  • TensorFlowGPUImage specifies the Docker container. You must specify one of the following values:
    • tensorflow/tensorflow:latest-gpu, which is the latest TensorFlow GPU binary image.
    • tensorflow/tensorflow:latest-devel-gpu, which is the latest TensorFlow GPU Binary image plus source code.
    • tensorflow/tensorflow:version-gpu, which is the specified version (for example, 0.12.1) of the TensorFlow GPU binary image.
    • tensorflow/tensorflow:version-devel-gpu, which is the specified version (for example, 0.12.1) of the TensorFlow GPU binary image plus source code.
We recommend installing one of the latest versions. For example, the following command launches the latest TensorFlow GPU binary image in a Docker container from which you can run TensorFlow programs in a shell:
$ nvidia-docker run -it tensorflow/tensorflow:latest-gpu bash
The following command also launches the latest TensorFlow GPU binary image in a Docker container. In this Docker container, you can run TensorFlow programs in a Jupyter notebook:
$ nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:latest-gpu
The following command installs an older TensorFlow version (0.12.1):
$ nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:0.12.1-gpu
Docker will download the TensorFlow binary image the first time you launch it. For more details see the TensorFlow docker readme.

Next Steps

You should now validate your installation.

Installing with Anaconda

Take the following steps to install TensorFlow in an Anaconda environment:
  1. Follow the instructions on the Anaconda download site to download and install Anaconda.
  2. Create a conda environment named tensorflow to run a version of Python by invoking the following command:
    $ conda create -n tensorflow pip python=2.7 # or python=3.3, etc.
  3. Activate the conda environment by issuing the following command:
    $ source activate tensorflow
 (tensorflow)$  # Your prompt should change
  4. Issue a command of the following format to install TensorFlow inside your conda environment:
    (tensorflow)$ pip install --ignore-installed --upgrade tfBinaryURL
    where tfBinaryURL is the URL of the TensorFlow Python package. For example, the following command installs the CPU-only version of TensorFlow for Python 3.4:
     (tensorflow)$ pip install --ignore-installed --upgrade \
 https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.7.0-cp34-cp34m-linux_x86_64.whl

Validate your installation

To validate your TensorFlow installation, do the following:
  1. Ensure that your environment is prepared to run TensorFlow programs.
  2. Run a short TensorFlow program.

Prepare your environment

If you installed on native pip, Virtualenv, or Anaconda, then do the following:
  1. Start a terminal.
  2. If you installed with Virtualenv or Anaconda, activate your container.
  3. If you installed TensorFlow source code, navigate to any directory except one containing TensorFlow source code.
If you installed through Docker, start a Docker container from which you can run bash. For example:
$ docker run -it tensorflow/tensorflow bash

Run a short TensorFlow program

Invoke python from your shell as follows:
$ python
Enter the following short program inside the python interactive shell:
# Python
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
If the system outputs the following, then you are ready to begin writing TensorFlow programs:
Hello, TensorFlow!
If you are new to TensorFlow, see Getting Started with TensorFlow.
If the system outputs an error message instead of a greeting, see Common installation problems.

Common installation problems

We are relying on Stack Overflow to document TensorFlow installation problems and their remedies. The following table contains links to Stack Overflow answers for some common installation problems. If you encounter an error message or other installation problem not listed in the following table, search for it on Stack Overflow. If Stack Overflow doesn't show the error message, ask a new question about it on Stack Overflow and specify the tensorflow tag.
Stack Overflow Link Error Message
36159194 
ImportError: libcudart.so.Version: cannot open shared object file:
  No such file or directory
41991101 
ImportError: libcudnn.Version: cannot open shared object file:
  No such file or directory
36371137 and here 
libprotobuf ERROR google/protobuf/src/google/protobuf/io/coded_stream.cc:207] A
  protocol message was rejected because it was too big (more than 67108864 bytes).
  To increase the limit (or to disable these warnings), see
  CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.
35252888 
Error importing tensorflow. Unless you are using bazel, you should
  not try to import tensorflow from its source directory; please exit the
  tensorflow source tree, and relaunch your python interpreter from
  there.
33623453 
IOError: [Errno 2] No such file or directory:
  '/tmp/pip-o6Tpui-build/setup.py'
42006320 
ImportError: Traceback (most recent call last):
  File ".../tensorflow/core/framework/graph_pb2.py", line 6, in 
  from google.protobuf import descriptor as _descriptor
  ImportError: cannot import name 'descriptor'
35190574 
SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify
  failed
42009190 
  Installing collected packages: setuptools, protobuf, wheel, numpy, tensorflow
  Found existing installation: setuptools 1.1.6
  Uninstalling setuptools-1.1.6:
  Exception:
  ...
  [Errno 1] Operation not permitted:
  '/tmp/pip-a1DXRT-uninstall/.../lib/python/_markerlib'
36933958 
  ...
  Installing collected packages: setuptools, protobuf, wheel, numpy, tensorflow
  Found existing installation: setuptools 1.1.6
  Uninstalling setuptools-1.1.6:
  Exception:
  ...
  [Errno 1] Operation not permitted:
  '/tmp/pip-a1DXRT-uninstall/System/Library/Frameworks/Python.framework/
   Versions/2.7/Extras/lib/python/_markerlib'

The URL of the TensorFlow Python package

A few installation mechanisms require the URL of the TensorFlow Python package. The value you specify depends on three factors:
  • operating system
  • Python version
  • CPU only vs. GPU support
This section documents the relevant values for Linux installations.

Python 2.7

CPU only:
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.7.0-cp27-none-linux_x86_64.whl
GPU support:
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.7.0-cp27-none-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in NVIDIA requirements to run TensorFlow with GPU support.

Python 3.4

CPU only:
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.7.0-cp34-cp34m-linux_x86_64.whl
GPU support:
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.7.0-cp34-cp34m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in NVIDIA requirements to run TensorFlow with GPU support.

Python 3.5

CPU only:
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.7.0-cp35-cp35m-linux_x86_64.whl
GPU support:
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.7.0-cp35-cp35m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in NVIDIA requirements to run TensorFlow with GPU support.

Python 3.6

CPU only:
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.7.0-cp36-cp36m-linux_x86_64.whl
GPU support:
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.7.0-cp36-cp36m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in NVIDIA requirements to run TensorFlow with GPU support.
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Use ful commands for enviroment

When you’re ready to deactivate your Anaconda environment, you can do so by typing:
    

  • source deactivate

    •
Note that you can replace the word source with . to achieve the same results.
To target a more specific version of Python, you can pass a specific version to the python argument, like 3.5, for example:
    

  • conda create -n my_env35 python=3.5

    •
You can update your version of Python along the same branch (as in updating Python 3.5.1 to Python 3.5.2) within a respective environment with the following command:
    

  • conda update python 
    •
You can inspect all of the environments you have set up with this command:
    

  • conda info --envs

    • 




Output

# conda environments:
#
my_env                   /home/sammy/anaconda3/envs/my_env
my_env35                 /home/sammy/anaconda3/envs/my_env35
root                  *  /home/sammy/anaconda3 
 
If you are no longer working on a specific project and have no further need for the associated environment, you can remove it. To do so, type the following:

  • conda remove --name my_env35 --all 

Permission management
sudo nautilus 
 


 
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Step 1 - Setting up Anaconda 3 on Ubuntu 16

How To Install the Anaconda Python Distribution on Ubuntu 16.04

UpdatedDecember 27, 2017 335.4k views Development Python Data Analysis Ubuntu 16.04

Introduction

Anaconda is an open-source package manager, environment manager, and distribution of the Python and R programming languages. It is commonly used for large-scale data processing, scientific computing, and predictive analytics, serving data scientists, developers, business analysts, and those working in DevOps.
Anaconda offers a collection of over 720 open-source packages, and is available in both free and paid versions. The Anaconda distribution ships with the conda command-line utility. You can learn more about Anaconda and conda by reading the Anaconda Documentation pages.
This tutorial will guide you through installing the Python 3 version of Anaconda on an Ubuntu 16.04 server.

Prerequisites

Before you begin with this guide, you should have a non-root user with sudo privileges set up on your server. You can learn how to do this by completing our Ubuntu 16.04 initial server setup guide.

Installing Anaconda

The best way to install Anaconda is to download the latest Anaconda installer bash script, verify it, and then run it.
Find the latest version of Anaconda for Python 3 at the Anaconda Downloads page. At the time of writing, the latest version is 5.0.1, but you should use a later stable version if it is available.
Next, change to the /tmp directory on your server. This is a good directory to download ephemeral items, like the Anaconda bash script, which we won't need after running it.
    

  • cd /tmp

    •
Use curl to download the link that you copied from the Anaconda website:
    

  • curl -O https://repo.continuum.io/archive/Anaconda3-5.0.1-Linux-x86_64.sh

    •
We can now verify the data integrity of the installer with cryptographic hash verification through the SHA-256 checksum. We’ll use the sha256sum command along with the filename of the script:
    

  • sha256sum Anaconda3-5.0.1-Linux-x86_64.sh

    •
You’ll receive output that looks similar to this:

Output

55e4db1919f49c92d5abbf27a4be5986ae157f074bf9f8238963cd4582a4068a  Anaconda3-5.0.1-Linux-x86_64.sh
You should check the output against the hashes available at the Anaconda with Python 3 on 64-bit Linux page for your appropriate Anaconda version. As long as your output matches the hash displayed in the sha2561 row then you’re good to go.
Now we can run the script:
    

  • bash Anaconda3-5.0.1-Linux-x86_64.sh

    •
You’ll receive the following output:

Output

Welcome to Anaconda3 5.0.1 (by Continuum Analytics, Inc.)

In order to continue the installation process, please review the license
agreement.
Please, press ENTER to continue
Press ENTER to continue and then press ENTER to read through the license. Once you’re done reading the license, you’ll be prompted to approve the license terms:

Output

Do you approve the license terms? [yes|no]
As long as you agree, type yes.
At this point, you’ll be prompted to choose the location of the installation. You can press ENTER to accept the default location, or specify a different location to modify it.

Output

Anaconda3 will now be installed into this location:
/home/sammy/anaconda3

  - Press ENTER to confirm the location
  - Press CTRL-C to abort the installation
  - Or specify a different location below

[/home/sammy/anaconda3] >>>
The installation process will continue, it may take some time.
Once it’s complete you’ll receive the following output:

Output

...
installation finished.
Do you wish the installer to prepend the Anaconda3 install location
to PATH in your /home/sammy/.bashrc ? [yes|no]
[no] >>>
Type yes so that you can use the conda command. You’ll next see the following output:

Output

Prepending PATH=/home/sammy/anaconda3/bin to PATH in /home/sammy/.bashrc
A backup will be made to: /home/sammy/.bashrc-anaconda3.bak
...
In order to activate the installation, you should source the ~/.bashrc file:
    

  • source ~/.bashrc

    •
Once you have done that, you can verify your install by making use of the conda command, for example with list:
    

  • conda list

    •
You’ll receive output of all the packages you have available through the Anaconda installation:

Output

# packages in environment at /home/sammy/anaconda3:
#
_ipyw_jlab_nb_ext_conf    0.1.0            py36he11e457_0  
alabaster                 0.7.10           py36h306e16b_0  
anaconda                  5.0.1            py36hd30a520_1
...
Now that Anaconda is installed, we can go on to setting up Anaconda environments.

Setting Up Anaconda Environments

Anaconda virtual environments allow you to keep projects organized by Python versions and packages needed. For each Anaconda environment you set up, you can specify which version of Python to use and can keep all of your related programming files together within that directory.
First, we can check to see which versions of Python are available for us to use:
    

  • conda search "^python$"

    •
You’ll receive output with the different versions of Python that you can target, including both Python 3 and Python 2 versions. Since we are using the Anaconda with Python 3 in this tutorial, you will have access only to the Python 3 versions of packages.
Let’s create an environment using the most recent version of Python 3. We can achieve this by assigning version 3 to the python argument. We’ll call the environment my_env, but you’ll likely want to use a more descriptive name for your environment especially if you are using environments to access more than one version of Python.
    

  • conda create --name my_env python=3

    •
We’ll receive output with information about what is downloaded and which packages will be installed, and then be prompted to proceed with y or n. As long as you agree, type y.
The conda utility will now fetch the packages for the environment and let you know when it’s complete.
You can activate your new environment by typing the following:
    

  • source activate my_env

    •
With your environment activated, your command prompt prefix will change:
    

  • 

    •
Within the environment, you can verify that you’re using the version of Python that you had intended to use:
    

  •  python --version

    • 




Output

Python 3.6.0 :: Continuum Analytics, Inc.
When you’re ready to deactivate your Anaconda environment, you can do so by typing:
    

  • source deactivate

    •
Note that you can replace the word source with . to achieve the same results.
To target a more specific version of Python, you can pass a specific version to the python argument, like 3.5, for example:
    

  • conda create -n my_env35 python=3.5

    •
You can update your version of Python along the same branch (as in updating Python 3.5.1 to Python 3.5.2) within a respective environment with the following command:
    

  • conda update python

    •
If you would like to target a more specific version of Python, you can pass that to the python argument, as in python=3.3.2.
You can inspect all of the environments you have set up with this command:
    

  • conda info --envs

    • 




Output

# conda environments:
#
my_env                   /home/sammy/anaconda3/envs/my_env
my_env35                 /home/sammy/anaconda3/envs/my_env35
root                  *  /home/sammy/anaconda3
The asterisk indicates the current active environment.
Each environment you create with conda create will come with several default packages:
  • openssl
  • pip
  • python
  • readline
  • setuptools
  • sqlite
  • tk
  • wheel
  • xz
  • zlib
You can add additional packages, such as numpy for example, with the following command:
    

  • conda install --name my_env35 numpy

    •
If you know you would like a numpy environment upon creation, you can target it in your conda create command:
    

  • conda create --name my_env python=3 numpy

    •
If you are no longer working on a specific project and have no further need for the associated environment, you can remove it. To do so, type the following:
    

  • conda remove --name my_env35 --all

    •
Now, when you type the conda info --envs command, the environment that you removed will no longer be listed.

Updating Anaconda

You should regularly ensure that Anaconda is up-to-date so that you are working with all the latest package releases.
To do this, you should first update the conda utility:
    

  • conda update conda

    •
When prompted to do so, type y to proceed with the update.
Once the update of conda is complete, you can update the Anaconda distribution:
    

  • conda update anaconda

    •
Again when prompted to do so, type y to proceed.
This will ensure that you are using the latest releases of conda and Anaconda.

Uninstalling Anaconda

If you are no longer using Anaconda and find that you need to uninstall it, you should start with the anaconda-clean module which will remove configuration files for when you uninstall Anaconda.
    

  • conda install anaconda-clean

    •
Type y when prompted to do so.
Once it is installed, you can run the following command. You will be prompted to answer y before deleting each one. If you would prefer not to be prompted, add --yes to the end of your command:
anaconda-clean
This will also create a backup folder called .anaconda_backup in your home directory:

Output

Backup directory: /home/sammy/.anaconda_backup/2017-01-25T191831
You can now remove your entire Anaconda directory by entering the following command:
    

  • rm -rf ~/anaconda3

    •
Finally, you can remove the PATH line from your .bashrc file that Anaconda added. To do so, first open nano:
    

  • nano ~/.bashrc

    •
Then scroll down to the end of the file (if this is a recent install) or type CTRL + W to search for Anaconda. Delete or comment out the following lines:
/home/sammy/.bashrc
# added by Anaconda3 4.2.0 installer
export PATH="/home/sammy/anaconda3/bin:$PATH"
When you’re done editing the file, type CTRL + X to exit and y to save changes.
Anaconda is now removed from your server.

Conclusion

This tutorial walked you through the installation of Anaconda, working with the conda command-line utility, setting up environments, updating Anaconda, and deleting Anaconda if you no longer need it.
You can use Anaconda to help you manage workloads for data science, scientific computing, analytics, and large-scale data processing.


Anaconda comes as a .sh file which is to be installed with the following command
bash Anaconda3-4.3.1-Linux-x86_64.sh
Now even though I have done it before for some reason I added a sudo before this command rendering the anaconda3 folder inaccesible without root permission.
Therefore the conda package management system could not access the directory and hence the issue. If such an issue exists simply delete the previous installation instance with sudo rm -rf anaconda3 and reinstall.
Thanks to George for his valuable comments!
hough Djokester's answer should work fine, it seems like terrible overkill to me. One could just do: 
sudo chown -R username:username anaconda3
Where username is your users name.
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Monday, April 9, 2018

Visual Studio 2015 - Downloads

You can use these links to download Visual Studio 2015
Community Edition:
And for anyone in the future who might be looking for the other editions here are the links for them as well:
Professional Edition:
Enterprise Edition:
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Thursday, April 5, 2018

Ubuntu Folder Permission Administration - Super User

how i can bulk change directory, folder and file permissions in Ubuntu using the GUI.
What you want to do is a admin task so you need nautilus in administration mode. From a terminal window (Ctrl+Alt+T or dash, "terminal") issue:
sudo nautilus
  • enter password;
  • navigate to directory
  • Right click on empty space in the main window of nautilus
  • Click on properties and go to tab permissions
  • Change permissons to what you need
Example of a random directory from my system (/var/tmp/):
enter image description here

The highlighted part does the files inside the directory.
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