I am using Linux Slackware 14.2, opam version 1.2.2, ocaml 4.03.0 and wanted to install why3 module. As far as I can see in the Internet, the last version of why3 package is 1.2.0.
But even after I ran
opam update
It still suggests my to install why3 1.0.0.
What should I do to update the packages list?
How can I install why3 module system wide as root? Is it safe enough?
Your opam and ocaml installations are both seriously out of date. The latest version of OCaml is 4.07.1, and the latest version of opam is 2.0.4. Linux distro repositories are unfortunately often not very up to date, and as I understand it, packages published for opam v2 aren't backwards-compatible with opam v1.x.
Try installing the latest opam by following the installation guide. Then, using opam, install a more recent version of OCaml and you should be able to get the latest version of why3.
It's recommended to manage your OCaml installations and associated packages with opam instead of installing them globally, since that can potentially cause an unruly mess of broken dependencies over time.
I am trying to install a Python module - pyproj-1.9.5.1-cp27-cp27m-win32.whl using the Python package installer - pip. Since I use a version of Python 2.7.10, which comes bundled with ArcGIS 10.4.1, it is already preloaded with pip, as all versions 2.7.9 onwards are. To install the module, I opened the Command Prompt in the directory of the module and typed
pip install pyproj-1.9.5.1-cp27-cp27m-win32
to get the error, which I have written below:
Could not find a version that satisfies the requirement pyproj-1.9.5.1-cp27-cp27m-win32 (from versions:) No matching distribution found for pyproj-1.9.5.1-cp27-cp27m-win32
Since ArcGIS 10.4 comes bundled with Python 2.7.10, it is pre-installed with pip. Pip is a recursive acronym that stands for "pip installs packages" and is a package management system that is used to install software packages written in Python, which is pre-installed in Python version 2.7.9+. Therefore, to install pyproj, make sure that %PYTHON_HOME%\Scripts\ and C:\Python27\ArcGIS10.4 are added in the Path of Environmental Variables in System Properties. After that, run Command Prompt as an administrator and type pip install pyproj to install pyproj.
I am installing Django framework in my virtual environment in Ubuntu 16.04 LTS. Need to decide whether to inherit the system-site-packages for my virtual environment. Are their any any potential conflicts of the system wide python3 packages with the Django installation?
If you use system site packages, problems can come when you ship your app on a different machine, where global environment can be very different from what you have locally, which causes lots of problems when deploying. Use clean virtual environment for every not only Django, but all Python projects.
Yes, system-wide packages can cause conflicts. Distro packages are sometimes outdated or are following LTS releases, and installing in a local environment the same package can lead to errors (some time ago I had a problem where django was installed with distro packages, and it was older than the one in my virtual environment, causing issues).
The rule I tend to follow while developing in python : only install Python on the distro. Then for each python project, use a virtual environment. This way you will never have a problem making projects.
As I've written elsewhere, "Many people prefer their isolated environments to be, well, isolated, so they don't use [--system-site-packages], but I use it often in order to avoid compiling packages. pip install gdal can take a long time because it needs to compile stuff, whereas apt-get install python-gdal is way faster; so I use --system-site-packages in order to be able to use the system-wide gdal (this issue is becoming less important now that most Python packages are distributed compiled, as a Python wheel). In addition, psycopg2 can be tricky to compile, and it is usually better to use the operating system's version, which is usually guaranteed to work with the operating system's postgresql. In Windows, however, I normally don't use --system-site-packages, as there are no site packages packaged with the operating system."
I have never had conflicts nor any other problems. The operating system can often have outdated packages, but this is not a problem. If the operating system has pandas 0.17 and my Django project requires 0.19, when I type pip install -r requirements.txt it will install 0.19, which will override the operating system's package.
Note that I never run pip system-wide; I always run it in a virtualenv. So my system-wide installation only has packages installed with apt-get.
I am starting to work with TensorFlow library for deep learning, https://www.tensorflow.org/.
I found a explicit guide to work on it on linux and Mac but I did not find how to work with it under Windows. I try over the net, but the information are lacking.
I use Visual Studio 2015 for my projects, and I am trying to compile the library with Visual studio Compiler VC14.
How to install it and to use it under Windows?
Can I use Bazel for Windows for production use?
How to install TensorFlow and to use it under Windows?
Updated on 8/4/16
Windows 10 now has a Ubuntu Bash environment, AKA Bash on Ubuntu on Windows, available as a standard option (as opposed to Insider Preview updates for developers). (StackOverflow tag wsl) This option came with the Windows 10 anniversary update (Version 1607) released on 8/2/2016. This allows the use of apt-get to install software packages such as Python and TensorFlow.
Note: Bash on Ubuntu on Windows does not have access to the GPU, so all of the GPU options for installing TensorFlow will not work.
The dated installation instructions for Bash on Ubuntu on Windows are basically correct, but only these steps are necessary:
Prerequisites
Enable the Windows Subsystem for Linux feature (GUI)
Reboot when prompted
Run Bash on Windows
Steps no longer needed:
Turn on Developer Mode
Enable the Windows Subsystem for Linux feature (command-line)
Then install TensorFlow using apt-get
sudo apt-get install python3-pip python3-dev
sudo pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp34-cp34m-linux_x86_64.whl
and now test TensorFlow
$ python3
...
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print(sess.run(hello))
Hello, TensorFlow!
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> print(sess.run(a + b))
42
>>> exit()
and run an actual neural network
python3 -m tensorflow.models.image.mnist.convolutional
Earlier Answer
After learning about the developer preview of Bash on Windows.
See Playing with TensorFlow on Windows by Scott Hanselman which uses Bash on Windows 10
Original Answer
Bazel is the problem
TensorFlow is not made with build automation tools such as make, but with Google's in-house build tool Bazel. Bazel only works on systems based on Unix such as Linux and OS X.
Since the current published/known means to build TensorFlow uses Bazel and Bazel does not work on Windows, one can not install or run TensorFlow natively on Windows.
From Bazel FAQ
What about Windows?
Due to its UNIX heritage, porting Bazel to Windows is significant
work. For example, Bazel uses symlinks extensively, which has varying
levels of support across Windows versions.
We are currently actively working on improving Windows support, but
it's still ways from being usable.
Status
See: TensorFlow issue #17
See: Bazel issue #276
Solutions
The solutions are listed in the order of complexity and work needed; from about an hour to may not even work.
Docker
~ 1 hour
Docker installation
Docker is a system to build self contained versions of a Linux operating system running on your machine. When you install and run TensorFlow via Docker it completely isolates the installation from pre-existing packages on your machine.
Also look at TensorFlow - which Docker image to use?
OS X
~ 1 hour
If you have a current Mac running OS X then see: Installation for Mac OS X
Linux
The recommend Linux system tends to be Ubuntu 14.04 LTS (Download page).
a. Virtual Machine - Hardware Virtualization - Full Virtualization
~ 3 hours
Download and install a virtual machine such as the commercial VMware or the free Virtual Box, after which you can install Linux and then install TensorFlow.
When you go to install TensorFlow you will be using Pip - Python's package management system. Visual Studio users should think NuGet. The packages are known as wheels.
See: Pip Installation
If you need to build from the source then see: Installing From Sources
~ 4 hours
Note: If you plan on using a Virtual Machine and have never done so before, consider using the Docker option instead, since Docker is the Virtual Machine, OS and TensorFlow all packaged together.
b. Dual boot
~ 3 hours
If you want to run TensorFlow on the same machine that you have Windows and make use of the GPU version then you will most likely have to use this option as running on a hosted Virtual Machine, Type 2 hypervisor, will not allow you access to the GPU.
Remote machine
~ 4 hours
If you have remote access to another machine that you can install the Linux OS and TensorFlow software on and allow remote connections to, then you can use your Windows machine to present the remote machine as an application running on Windows.
Cloud Service
I have no experience with this. Please edit answer if you know.
Cloud services such as AWS are being used.
From TensorFlow Features
Want to run the model as a service in the cloud?
Containerize with Docker and TensorFlow just works.
From Docker
Running Docker on AWS provides a highly reliable, low-cost way to
quickly build, ship, and run distributed applications at scale. Deploy
Docker using AMIs from the AWS Marketplace.
Wait for Bazel to work on Windows.
Currently it appears the only hold up is Bazel, however Bazel's roadmap list working on Windows should be available this year.
There are two features listed for Windows:
2016‑02 Bazel can bootstrap itself on Windows without requiring admin privileges.
2016‑12 Full Windows support for Android: Android feature set is identical for Windows and Linux/OS X.
Build TensorFlow by hand.
A few days or more depending on you skill level. I gave up on this one; too many subprojects to build and files to locate.
Remember that Bazel is only used to build TensorFlow. If you get the commands Bazel runs and the correct source code and libraries you should be able to build TensorFlow on Windows. See: How do I get the commands executed by Bazel.
While I have not researched this more, you can look at the continuous integration info for needed files and info on how to they build it for testing. (Readme) (site)
Build Bazel on Windows
A few days or more depending on you skill level. I gave up on this one also; could not find the necessary source files needed for Windows.
There is a public experimental source code version of Bazel that boots on Windows. You may be able to leverage this into getting Bazel to work on Windows, etc.
Also these solutions require the use of Cygwin or MinGW which adds another layer of complexity.
Use alternative build system such as Make
If you get this one to work I would like to see in on GitHub.
This currently does not exist for TensorFlow. It is a feature request.
See: TensorFlow issue 380
Cross Build
If you get this one to work I would like to see in on GitHub.
You build TensorFlow on Linux using Bazel but change the build process to output a wheel that can be installed on Windows. This will require detailed knowledge of Bazel to change the configuration, and locating the source code and libraries that work with Windows. An option I would only suggest as a last resort. It may not even be possible.
Run on the new Windows Subsystem for Linux.
See: Windows Subsystem for Linux Overview
You will know as much as I do by reading the referenced article.
Can I use Bazel for Windows for production use?
Since it is experimental software I would not use on a production machine.
Remember that you only need Bazel to build TensorFlow. So use the experimental code on a non production machine to build the wheel, then install the wheel on a production machine. See: Pip Installation
TLDR;
Currently I have several versions for learning. Most use a VMWare 7.1 Workstation to host Ubuntu 14.04 LTS or Ubuntu 15 or Debian. I also have one dual boot of Ubuntu 14.04 LTS on my Windows machine to access the GPU as the machine with VMware does not have the proper GPU. I would recommend that you give these machines at least 8G of memory either as RAM or RAM and swap space as I have run out of memory a few times.
I can confirm that it works in the Windows Subsystem for Linux!
And it is also very straightforward.
In the Ubuntu Bash on Windows 10, first update the package index:
apt-get update
Then install pip for Python 2:
sudo apt-get install python-pip python-dev
Install tensorflow:
sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl
The package is now installed an you can run the CNN sample on the MNIST set:
cd /usr/local/lib/python2.7/dist-packages/tensorflow/models/image/mnist
python convolutional.py
I just tested the CPU package for now.
I blogged about it: http://blog.mosthege.net/2016/05/11/running-tensorflow-with-native-linux-binaries-in-the-windows-subsystem-for-linux/
cheers
~michael
Sorry for the excavation, but this question is quite popular, and now it has a different answer.
Google officially announced the addition of Windows (7, 10, and Server 2016) support for TensorFlow:
developers.googleblog.com
The Python module can be installed using pip with a single command:
C:\> pip install tensorflow
And if you need GPU support:
C:\> pip install tensorflow-gpu
TensorFlow manual - How to install pip on windows
Another useful information are included in release notes:
https://github.com/tensorflow/tensorflow/releases
UPD: As #m02ph3u5 right mentioned in the comments TF for windows supports only Python 3.5.x Installing TensorFlow on Windows with native pip
Installing TensorFlow
TensorFlow currently supports only Python 3.5 64-bit. Both CPU and GPU are supported. Here are some installation instructions assuming you do not have Python 3.5 64-bit:
Download and install Microsoft Visual C++ 2015 Redistributable Update 3:
https://www.microsoft.com/en-us/download/details.aspx?id=53587 (required by Python 3.5 and TensorFlow)
Download and install Python 3.5 64-bit: https://www.python.org/ftp/python/3.5.2/python-3.5.2-amd64.exe
Install pip as follows: download https://bootstrap.pypa.io/get-pip.py, then run python get-pip.py
Install TensorFlow with either pip install tensorflow (CPU version) or pip install tensorflow-gpu (GPU version --> requires CUDA to be installed).
Testing TensorFlow
You can now run something like following to test whether TensorFlow is working fine:
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
a = tf.constant(10)
b = tf.constant(32)
print(sess.run(a + b))
TensorFlow comes with a few models, which are located in C:\Python35\Lib\site-packages\tensorflow\models\ (assuming you installed python in C:\Python35). For example, you can run in the console:
python -m tensorflow.models.image.mnist.convolutional
or
python C:\Python35\Lib\site-packages\tensorflow\models\image\mnist\convolutional.py
Limitations of TensorFlow on Windows
Initial support for building TensorFlow on Microsoft Windows was added on 2016-10-05 in commit 2098b9abcf20d2c9694055bbfd6997bc00b73578:
This PR contains an initial version of support for building TensorFlow
(CPU only) on Windows using CMake. It includes documentation for
building with CMake on Windows, platform-specific code for
implementing core functions on Windows, and CMake rules for building
the C++ example trainer program and a PIP package (Python 3.5 only).
The CMake rules support building TensorFlow with Visual Studio 2015.
Windows support is a work in progress, and we welcome your feedback
and contributions.
For full details of the features currently supported and instructions
for how to build TensorFlow on Windows, please see the file
tensorflow/contrib/cmake/README.md.
The Microsoft Windows support was introduced in TensorFlow in version 0.12 RC0 (release notes):
TensorFlow now builds and runs on Microsoft Windows (tested on Windows 10, Windows 7, and Windows Server 2016). Supported languages include Python (via a pip package) and C++. CUDA 8.0 and cuDNN 5.1 are supported for GPU acceleration. Known limitations include: It is not currently possible to load a custom op library. The GCS and HDFS file systems are not currently supported. The following ops are not currently implemented: DepthwiseConv2dNative, DepthwiseConv2dNativeBackpropFilter, DepthwiseConv2dNativeBackpropInput, Dequantize, Digamma, Erf, Erfc, Igamma, Igammac, Lgamma, Polygamma, QuantizeAndDequantize, QuantizedAvgPool, QuantizedBatchNomWithGlobalNormalization, QuantizedBiasAdd, QuantizedConcat, QuantizedConv2D, QuantizedMatmul, QuantizedMaxPool, QuantizeDownAndShrinkRange, QuantizedRelu, QuantizedRelu6, QuantizedReshape, QuantizeV2, RequantizationRange, and Requantize.
Now Tensorflow is officially supported in Windows, you can install it using pip command of Python 3.5 without compile it yourself
CPU Version
pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-0.12.0-cp35-cp35m-win_amd64.whl
cp35 indicates python 3.5 wheel, 0.12.0 the version, you can edit these according your preference, or to install latest CPU version available you can use
pip install --upgrade tensorflow
GPU Version
pip install --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-0.12.0-cp35-cp35m-win_amd64.whl
cp35 indicates python 3.5 wheel, 0.12.0 the version, you can edit these according your preference, or to install latest GPU version available you can use
pip install --upgrade tensorflow-gpu
More Info
Following may work for you: install Virtual Box, create Linux VM and install Linux into it. I'd recommend Ubuntu, because Google often uses it internally. Then, install TensorFlow in Linux VM.
You can't at the moment. The problem is that tensorflow uses the bazel build another Google internal tool that has been exposed as an open source project and it has only support for mac and unix.
Until bazel is ported to windows or another build system is added to tensorflow there is a little chance to run tensorflow natively on windows.
That said you can install virtualbox and then install docker-machine and run a linux container with tensorflow inside it.
I managed to install TensorFlow on Win8.1 without Docker using advice from
https://discussions.udacity.com/t/windows-tensorflow-and-visual-studio-2015/45636
I tried a lot of stuff before that, and i won't try to install it twice but here is what i did:
- install VS2015 (make sure Visual C++ installed as well)
- install Python Tools for VS2015
- install Python2.7 with Anaconda2
- install pip and conda for Python
- install numpy with pip inside VS2015
- install tensorflow with pip inside VS2015
i didn't manage to do it with Python3.5
I managed also to install on Win8.1 via Cloud9
There is a video tutorial on Youtube.
https://www.youtube.com/watch?v=kMtrOIPLpR0
EDIT: actually for the above, (not Cloud9 which is fine) i have problems:
TensorFlow LOOKS LIKE it's installed (i can see it in the list of modules installed in VS2015 when clicking in Solution Explorer on Python 64-bit 2.7)
but if i type in a script or in Python Interactive import tensorflow as TF then i get an error message
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\Fagui\Anaconda2\lib\site-packages\tensorflow\__init__.py", line 23, in <module>
from tensorflow.python import *
File "C:\Users\Fagui\Anaconda2\lib\site-packages\tensorflow\python\__init__.py", line 50, in <module>
from tensorflow.python.framework.framework_lib import *
File "C:\Users\Fagui\Anaconda2\lib\site-packages\tensorflow\python\framework\framework_lib.py", line 62, in <module>
from tensorflow.python.framework.ops import Graph
File "C:\Users\Fagui\Anaconda2\lib\site-packages\tensorflow\python\framework\ops.py", line 40, in <module>
from tensorflow.python.framework import versions
File "C:\Users\Fagui\Anaconda2\lib\site-packages\tensorflow\python\framework\versions.py", line 24, in <module>
from tensorflow.python import pywrap_tensorflow
File "C:\Users\Fagui\Anaconda2\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 28, in <module>
_pywrap_tensorflow = swig_import_helper()
File "C:\Users\Fagui\Anaconda2\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 20, in swig_import_helper
import _pywrap_tensorflow
As of writing this answer, I wasn't able to get tensorflow to install properly with python version 3.5.2. Reverting to python 3.5.0 did the trick.
Then I was able to install with
C:> pip install tensorflow
If you have already installed anaconda on your windows, there is an easier way as I found out:
conda create --name snakes python=3
Then
activate snakes
Then
pip install tensorflow
This is similar to virtualenv and I found this helpful.
Follow this link to install Tensorflow on Windows and you can also use it in Visual Studio
How to install ewswrapper for python off of their site on a windows, (sadly), 64 bit machine with python 2.7.
The Link to the site is here.
Thank you for your help.
The generally recommended way to install python packages is using a package manager like pip or (gasp) easy_install. However, there are a few exceptions like this one.
On Windows, there may be a pre-built package for you to download. In this case, the link is http://ewswrapper.lafiel.net/index.php?al3x_download=file&userid=PUBLIC&filepath=/PYTHON_Releases/_PYTHON_2012_02_09_EWSWrapper_v_0_2.7z.
You'll need to unzip the file with 7zip, and place the resulting directory in your site-packages path. You can locate your system's site-packages path by looking at this SO answer: How do I find the location of my Python site-packages directory?
Additionally, EWSWrapper will require a few dependencies that you should be able to get in pip.
pip.exe install python-ntlm suds
If you don't have pip, you can install it: How do I install pip on Windows?
Just so you know, this isn't the normal way a package is installed. Perhaps someone should let them know...