It seems that NNabla has been released by SONY! Let's skip the detailed explanation and use it immediately!
First, try installing directly in the virtual environment using pip. The version of cuDNN is different from 6.0 and the version 5.2 supported by __TensorFlow, so it is the one who does not use GPU.
$ conda create --name py27_nnabla python=2.7
For the time being, it seems that you can create an environment with conda and install it with pip, so I will try it. It seems that python3 series is not supported, so python2.7.
$ pip install nnabla
It didn't take long. Let's try the import code, which is the first step.
$ python -c "import nnabla"
2017-06-28 22:14:29,602 [nnabla][INFO]: Initializing CPU extension...
According to NNabla's site, it seems that it is installed, so let's run Example.
From nnabla repository, git clone and zip download.
Run the example code in the nnabla / examples / vision / mnist directory.
python classification.py
Of course, it will take some time.
……
If you take Mnist seriously, it shouldn't end in an hour, but what about that?
……
I confirmed that it works, so I interrupted the keyboard.
The main subject of this time is Docker.
How to run Docker ...
Run A Docker Image hosted on DockerHub
TODO.
Build Docker Image from source
TODO.
Gununu
Fortunately, the Dockerfile is open to the public, so let's do something about it here.
Go to nnabla / docker
$ sudo bash ./docker-build develop-ubuntu16.04
This time I tried it with develop-ubuntu 16.04.
It will take some time, but I will wait this time. You should estimate at least a few tens of minutes.
After building the image, try running it.
$ docker run -it nbla:develop-ubuntu16.04
I will log in as root, so let's do something. For example, execute import
$ python -c "import nnabla"
Traceback (most recent call last):
File "<string>", line 1, in <module>
ImportError: No module named nnabla
Isn't it useless!
When I read the Dockerfile, I found that it seems that NNabla is not installed even if I execute it as described in the __README. Then I'll fall over, so I'll do something on my own.
Continue to the second part