How to use TensorFlow with Container Station?

How to use TensorFlow with Container Station?

1. About Chainer

TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in
the graph represent mathematical operations, while the graph edges represent the multidimensional data
arrays (tensors) communicated between them.


2. Installing TensorFlow in Container Station

1. Assign GPUs to Container Station.

a. Go to Control Panel > System > Hardware > Graphics Card.
b. Under Resource Use, assign the GPUs to Container Station.
c. Click Apply.

2. Open Container Station.
3. Use the correct image version.

a. Click Images.
b. Click Pull to the desired image is installed.


4. Click Create.
5. Search for the keyword “TensorFlow”. Find tensorflow/tensorflow and click Install.

6. Select a version of Chainer based on what version of QTS and Nvidia Driver you have installed.

7. Click Next.
8. Click Advanced Settings.

9. Assign GPUs to the container.

a. Go to Device.
b. Click Add.

c. Choose the GPUs to add to the container.

10. Optional: Share a NAS folder with the container.

a. Go to Shared Folder.
b. Over Volume from Host, click Add.  A new volume from host is added.
c. Select a Host Path.
d. Specify a Mount Point.

11. Click Create.  A Summary of your new container will be displayed.
12. Review the container’s settings.
13. Click OK.  The container image is installed.

3. Mounting an NVIDIA GPU via SSH

1. Connect to your NAS via SSH.
2. Mount GPUs to the container.

a. Enter one of the following commands based on the GPU you want to mount.

GPU to mount Command

Example commands based on your QTS and Nvidia Driver versions are listed below:

QTS and Nvidia Driver versions Command
QTS 4.3.5/4.3.6 and Nvidia Driver v1.3.5
QTS 4.4.x and Nvidia Driver v2.0.0


4. Accessing the Container

1. Open Container Station.
2. Click Overview.
3. Find the container you just installed and open the container’s page.
4. Copy the Token from the Console.
5. Click the URL

6. Paste the Token into Password or Token.
7. Click Log in.

You can now use Jupyter notebook with TensorFlow.

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