Windows environment is not yet supported.
But if we use docker s/w that virtual machine similar with virtual box, we can run tensor flow on window environment.
In other words, docker create virtual environment for linux environment.
So how we use the tensorflow result that learned based on linux or mac in window?
I have not tried it yet.
There are api for c++ version in tensorflow.
There is no learning part, C++ version is included only evaluating part.
But if I success build c++ version api of tensor flow in window environment, we available the learning result in window after learning from linux.
I will try this plan after test basic tensorflow.
Then, let's use the tensorflow using docker s/w on window.
Tutorial about installation in linux and mac, refer to official site.
0. docker install guide
First page to install docker for various environments
1. download docker toolbox
download docker for widnow and mac user
document for install docker in window
3. run docker
excute Docker Quickstart terminal.
After setting (first takes about 1~2 minutes)
You can see the whale illustration.
verify your setup, type this command
$ docker run hello-world
then you can this message.
type this command
$docker-machine ip default
then you can check notebook server ip.
type this command
$ docker run -it b.gcr.io/tensorflow/tensorflow
$ docker run -p 8888:8888 -it b.gcr.io/tensorflow/tensorflow
when you enter the command firstly, tensorflw is installed once.
and run notebook server.
but in my case, first command is not run notebook server.
If you problem with me, type second command.
type this on you browser
then you can see notebook web page.
docker run -p 8888:8888 -d --name tfu b.gcr.io/tensorflow-udacity/assignments
*starting your container
docker start tfu
*stopping your container
docker stop tfu
show detail here
#build a local Docker container
move to your directory
tensorflow-master is downloaded on github
and write command (must be typing '.' )
docker build -t imageName .
about this issue refer to
*docker useful command
//show running containers
docker rmi imageID
docker rm containerID